Abstract: Over the years, technology has continued to improve and evolve, along with the virtual representation of real-world environments. These reconstructions serve various fields of research and beyond. The focus of this work is to transfer a real-world road segment into a driving simulation software, aiming to achieve a high level of graphical fidelity. The study is based on the reconstruction of a section of roadway located within the University of Salerno’s Fisciano campus. This was made possible through a methodology involving laser scanner surveying of the circuit, subsequent import into modeling software, refinement of any imperfections, and final validation of the resulting model. The last phase took place at the Virtual Reality Laboratory of the Department of Industrial Engineering, where, according to the main goal of the workflow, test drives were carried out to assess the accuracy of the modeled track compared to the real one. After several iterations aimed at ensuring correct compatibility with the driving simulation software and fixing missing points resulting from the scans, the validity of the proposed model was confirmed.

Keywords: 3D Modeling | Driving Simulation | Laser Scanning | Virtual Reality

Abstract: Nowadays, driving simulation devices represent a continuously evolving and developing area in the world of virtual reality. One of the fundamental elements in the design of driving software is the track model. This work aims to study the use of advanced technologies for the three-dimensional modeling of a racing track in a driving simulator. Specifically, it employs the LiDAR methodology to acquire the three-dimensional coordinates of a 1 km long circuit located on the Fisciano campus of the University of Salerno. The purpose of this work is to explain and present a novel track acquisition and modeling methodology within the realm of simulated driving reality. Following the study, the Virtual Reality Laboratory’s driving simulator at the Department of Industrial Engineering conducted tests to validate the proposed circuit model. The test rides analyzed the realism of the driving experience, thereby validating the proposed track model. This phase was complemented by a series of proposals for possible future developments in the field of three-dimensional modeling applied to driving simulation and beyond. In the end, the 3D model obtained demonstrated the high definition of the acquired result and the speed with which multiple data were obtained simultaneously, thanks to the laser scanner used.

Keywords: 3D modeling | driving simulation | LiDAR data | real-world driving | track modeling | virtual environment | virtual reality | virtual reality

Abstract: The Building Information Modeling (BIM) methodology and Digital Twin technologies have emerged as potential solutions to address significant challenges across various sectors of the built environment, particularly in facility management (FM). While commercial solutions are already available, and numerous researchers have proposed their approaches, there is a lack of consensus in terms of usage, content, and technical implementation. These solutions typically rely on specific vendors and software, often offering a basic 3D interface for visualizing spaces and conditions. However, their full potential has yet to be realized. This contribution seeks to offer a condensed framework and illustrate its application in facility management.

Keywords: Digital Twin | Facility Management | Internet of Things

Abstract: Lithium-ion battery capacity degrades over time due charging and discharging. As a result, the capacity exhibits large jumps, that have long-range dependence and stochastic components. In this study we propose a method for estimation of remaining useful life of lithium-ion batteries by using fractional Poisson process with adaptive jump intensity modification. We derived a Molchan–Golosov-type integral transform which changes fractional Brownian motion of arbitrary Hurst index into the fractional Brownian motion of index H. Numerical simulations are performed for the fractional Poisson process with the Molchan-Golosov kernel. Poisson random walk is initiated by the integral term of the Riemann-Liouville fractional formula. A semimartingale approximation technique is devised to demonstrate the existence and uniqueness of the solution. In order to obtain the iterative model, the stochastic differential equation is discretized by using the Maruyama function. To solve the iterative prediction model, the distribution convergence and Fokker-Planck equation are utilized. As a result, we obtained the probability density function of the reliability function. Estimation of parameters in the solution is achieved by using the martingale function.

Keywords: Fractional Poisson process | Long-range dependence | Martingale estimation function | Reliability function | Remaining useful life | Semimartingale

Abstract: The Internet of Things (IoT) paradigm has transformed the establishment of interconnected technology settings, paving the way for novel approaches to intelligent service management. Smart Cities and Smart Homes are just a few examples of how this technology might improve people’s lives. This study proposes a novel way to Smart Home management that combines the Internet of Things with graphic representation and machine learning techniques. Our technology enables you to collect and analyze data from sensors and other devices, resulting in relevant information and autonomous actions that optimize energy and ventilation control.

Keywords: Energy Optimization | Environmental Monitoring | Internet of things | Smart Home | User-Centered Automation

Abstract: Ensuring worker safety is a basic concern in high-risk areas like construction sites, where accurate and efficient human identification is essential. Accurate identification of personnel at worksites is crucial for reducing security hazards. Contemporary approaches exhibit low identification rates, localization inaccuracies, and elevated latency, hence jeopardizing safety. This paper presents SECURITY YARDS, a framework utilizing cloud computing and the Internet of Things (IoT) for human identification through gait recognition. Our approach utilizes Speeded Up Robust Features (SURF) and Convolutional Neural Networks (CNNs) to extract and classify gait characteristics, providing a more nuanced and adaptable representation. A Support Vector Machine (SVM) at the Fog level optimizes classification, improving precision and processing efficacy. Final decision-making, data storage, and monitoring are conducted on the cloud, ensuring scalability and real-time safety management. The findings indicate that SECURITY YARDS markedly enhances accuracy and lowers latency relative to traditional biometric identification systems.

Keywords: Cloud computing | Deep Learning | Internet of Things | Machine Learning

Abstract: The introduction of automated vehicles has redefined the level of interaction between the driver and the vehicle, introducing new tasks and so impose different workloads. Existing tools such as NASA-TLX and DALI are still used to assess driving workload in automated vehicles, despite not accounting for new tasks. This study introduces AV-TLX, a specialized tool for measuring workload in Level 3 automated driving. The development process began with a narrative literature review to identify the primary factors influencing workload. This was followed by a series of qualitative sessions during which the dimensions—and later the questions—of the questionnaire were designed. The tool’s validity was first assessed using CVR and CVI indices, and its reliability and convergent validity were evaluated using a dynamic driving simulator with medium fidelity. The final version of AV-TLX comprises 19 questions across 8 subscales, demonstrating excellent reliability (0.86) and validity (CVR > 0.78). An agreement scores between the results of AV-TLX and NASA-TLX in the simulation study was 0.6, which is considered acceptable for the consistency of two questionnaires. Furthermore, this questionnaire can be utilized in two ways: by reporting the overall workload and/or divided into 8 primary subscales, or by categorizing the questions into two groups— “takeover task” workload and “automated driving task” workload.

Keywords: Autonomous driving | Autonomous vehicle | DALI | Driver workload | Driving Load | Human Factors in Transportation | NASA-TLX | Workload Questionnaire

Abstract: Background: Both industries and academia are paying deep attention to the concept of trust in automation since it is significant that creates people’s desire to use and buy autonomous cars. Objectives: Regarding the previously introduced trust questionnaire by our research team, this study was conducted to present more objective insights of this questionnaire to show its convergent validity based on the physiological data. Methods: Using a dynamic driving simulator, the 8-step experiment was performed on 29 min with 21 participants. The steps provided building, improvement, destruction, and re-building of trust. The experiment contained both manual and autonomous driving. Subjects completed stress and trust scales regarding the experience they performed. Finally, they answered to the trust questionnaire; their heart rate was acquired throughout the experiment. Results: The Mean ± SD of participants’ trust score was 139 ± 5.76. Self-driving car’s unsafe behaviors were accompanied by a rise in the individuals’ heart rates (objective metrics of stress). Throughout the two phases of fastest and riskiest parts of the scenario, people felt the highest stress (6 out of 10) and the least trust (4.33 out of 10), that were obtained from the mentioned one question scale. Conclusion: Regarding the results, the mean score of people’s trust from our multi-dimensional questionnaire is far from the desired score, which is 235. Objective (heart rate) and other subjective data (stress and trust) confirmed that the participants’ trust in an autonomous vehicle was low.

Keywords: Autonomous vehicles | Heart Rate | Multi-dimensional questionnaire | Stress | Trust

Abstract: The widespread and diversified use of portable devices with high magnification power at relatively low-cost, i.e., the so-called USB digital microscopes, has revealed the possibility of employing their photographic output in Structure from Motion processes for sub-millimetric digitisation. However, USB microscopes, born for two-dimensional inspection – if used without specific accessories, designed ad-hoc for the photogrammetric capture phase – can pose some difficulties for the surveyor in managing capture operations. Thus, this study suggests prototyping a low-cost, complete, and portable mounting compatible with the most common USB microscopes for image scanning. This system makes it possible to use portable USB microscopes for three-dimensional modelling at a low-cost, as it is easy to replicate both in terms of construction and the materials used. Therefore, through the interaction of several skills, the work shows how the photogrammetric process can also be made accessible to devices that were not initially designed for such application, paving the way for new paths in representation engineering research.

Keywords: Additive Manufacturing | Fast Survey | Structure from Motion

Abstract: Remaining useful life prediction guarantees a reliable and safe operation of turbofan engines. Long-range dependence (LRD) and heavy-tailed characteristics of degradation modeling make this method advantageous for the prediction of RUL. In this study, we propose fractional Lévy stable motion for degradation modeling. First, we define fractional Lévy stable motion simulation algorithms. Then, we demonstrate the LRD and heavy-tailed property of fLsm to provide support for the model. The proposed method is validated with the C-MAPSS dataset obtained from the turbofan engine. Principle components analysis (PCA) is conducted to extract sources of variance. Experimental data show that the predictive model based on fLsm with exponential drift exhibits superior accuracy relative to the existing methods.

Keywords: feature fusion | fractional Lévy stable motion | long-range dependence | remaining useful life | self-similar

Abstract: The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation.

Keywords: adaptive fractional generalized Pareto motion | measurement error | meta representation learning | metric-based feature selection algorithm | planetary gearbox remaining useful life prediction

Abstract: In order to improve the design of the actuators of a Dragon Fly prototype, we study the loads applied to the actuators in operation. Both external and inertial forces are taken into account, as well as internal loads, for the purposes of evaluating the influence of the compliance of the arms on that of the "end-effector". We have shown many inadequacies of the arms regarding the stiffness needed to meet the initial design requirements. In order to reduce these inadequacies, a careful structural analysis of the stiffness of the actuators is carried out with a FEM technique, aimed at identifying the design methodology necessary to identify the mechanical elements of the arms to be stiffened. As an example, the design of the actuators is presented, with the aim of proposing an indirect calibration strategy. We have shown that the performances of the Dragon Fly prototype can be improved by developing and including in the control system a suitable module to compensate the incoming errors. By implementing our model in some practical simulations, with a maximum load on the actuators, and internal stresses, we have shown the efficiency of our model by collected experimental data. A FEM analysis is carried out on each actuator to identify the critical elements to be stiffened, and a calibration strategy is used to evaluate and compensate the expected kinematic errors due to gravity and external loads. The obtained results are used to assess the size of the actuators. The sensitivity analysis on the effects of global compliance within the structure enables us to identify and stiffen the critical elements (typically the extremities of the actuators). The worst loading conditions have been evaluated, by considering the internal loads in the critical points of the machine structure results in enabling us the sizing of the actuators. So that the Dragon fly prototype project has been set up, and the first optimal design of the arms has been performed by means of FEM analysis.

Keywords: compliant behavior | flexible-link robot | input shaping method | optimal trajectory planning | parallel manipulator | robot-environment interaction | Vibration suppression method

Abstract: Roller bearing degradation features fractal characteristics such as self-similarity and long-range dependence (LRD). However, the existing remaining useful life (RUL) prediction models are memoryless or short-range dependent. To this end, we propose a RUL prediction model based on fractional Brownian motion (FBM). Bearing faults can happen in different places, and thus their degradation features are difficult to extract accurately. Through variational mode decomposition (VMD), the original degradation feature is decomposed into several components of different frequencies. The monotonicity, robustness and trends of the different components are calculated. The frequency component with the best metric values is selected as the training data. In this way, the performance of the prediction model is hugely improved. The unknown parameters in the degradation model are estimated by the maximum likelihood algorithm. The Monte Carlo method is applied to predict the RUL. A case study of a bearing is presented and the prediction performance is evaluated using multiple indicators.

Keywords: degradation component selection | fractional Brownian motion | long-range dependence | remaining useful life | roller bearings | self-similar | variational mode decomposition

Abstract: For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy.

Keywords: fractional Weibull process | hybrid energy storage system | lithium-ion batteries | non-homogeneous compound Poisson process | remaining useful life prediction | supercapacitor

Abstract: In this paper we define an optimized digital twin knowledge network based both on the carrying capacity and preferential attachment. It is a logistic growth scale-free network which solves a min-max problem. The main advantage of this model is to build an integrated system (humans-digital twins) aiming to the max efficiency with limited costs of management.

Keywords: Digital Twin | Human-machine interaction | Industry 4.0 | knowledge graph | networks | smart manufacturing

Abstract: This paper introduces a comprehensive model for studying the glycolysis pathway, involving the fractional Caputo derivative to delve into its dynamics. By employing the fractional Caputo derivative, the study explores the system’s behavior and reveals insights into its stability and bifurcation points. Numerical simulations are conducted to demonstrate how the fractional-order derivative influences the system’s behavior.

Keywords: Fractional derivative | Glycolysis | Hopf bifurcation | Stability

Abstract: This study delves into the dynamic behavior of a beam, employing a fourth-order transverse equation featuring constants like elastic modulus and moment of inertia, and variables such as linear mass density and an applied load expressed as a function of transverse displacement. Our proposed numerical approach utilizes a B-spline-derived basis, ensures meticulous treatment of boundary conditions, and incorporates a higher-order difference scheme. Numerical examples highlight the approach’s versatility and effectiveness, surpassing conventional benchmarks. This study converges mathematical rigor with engineering applicability, offering a comprehensive exploration of dynamic structural analysis.

Keywords: B-spline | Beam equation | Higher order difference scheme | Numerical approximation | Transverse displacement

Abstract: In this paper we give the numerical simulation of the fourth-order transverse equation beam’s equation, under the assumptions of elastic modulus, constant mass density and an applied load expressed as a function of the transverse displacement. In our approach we have used a B-spline basis, combined with a suitable collocation method and finite difference scheme. By a rigorous treatment of the boundary conditions we have obtained an efficient higher-order method for an accurate numerical solution of the beam’s equation. Some non trivial numerical examples show the high efficiency of our approach, its versatility and effectiveness, surpassing conventional benchmarks.

Keywords: B-spline | Beam equation | Higher order difference scheme | Numerical approximation | Transverse displacement

Abstract: Due to the importance of the historical memory in the analysis of a class of dynamical systems, weintroduce in this paper a time fractional-order derivative into the Goldbeter-Lefever model, which is a generalized form of its corresponding first-derivative model. For this system, we investigate the local stability of the proposed system, and we discuss the Hopf bifurcation. Some numerical simulations are performed to confirm the analytical obtained results.

Keywords: fractional derivative | Goldbeter-Lefever model | Hopf bifurcation | stability

Abstract: The need, due to road safety reasons, to constantly monitor the physiological conditions of a driver (stress, concentration, fatigue) as well as the alterations to the comfort state of the driving position in long-haul journey, has led many car manufacturers to focus attention on the development of innovative technologies and methods that include the monitoring of biosignals that can be acquired by the driver himself. The possibility of detecting and processing such a signal in real time was the object of this research, which allowed to produce a technology that exploited the analysis of Heart Rate Variability (HRV), a method widely used in clinical field for the analysis of the autonomic nervous system (ANS) with respect to daily biological rhythm stress, comfort or vigilance. While the conventional cardiac signal acquisition system uses clinical instruments such as the electrocardiograph (ECG), in this study a technique is developed that allows the detection and processing of the pulsation signal at the level of the femoral artery through a sensor placed on the driver's seat. The main advantage consists in having a control in real time by avoiding the application of sensors on the skin. In fact, three fundamental parameters: the heart rate (HR), the interval between beats (RRI) and the typical HRV indices are determined, by using a suitable signal analysis algorithm. The comparison with equivalent values obtained by a conventional ECG device shows a Pearson correlation between 0.35 to and 0.94. This may be of a great help in evaluating thesympathovagal balance.

Keywords: HCD | HRV | RRI

Abstract: The reusability of informative content throughout the building life cycle is a current issue in the AEC sector. One of the cornerstones of BIM is to guarantee the availability and portability of data which, against a greater initial investment for the construction of the model, will offer a multidisciplinary and integrated tool to support all possible operations on the building. The issue becomes even more complicated in the case of cultural heritage or existing structures where the information process starts directly from the operation stage (management and maintenance phases) and provides, through reverse engineering methodology, an Asset Information Model. It is therefore essential to keep track of the levels of accuracy of this content, in relation to the geometric and informative attributes of all the objects that make up the model. Starting from a careful analysis of the state of the art related to these issues, this paper proposes a possible approach to the statistical treatment of uncertainties related to geometric attributes in case of Historic or Existing BIM, differentiating between the products of the survey and those of the subsequent parametric modelling.

Keywords: Detected accuracy | Modelled accuracy | Scan-to-BIM

Abstract: Tool wear will reduce workpieces’ quality and accuracy. In this paper, the vibration signals of the milling process were analyzed, and it was found that historical fluctuations still have an impact on the existing state. First of all, the linear fractional alpha-stable motion (LFSM) was investigated, along with a differential iterative model with it as the noise term is constructed according to the fractional-order Ito formula; the general solution of this model is derived by semimartingale approximation. After that, for the chaotic features of the vibration signal, the time-frequency domain characteristics were extracted using principal component analysis (PCA), and the relationship between the variation of the generalized Hurst exponent and tool wear was established using multifractal detrended fluctuation analysis (MDFA). Then, the maximum prediction length was obtained by the maximum Lyapunov exponent (MLE), which allows for analysis of the vibration signal. Finally, tool condition diagnosis was carried out by the evolving connectionist system (ECoS). The results show that the LFSM iterative model with semimartingale approximation combined with PCA and MDFA are effective for the prediction of vibration trends and tool condition. Further, the monitoring of tool condition using ECoS is also effective.

Keywords: linear fractional alpha-stable motion (LFSM) | long-range dependence (LRD) | maximum Lyapunov exponent (MLE) | semimartingale

Abstract: Although reality-based models are widely used to describe the geometric surfaces of an entity in a digital space, a systematic and universally recognised treatment of issues such as accuracy is lacking. The topic is certainly complex as this analysis should involve not only shape approximation but also other attributes (e.g., colour). Wanting to limit ourselves to geometry alone, this work proposes solutions for assessing the quality of photogrammetric models, differentiating them according to possible scenarios: sometimes, homologous models obtained using different techniques and technologies are available. In these cases, a comparison between digital reconstructions can serve to effectively quantify accuracy; more often, no terms of comparison are available, and one is forced to derive indicators from the same photogrammetric process to describe quality. We propose for this scenario a statistical analysis on the covariance matrix of the estimated coordinates for the tie points. The main goal is to provide a range of possible approaches to the conscious management of survey data.

Keywords: Accuracy assessment | Coordinate covariance matrix | Hausdorff distance | Tolerance intervals

Abstract: In this paper we define a scale-free network based both on the preferential attachement parameter, of the Barabasi-Albert model, and on the new parameter of carrying capacity under a logistic growth. The main advantage is that by using this new parameter the network will grow as a set of communites each one with a limited number of nodes, each community with only one hub and a very little number of connections between communities, thus minimizing the number of links. With this model, which fulfills the 80–20 Pareto rule, we will also get an optimal designed network characterized by the limited cost of management.

Keywords: Barabasi and Albert model | preferential attachement | scale-free network | Supply chain management

Abstract: Edge detection is important in extracting image features, and microscopic slice images consist of closed-loop structures and complex internal textures, and extracting the corresponding features has an important role in biology, epidemiology, pathology and other fields. In this study, an edge detection algorithm for slice images based on empirical wavelet transform (EWT) and morphology is proposed. The empirical wavelet divides the Fourier spectrum of the signal into successive intervals, and then constructs a wavelet filter bank for filtering in the corresponding interval segments, and finally obtains the amplitude modulation frequency components by signal reconstruction. The empirical wavelet transform overcomes the modal aliasing problem caused by the scale discontinuity in the time domain, which reflects the characteristics of the empirical wavelet transform. The image components extracted by the empirical wavelet are then enhanced using a morphological algorithm, which can effectively extract the closed-loop edges of the sliced image as well as the significant textures inside. In this paper, the proposed method is tested on locust slice images as an example. The proposed algorithm can also be effectively applied to other biological cross-sectional images.

Keywords: biological slice images | edge detection | empirical wavelet transform | morphology

Abstract: Artificial Neural Network (ANN) has been used extensively and constantly developed. The combination of wavelet transform theory and the neural network has become an important branch to explore the optimization of neural network structure, and Wavelet Neural Network (WNN), a special network structure, was born. This paper reviews WNN’s development and summarizes the system structure and algorithm implementation and presents derivative models and cutting-edge applications with obvious characteristics. The sorting and analysis of the above contents show that the combination of wavelet theory and neural network algorithm can make the network model have the advantages of fast convergence speed and high model accuracy, and has a rapid development trend in many fields such as audio signal and image processing. The work of this paper is intended to provide a reference for potential applications based on WNN and new network model design ideas.

Keywords: Wavelet Neural Network | Wavelet Transform

Abstract: Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the bendlets system is a powerful tool with which to represent images with curve contours, such as the brain MRI images, sparsely. By means of the characteristic of bendlets, an adaptive denoising method for microsection images with Rician noise is proposed. In this method, the curve contour and texture can be identified as low-frequency components, which is not the case with other methods, such as the wavelet, shearlet, and so on. It is well known that the Rician noise belongs to a high-frequency channel, so it can be easily removed without blurring the clarity of the contour. Compared with other algorithms, such as the shearlet transform, block matching 3D, bilateral filtering, and Wiener filtering, the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) obtained by the proposed method are better than those of other methods.

Keywords: adaptive algorithm | bendlet transform | magnetic resonance imaging | Rician noises

Abstract: In various tasks of machine vision, image resolution is one of the important factors that affect the performance of the model. Generally, crop images with low resolution and lack of detail informa-tion may be collected. The picture is not good for the accuracy of yield prediction and crop pest identification. In this paper, tomato leaves are used as the target image, and the super-resolution reconstruction process takes advantage of the sharpness of the image, which has the characteristic of Scale invariance. Firstly, each image block is classified by using clustering algorithm accord-ing to the sharpness value of the image, and then wavelet transform is used to extract image features from each class of image blocks to get wavelet subbands respectively, subbands of each class not only train a union dictionary, but also learn a separate mapping function. Joint dictionary training and separate mapping matrix learning are helpful to optimize the high resolution and low resolution sparse coefficients. In the Reconstruction Stage: in order to reduce the image reconstruction time, the wavelet transform is only applied to the image blocks with a certain sharpness value, while the image reconstruction performance is basically unchanged, then the high-resolution image blocks are reconstructed by using the mapping function, coupled dictionary and the sparse representation coefficients of the image blocks. When the sharpness of the image block is lower than a certain sharpness value, the high and middle resolution image blocks will be superimposed to finally get the high resolution image.In the various tasks of machine vision, image resolution is one of the important factors that affect average PSNR value of the algorithm in this paper is 3.94 dB, 3.54 dB, 3.36 dB, 3.23 dB, 3.01 dB and 1.51 dB higher respectively.

Keywords: Clustering | Coupled dictionary | Sharpness value | Sparse representation

Abstract: Some critical limit conditions for the stability of the self-propelled hydrodynamic irrigation machine used for food industry crops, have been studied, and experimental and numerical tests have been carried out for their determination. The strength forces necessary for the machine overturn have been calculated by a computer code realized in Matlab R2019a, and the corresponding values are listed as function of the soil slope angle ψ of the weight W and the pipeline strength force., With this aim, different operative conditions for the considered machine have been examined so that the pipeline strength force, under the following conditions: water filled pipeline of and empty pipeline;dry and wet soil. By analyzing the data measured in the open field, on a considered machine with a coil diameter of 3 m, the different contributes to the total rewinding strength have been examined during the considered tests. Further, it has been possible to deduce that by changing; only the value of the water pressure, the total value of the rewinding strength force increased by 100 daN, which is clearly due; to the changing pressure which increases the stiffness of the polyethylene pipeline. Moreover, other very dangerous limit conditions were determined during the rewinding phase of the pipeline on overflooded soil (also due to a rain storm), with a pipeline completely unwound on the soil and sunk into it. In these critical conditions, it has been noted that, to perform the operating phase, it is possible to reach a very high T value, which can cause the machine overturning even for ψ = 0 (horizontal case).

Keywords: Irrigating machine | Machine overturn | Machine stability

Abstract: Modelling a geometric surface or contour surface is a modern field involving a wide range of topics such as mathematics, computer science, engineering design. The main task in modelling a geometric surface is to approximation the shape of the contour surface of a 3D solid object the most efficient way. In this paper we will give a method to optimize the geometric model, by defining a local sampling of the mesh, based on k-means method and minimization of the Hausdorff measure with an homologous model.

Keywords: Clustering | Geometric modelling | Hausdorff distance | k-means

Abstract: Locust slice image is a kind of cartoon-like images, in which the texture possesses the property of self-similarity. Both of the texture and the noises belong to the high-frequency signals, and so it is difficult to tell the difference between them for most denoising methods. Aim to the problem, we propose a novel denoising method by combining the patch reordering with the shearlet transform. In the reordering process, the patches are divided into smooth and texture components. The filters obtained from the training set are employed to process the patches in smooth regions and the shearlet transform are employed to process the texture regions. The experiments show that the values of PSNR and SSIM of the processed images obtained by the proposed method are better than the common methods.

Keywords: Image denoising | Patch reordering | Shearlet transform | Texture preserving

Abstract: Shearlet is a multi-dimensional function used for sparse representation, which has many excellent characteristics such as multi-resolution and multi-direction. It can detect the position of singular points and the direction of singular curves, and is more sensitive to the geometric structure of the image. Therefore, this paper introduces the shearlet transform and its application in image processing, and introduces the bendlet transform proposed on this basis.

Keywords: Bendlet | Image processing | Shearlet | Transform

Abstract: The development and spread of human-centered products that are increasingly simple and affordable has seen their application areas increase over the years, as well as their effectiveness and reliability. The study of a model based on the interpretation of biosignals is able to provide real-time monitoring of physiological conditions and to offer useful support in both clinical and driving safety fields. Heart rate variability (HRV) analysis is useful in assessing the dynamics of the autonomic nervous system (ANS) and detecting the effects of numerous systemic diseases as well as changes related to the normal daily biological rhythm or to accidental situations of stress or fatigue, as well as comfort or vigilance. The conventional system for the acquisition of the electrocardiographic (ECG) signal uses a methodology that provides for the contact of electrodes with the human skin. This research proposes a detection platform without direct contact with the skin capable of acquiring cardiac signals in real time: through a digital signal processing algorithm able of filtering noise and identifying peaks, heart rate (HR), the interval between beats (RRI) and the characteristic indices of HRV in the time and frequency domain are determined. The parameters were compared with those of a conventional ECG using the Pearson correlation coefficient which produced an index ranging from 0.30 to 0.78 for the tachogram, managing to provide, in the cases less affected by noise, a correspondence in the results of the spectral analysis useful for the evaluation of sympatho-vagal balance.

Keywords: HRV | Human-centered-design | Non-invasive | RRI

Abstract: The field of simplification of geometric surfaces still lacks a formal and universally recognized definition of the error, which should involve both the approximation of the shape and the conservation of the other attributes of the mesh (starting from the colour). In order to solve this problem, we propose a hypothesis of methodological comparison that allow the evaluation of differences between two homologous surfaces, quantified employing the Hausdorff distance. The main advantage of this method is the independence from sampling techniques used to produce the mesh, without losing its characteristics of objectivity and generality. The Hausdorff distance geometrically represents the distance between two sets A and B in a suitable metric space, and it is defined as the maximum between the excess of A over B and the excess of B over A. This value is then compared with the average length of the diagonals of the “bounding boxes” of the homologous models, i.e. the parallelepipeds corresponding to the minimum volume that completely envelops each set; this results in an effective representation of error in relative terms.

Keywords: Accuracy assessment | Cultural Heritage | Hausdorff distance | Mesh

Abstract: Nonlinear partial differential equations emerge in an extensive variants of physical problems inclusive of fluid dynamics, solid mechanics, plasma physics, quantum field theory as well as mathematics and engineering. It has also been noticed that systems of nonlinear partial differential equations arise in biological and chemical applications. This article presents the analytical investigation of a completely generalized (3 + 1)-dimensional nonlinear potential Yu-Toda-Sasa-Fukuyama equation which has applications in the fields of engineering and physics. The generalized version of the potential Yu-Toda-Sasa-Fukuyama equation is more comprehensively studied in this paper compared to other research work previously done on the equation, with various new solutions of interests achieved. The theory of Lie group is applied to the nonlinear partial differential equation to basically reduce the equation to an integrable form which consequently allows for direct integration of the result. The rigorous process involved in performing a comprehensive reduction of the model under consideration using its Lie algebra makes it possible to achieve various nontrivial solutions. Besides, more general solutions are found via a well-known standard technique. In consequence, we secured diverse solitons and solutions of great interest including topological kink solitons, singular solitons, algebraic functions, exponential function, rational function, Weierstrass function, Jacobi elliptic function as well as series solutions of the underlying equation. Moreover, the completeness of the result was ascertained by presenting the solutions graphically. In addition, discussions of the pictorial representations of the results are done. Conclusively, we constructed conserved quantities of the underlying equation via both the variational and non-variational approaches using the classical Noether's theorem as well as the standard multiplier technique respectively. In addition, some pertinent observations made from the secured results via both techniques are analyzed.

Keywords: generalized (3 + 1)-dimensional nonlinear potential Yu-Toda-Sasa-Fukuyama equation | Conserved quantities | Exact analytic solutions | Integrability | Theory of Lie group | Variational and non-variational principles

Abstract: Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.

Keywords: Bendlet system | feature set | image expression | segmentation | Shannon-cosine wavelet

Abstract: In this study, we examined a tank container for foodstuff that is generally used for the transport of foodstuffs. With the aid of the “ANSYS R17.0” program code, a numerical model of the tank container for foodstuffs was realized. Further, to validate the considered model, the tank container considered was submitted to the most important ISO tests concerning both its support frame and the tank. The results obtained from the FEM analysis, in terms of displacement for each test, were compared with those provided by the manufacturer and related to the tank container considered, evaluating the difference between the numerical results with the experimental ones. This allowed us to validate the model examined. Furthermore, the results obtained from each test, in terms of stress, have made it possible to locate the areas with the highest equivalent stress and quantify the maximum value, comparing it with the allowable stress. In this way, a better understanding of the structure was achieved, and it was detected that the most stressed area is that of the connections between the container and the frame. Furthermore, modal analysis was carried out, in which the natural frequencies relating to the most dangerous modes of vibrations were found, that is, with the lowest frequency values. Finally, changes for the considered tank container were examined, and it was found that, by changing parameters, such as the thickness of the plate and skirt, and subsequently acting on the arrangement of the corner supports, the highest value of the stresses generated by the loads related to the ISO tests, it is significantly lowered, resulting in a better distributed stiffening of the structure and a reduction, although minimal, of weight. It is evident that this modeling and validation method, suitably integrated by further calculation modules, can be used in an iterative optimization process.

Keywords: ISO tests | Numerical simulation | Tank containers for foodstuff

Abstract: Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PER-CLOS showed a 63% adherence to the experimental findings. Conclusions: the present study con-firms the possibility of continuously monitoring the driver’s status through the detection of the ac-tivation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.

Keywords: Driver conditions | Fatigue | Heart rate variability | On-road experiment | Sleepiness

Abstract: In this paper a machine that performed the soil sterilization has been designed. The soil is cut and put in a loading hopper and downloaded in a rotating cylinder placed on the machine. The fins located inside the rotating cylinder performed the crushing and the mixing of the soil. Each soil particle through the temperature field ranged between 290–1900° C for 3–5 min and a preset output soil temperature of 130–140° C is reached and discharged downward. It maintained the process temperature long enough, to allow the elimination of the infesting organisms located in the considered soil.

Keywords: Soil sterilizing | Soil sterilizing machineries | Soil thermal exchange

Abstract: A numerical model of cryo-maceration plant for mashed grapes has been realized to simulate the rapid cooling process for mashed grapes according to the number of nozzles enabled to inject CO2, to their flow and temperature of CO2 injected. ANSYS CFX program was used and two geometries have been considered with the axis of each nozzle orthogonal and parallel to the flow direction. Different boundary conditions have been considered. For the models have been considered the most burdensome condition.

Keywords: Cryo-maceration plant | Liquid CO 2 | Numerical simulation

Abstract: The contribution of this article is mainly to develop a new stochastic sequence forecasting model, which is also called the difference iterative forecasting model based on the Generalized Cauchy (GC) process. The GC process is a Long-Range Dependent (LRD) process described by two independent parameters: Hurst parameter H and fractal dimension D. Compared with the fractional Brownian motion (fBm) with a linear relationship between H and D, the GC process can more flexibly describe various LRD processes. Before building the forecasting model, this article demonstrates the GC process using H and D to describe the LRD and fractal properties of stochastic sequences, respectively. The GC process is taken as the diffusion term to establish a differential iterative forecasting model, where the incremental distribution of the GC process is obtained by statistics. The parameters of the forecasting model are estimated by the box dimension, the rescaled range, and the maximum likelihood methods. Finally, a real wind speed data set is used to verify the performance of the GC difference iterative forecasting model.

Keywords: Difference iterative forecasting model | Fractal dimension | Generalized cauchy process | Hurst parameter | Long-range dependent | Wind speed forecasting

Abstract: Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and moreover, the results obtained in a complex environment are easily affected by noise. These drawbacks reduce the application of MOMEDA in engineering practice greatly. In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation. In the first part, a Teager energy spectrum amplitude factor (T-SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing. In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. A cyclic recognition method is proposed to appraise the extracted feature frequency, and the evaluation system based on threshold and weight coefficient removes the wrong feature frequency. Finally, the feasibility of the method is verified by simulation data, experimental signals, and on-site signals. The results show that the proposed method can effectively identify the bearing state.

Abstract: This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, material, and lubrication, in order to validate ideas from the literature and to make several comparisons. Furthermore, a coupled Eulerian–Lagrangian (CEL) analysis was performed, which was an innovative way of evaluating the sound pressure level of the aforementioned gears. Different parameters were considered again, such as the friction, lubrication, material, and rotational speed, in order to make different research comparisons. The analytical results agreed with those in the literature, both for the static analysis and CEL analysis—for example, it was shown that changing the material from steel to ductile iron improved the gear noise, while increasing the rotational speed or the friction increased the acoustic emissions. Regarding the CEL analysis, air was considered a perfect gas, but its viscosity or another state equation could have also been taken into account. Therefore, the above allowed us to state that research into these scientific fields will bring about reliable results.

Keywords: Coupled Eulerian–Lagrangian analysis | Entropy | Gearboxes | Noise reduction

Abstract: In recent years, driving simulators have been widely used by automotive manufacturers and researchers in human-in-the-loop experiments, because they can reduce time and prototyping costs, and provide unlimited parametrization, more safety, and higher repeatability. Simulators play an important role in studies about driver behavior in operating conditions or with unstable vehicles. The aim of the research is to study the effects that the force feedback (f.f.b.), provided to steering wheel by a lane-keeping-assist (LKA) system, has on a driver's response in simulators. The steering's force feedback system is tested by reproducing the conditions of criticality of the LKA system in order to minimize the distance required to recover the driving stability as a function of set f.f.b. intensity and speed. The results, obtained in three specific criticality conditions, show that the behaviour of the LKA system, reproduced in the simulator, is not immediately understood by the driver and, sometimes, it is in opposition with the interventions performed by the driver to ensure driving safety. The results also compare the performance of the subjects, either overall and classified into subgroups, with reference to the perception of the LKA system, evaluated by means of a questionnaire. The proposed experimental methodology is to be regarded as a contribution for the integration of acceptance tests in the evaluation of automation systems.

Keywords: Driving simulator | Lane keeping assist | Steering wheel torque

Abstract: In this paper is given a three-dimensional numerical simulation of the eddy current welding of rails where the longitudinal two directions are not ignored. In fact, usually it is considered a model where, in the two-dimensional numerical simulation of rail heat treatment, the longitudinal directions are ignored for the magnetic induction strength and temperature, and only the axial calculation is performed. Therefore, we propose the electromagnetic-thermal coupled three-dimensional model of eddy current welding. The induced eddy current heat is obtained by adding the z-axis spatial angle to the two-dimensional electromagnetic-thermal, thus obtaining some new results by coupling the numerical simulation and computations of the electric field and magnetic induction intensity of the three-dimensional model. Moreover, we have considered the objective function into a weak formulation. The three-dimensional model is then meshed by the finite element method. The electromagnetic-thermal coupling has been numerically computed, and the parametric dependence to the eddy current heating process has been fully studied. Through the numerical simulation with different current densities, frequencies, and distances, the most suitable heat treatment process of U75V rail is obtained.

Keywords: Eddy heating | Electromagnetic induction | Grid planning | Rail welding | Three-dimensional model

Abstract: It is well known that wastewaters from the food industry are a potential source of valuable compounds, such that these wastewaters may be considered by-products awaiting the development of appropriate treatment methodologies. Whey is one of the most important polluting effluents from the milk industry because of its very high biological oxygen demand (BOD). Nevertheless, whey is rich in by-products such as whey proteins and lactose, both of which are widely used industrially. Integrated processes could permit the selective recovery of these by-products in conjunction with the generation of clean, reusable water, thus reducing the environmental impact of this wastewater. Ultrafiltration is a crucial step in the separation and recovery of whey protein and lactose, but this technology suffers from fouling problems that can reduce its performance. New technologies assisted by smart modeling and control systems could improve this ultrafiltration step. In this work, a novel system that allows the recovery of the whey protein in milk industry effluent and the disposal of wastewater was developed and tested. This system utilizes an ultrafiltration process, as this permits the purification of the effluent at room temperature, ensuring the preservation of the recovered proteins. A specially designed autoadaptive fuzzy logic controller evaluates the level of filter membrane fouling and controls a mechanical device that induces filter regeneration. Experimental testing showed that this approach permits process intensification.

Keywords: Dairy farm | Fuzzy logic | Ultrafiltration | Wastewater treatment

Abstract: The whey is a by-product rich in organic substances; the system, as conceived, is self-learning.

Keywords: Dairy farm | Fuzzy logic | Ultrafiltration | Wastewater treatment

Abstract: Mechanical parameters of the olive wood plate have been computed by data inversion of vibrational experimental tests. A numerical-experimental method has allowed the evaluation of the two transverse shear moduli and the four in-plane moduli of a thick orthotropic olive tree plate. Therefore, the natural flexural vibration frequencies of olive trees plates have been evaluated by the impulse technique. For our purposes, we define the objective function as the difference between the numerical computation data and the experimental ones. The Levenberg-Marquardt algorithm was chosen as optimization strategy in order to minimize the matching error: the evaluation of the objective function has required a complete finite element simulation by using the ANSYS code. As input, we have used the uniaxial test data results obtained from the olive plates. The converged elastic moduli with n = 10 natural modes were E1 = 14.8 GPa, E2 = 1.04 GPa, G12 = 4.45 GPa, G23 = 4.02 GPa, G13 = 4.75 GPa, ν12 = 0.42, and ν13 = 0.42. The relative root mean square (RMS) errors between the experimental frequencies and the computed one is 9.40%. Then, it has been possible to obtain a good agreement between the measured and calculated frequencies. Therefore, it has been found that for plates of moderate thickness the reliability of the estimated values of the transverse shear moduli is good.

Keywords: Flexural vibration frequencies | Levenberg-Marquardt method | Olive tree | Orthotropic material

Abstract: In this study, fluid-structure interaction (FSI) modeling was applied for predicting the fluid flow in a specific peristaltic pump, composed of one metallic roller and a hyperelastic tube pumping a viscous Newtonian fluid. Hyperelastic material dynamics and turbulence flow dynamics were coupled in order to describe all the physics of the pump. The commercial finite element software ABAQUS 6.14 was used to investigate the performance of the pump with a 3D transient model. By using this model, it was possible to predict the von Mises stresses in the tube and flow fluctuations. The peristaltic pump generated high pressure and flow pulses due to the interaction between the roller and the tube. The squeezing and relaxing of the tube during the operative phase allowed the liquid to have a pulsatile behavior. Numerical simulation data results were compared with one cycle pressure measurement obtained from pump test loop data, and the maximum difference between real and simulated data was less than 5%. The applicability of FSI modeling for geometric optimization of pump housing was also discussed in order to prevent roller and hose parts pressure peaks. The model allowed to investigate the effect of pump design variations such as tube occlusion, tube diameter, and roller speed on the flow rate, flow fluctuations, and stress state in the tube.

Keywords: Hyperplastic models | Peristaltic pump | Pulsatile flow | Turbulence models

Abstract: The purpose of this research was to determine the optimal geometry of a variable pitch conical helicoid to be used in a pressing machine for grape pomace, also known as grape marc. This study attempted to understand if the optimized geometry of the considered helicoid after every pitch resulted in volume decrease DVc, equal to that obtained during the pressing phase of grape pomace DVp, using an optimized membrane press. The conical helicoid with variable pitch was replaced in a machine that offered continuous pressing of grape pomace using a cylindrical helicoid with constant pitch (constant pressure distribution, not optimized, along the cochlea axis). As this was a machine already available in the market, the overall dimensions were already established-5.95 m in length and 1.5 m in width. The pressure distribution p1 and volume change DVp, obtained during the grape pomace pressing phase in the optimized membrane press (producing high-quality wine) was taken into consideration in this research. Furthermore, the optimized pressure distribution p1 was applied in seven phases during the pressing process, and a consequent volume change value DVp was obtained for each phase. Therefore, this study determined the geometry of the variable pitch conical helicoid, which, after every pitch, resulted in volume changing DVc that was similar to the volume changing DVp obtained by the optimized membrane press. For this scope, calculations were realized using the Mathematica 10 program code, which, on being assigned the overall dimensions, slope angle of the helicoid, and volume for the first pitch value, determined the radius and pitch values of the helicoid, total volume, and volume change DVc. It was also noted that by appropriately varying the geometric parameters (taper and pitch of the helicoid), different options of pressure distribution on grape pomace can be obtained, thus enabling improvement and optimization of product quality.

Keywords: Continuous press | Grape pomace pressing | Winemaking process

Abstract: This work deals with a novel procedure that can be used for reverse engineering (RE) of big and old boats’ hull through cheap and effective instruments. The procedure has been used to acquire dimensions and shapes of an offshore boat designed by Renato Levi in 1962, named “Ultima Dea”, commissioned by Gianni Agnelli. The research purpose is the development of a method that gives to designers and restorer an “easy to use” instrument for obtaining the 2D and 3D CAD models from a degraded physical object in order to check and re-design the parts to be restored. The study and the application allowed to develop an innovative procedure to set the right acquisition parameters for optimizing the RE output in terms of minimization of maximum error and mean geometric errors between physical object and virtual model, by using a one-shot RE operation and a completely off-line post-processing. This procedure ensures good timesaving, during acquisition, very high reliability level and lightness of CAD models, also being able to reconstruct worn down and spoiled parts (through ex-novo modelling). The procedure shows how the CAD-modelling step can be done directly on graphical models (without surfaces’ mathematics) while ensuring the appropriate level of detail and, contemporarily, improving the interoperability of used and developed software. This procedure is based on the use of well-known methodologies and instruments that usually are employed in architectural relief; finally, it allowed to model the boat’s hull for the redesigning of engine/electrical/services systems and to restore the boat completely.

Keywords: Polygon mesh modelling | Reverse engineering | Yacht design

Abstract: In this article, the information value is used in numeric analysis as both a method for data approximation and a measure of data equality among a set of values. To this end, a surface segmentation, based on a study for constructing a hierarchy for vectors clustering using certain similarity criteria, is presented. The technique is based on the analysis of vectors representing regions associated with given types of critical points. An approach based on the Max Entropy in Metric Space (MEMS) is introduced in the paper, in order to extract a cluster of local features and to obtain an analysis of mechanical systems in the 2D and/or 3D spaces. The approach proposed in the paper can be effectively used in virtual prototyping and optimal designing of mechanical systems.

Keywords: Computer Aided Design (CAD) | Max Entropy in Metric Space (MEMS) | Maxinf principle | Multibody Mechanical Systems (MBS) | Optimal design | Virtual prototyping

Abstract: This research aimed to determine the effects of cryo-maceration at different temperatures on polyphenol content during the winemaking process of Chardonnay wine. Samples of Chardonnay grapes were subjected to rapid cooling processes by direct injection of liquid CO 2 to obtain final temperatures of 10.0, 8.0, 6.0 and 4.0 ◦ C and yield different batches of grape mash. Subsequently, each batch underwent the winemaking process to produce four different wines. The wines obtained were characterized by chemical analyses. We observed higher extraction of polyphenolic compounds with low-temperature cold maceration, particularly when the temperature was reduced from 10.0 to 6.0 ◦ C. Conversely, when the temperature was reduced below 6.0 ◦ C, the increase in polyphenol content in wine was negligible, whereas CO 2 consumption increased. Furthermore, a numerical simulation was performed to determine the pipe length, L 0 , after which the temperature was constant. This condition is very important because it guarantees that after the length L 0 , the thermodynamic exchange between liquid CO 2 and is complete, eliminating the possibility of liquid CO 2 pockets in the cyclone.

Keywords: Chardonnay grapes | Cryo-maceration | Liquid carbon dioxide | Numerical simulation | Polyphenols

Abstract: After briefly recalling the main problems that arise in the study of globe valves for alternative pumps, a methodology has been set up in order to refine the design. The obtained method has the advantages of simplicity and independence from empirical diagrams. In summary, from the obtained equation, the suitable values of the parameters can be deduced, based on the assigned data (capacity Q0 and number of rounds n) of all the dimensions of the valve or of the valves. Depending on the parameter values, it is possible to identify the most suitable kind of valve: a single dish-shaped valve, a ring valve, a valve with several rings or a group of valves.

Keywords: Capacity | Design | FEM | Fluent | Valve

Abstract: In this paper, the problem of the evaluation of the uncertainties that originate in the complex design process of a new system is analyzed, paying particular attention to multibody mechanical systems. To this end, the Wiener-Shannon's axioms are extended to non-probabilistic events and a theory of information for non-repetitive events is used as a measure of the reliability of data. The selection of the solutions consistent with the values of the design constraints is performed by analyzing the complexity of the relation matrix and using the idea of information in the metric space. Comparing the alternatives in terms of the amount of entropy resulting from the various distribution, this method is capable of finding the optimal solution that can be obtained with the available resources. In the paper, the algorithmic steps of the proposed method are discussed and an illustrative numerical example is provided.

Keywords: Complexity | Design | Fair division | Multibody systems | Non-probabilistic entropy | Uncertainty

Abstract: In this paper, the use of the MaxInf Principle in real optimization problems is investigated for engineering applications, where the current design solution is actually an engineering approximation. In industrial manufacturing, multibody system simulations can be used to develop new machines and mechanisms by using virtual prototyping, where an axiomatic design can be employed to analyze the independence of elements and the complexity of connections forming a general mechanical system. In the classic theories of Fisher and Wiener-Shannon, the idea of information is a measure of only probabilistic and repetitive events. However, this idea is broader than the probability alone field. Thus, the Wiener-Shannon's axioms can be extended to non-probabilistic events and it is possible to introduce a theory of information for non-repetitive events as a measure of the reliability of data for complex mechanical systems. To this end, one can devise engineering solutions consistent with the values of the design constraints analyzing the complexity of the relation matrix and using the idea of information in the metric space. The final solution gives the entropic measure of epistemic uncertainties which can be used in multibody system models, analyzed with an axiomatic design.

Keywords: Arrow's impossibility theorem | Axiomatic design | Axioms | Information | Multibody systems | Non-probabilistic entropy

Abstract: Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

Keywords: Back-propagation (BP) | Bearing fault diagnosis | Delay time | Embedding dimension | FNN | HMM | Improved LMD | MI | Multi-scale permutation entropy

Abstract: In this paper, a procedure for the optimization of the working surface of a plough for a soil type considered and to analyze the efficiency, is performed. It fits in the set of numerical - experimental techniques used for the improvement of the energy performance related to ploughing of agricultural soil. In the first part of this paper, it describes how to generate a family of working surfaces for assigned geometrical and process parameters; latter, by mean the use of a physical-mathematical model which describes the soil - plough interaction, it is examined the draught resistance changing in function of the geometric and process parameters for a soil considered, aimed to optimize the shape. A commercially available plough, subsequently, was examined and, applying such methods, its parametric representation and the optimized surface were obtained for the examined soil.

Keywords: Design optimization | Plough working surface | Soil mechanics | Soil-tool interaction

Abstract: In this paper we analyze the capabilities of a routine, based on Fuzzy logic, for elaborating a data set coming from a CMM (Coordinate Measuring Machine). We will show how to obtain, during holes measuring, the best measure, so that the approximation error is minimized. Moreover the CMM on-board software can elaborate these data and select the mathematical representation of the stored data, by identifying quotes, measures, axes, diameters, tolerances and so on. Information on measured parts is usually elaborated by an algorithm based on the least square squared error method, in order to evaluate the good shape of the hole; our purpose is to propose a new kind of approach, based on the Inferential Fuzzy system method, both to reduce the number of measured points, and to obtain the same accuracy. Our approach enables to measure the holes with a number of points lower than those usually needed for the CMM software. Thus time spent for obtaining a good measure is significantly reduced.

Keywords: Accuracy | Coordinate Measuring Machine | Fuzzy inference | Holes measurement | Precision

Abstract: The psycho-physical state of a driver has been widely recognized as the crucial point in any issue concerning the development of models headed to improve the vehicle safety, either inherent and active, so much so that almost all the new in-vehicle technology, currently developing at a rapid rate, introduces devices to continuously monitor the driver. This paper describes the architecture of a real time, performance-based, driver monitoring system able to detect the decrease in driver performances due to driver distraction, fatigue, sleepiness and alcohol or drugs ingestion. The system processes the instantaneous lateral position of the vehicle on the road. This allows to work out an index of the lane keeping precision by means of the lateral position standard deviation (SDLP). This latter and the road environment complexity has been processed by a fuzzy inference system that has, as an output, a score reflecting the driver's ability to maintain adequate lane-tracking movements for a given road scenario. Fuzzy membership functions and inference rules has been based and optimized on data obtained on 12 subjects performing driving simulation under both baseline condition and two different cognitive overload situations induced by different secondary tasks, one with visual distraction, the other characterized by a pure cognitive load, respectively. Aim of the work is to attain to a black-box type devices that could both provide warnings or reminding in case of risky driving and encourage the driver to improve his behavior. Advantages would also come for parents of novice drivers promptly alerted for improper driving and even for the car insurance companies that could reward safe drivers.

Keywords: Cognitive load | Drive simulator | Fuzzy logic | Safe driving

Abstract: A diagnostic engine for supporting physicians in analyzing symptoms and anamnesis of patients is proposed. The system uses fuzzy logic as the picklock to overcome difficulties typically encountered when dealing with a computer aided model for medical diagnosis, since the large number of parameters to be taken into account. The model in its current formulation allows physicians to have immediate diagnostic hypotheses considering patient's anamnesis, symptoms, drug administered and previously formulated diagnoses, simultaneously accessing available databases and suggesting new diagnostic examinations. An auto-adaptative fuzzy logic approach can be easily implemented, that could allow the model to progressively learn from each formulated diagnosis. © 2013 IFMBE.

Keywords: computerized diagnosis | fuzzy logic | Medical diagnosis | modeling

Abstract: When designing mechanical assemblies, assembly tolerance design is an important issue which must be seriously considered by designers. Assembly tolerances reflect functional requirements of assembling, which can be used to control assembling qualities and production costs. This paper proposes a new method for designing assembly tolerance networks of mechanical assemblies. The method establishes the assembly structure tree model of an assembly based on its product structure tree model. On this basis, assembly information model and assembly relation model are set up based on polychromatic sets (PS) theory. According to the two models, the systems of location relation equations and interference relation equations are established. Then, using methods of topologically related surfaces (TTRS) theory and variational geometric constraints (VGC) theory, three VGC reasoning matrices are constructed. According to corresponding relations between VGCs and assembly tolerance types, the reasoning matrices of tolerance types are also established by using contour matrices of PS. Finally, an exemplary product is used to construct its assembly tolerance networks and meanwhile to verify the feasibility and effectiveness of the proposed method. Copyright © 2012 Yi Zhang et al.

Abstract: The search for safe vehicles is increasing with both diffusion of high traffic density over the world and availability of new technologies providing sophisticated tools previously impossible to realize. Design and development of the necessary devices may be based on simulation tests that reduce cost allowing trials in many directions. A proper choice of the arrangement of the drive simulators, as much as of the parameters to be monitored, is of basic importance as they can address the design of devices somehow responsible for the drivers safety or, even their lives. This system setup, consisting of a free car simulator equipped with a monitoring system, collects in a nonintrusive way data of the car lateral position within the road lane and of its first derivative. Based on these measured parameters, the system is able to detect symptoms of drowsiness and sleepiness. The analysis is realized by a fuzzy inferential process that provides an immediate warning signal as soon as drowsiness is detected with a high level of certainty. Enhancement of reliability and minimisation of the false alarm rate are obtained by operating continuous comparison between learned driver typical modalities of operation on the control command of the vehicle the pattern recorded. © 2012 Pasquale Sena et al.

Abstract: Naringenin (Nn) and Quercetin (Q) have numerous health benefits particularly due to their antioxidant properties. However, their low solubility, bioavailability and stability limit their use as components for functional foods, nutraceuticals and pharmaceutical agents. In this research, Nn- and Q-microparticles were produced by a spray-drying process using a combination of cellulose acetate phthalate (CAP) as coating gastroresistant polymer and swelling or surfactant agents as enhancers of dissolution rate. Raw materials and microparticles produced were all characterized by particle size analysis, differential scanning calorimetry, X-ray diffraction, and imaged by electron and fluorescence microscopy. During 12 months, storage stability was evaluated by analyzing drug content, HPLC and DSC profiles, as well as antioxidant activity (DPPH test). In vitro dissolution tests, using a pH-change method, were carried out to investigate the influence of formulative parameters on flavonoid release from the microparticles. Presence of a combination of CAP and surfactants or swelling agents in the formulations produced microparticles with good resistance at low pH of the gastric fluid and complete flavonoid release in the intestinal environment. The spray-drying technique and the process conditions selected have given satisfying encapsulation efficiency and product yield. The microencapsulation have improved the technological characteristics of the powders such as morphology and size, have given long-lasting storage stability and have preserved the antioxidant properties. © 2010 Elsevier Ltd. All rights reserved.

Keywords: Antioxidant activity | Enhancers of the dissolution rate | Naringenin | Quercetin | Spray-dried gastroresistant microparticles | Storage stability

Abstract: Natural gas is currently the natural substitute of petroleum as an energy source, since the foreseen ending up of this latter in the next decades. As a matter of fact, natural gas is easier to handle, less dangerous to be transported, somehow environmentally more friendly. The gas ducts operate with large flow rates over very long distances at high pressures, which are usually lowered in proximity of the final substations by lamination valves which, in fact, dissipate energy. However, a careful management of the pressure reduction may allow an energy recovery while using the gas expansion to operate a turbine. In this case, gas must be preheated to compensate for the energy required by the expansion. A proper control of all the parameters involved becomes crucial to an intelligent use of these resources. In this paper, the possibility of using a pre-heating system has been examined as a way to intensify the energy cycle in an expansion substation of the city gas network. Fuzzy logic has been used to optimize the natural gas expansion in a turbine to produce electrical energy. A fuzzy system has been designed and realized to control the whole process of gas expansion, from the gas pre-heating to the pressure reduction. The system operates over the whole year, accounting for the pressure, temperature, and gas flow rate variations experienced in the gas line. The exit values of the latter and the inlet value of the gas pressure are selected as input variables, being the output variable the temperature of the pre-heating water at the heat exchanger inlet. © 2010 Arcangelo Pellegrino and Francesco Villecco.

Abstract: In this paper, the propulsive motility characteristics and the corresponding motion equations of a self-designed sub-mini underwater robot were analyzed. And the disturbance observer was used in propulsive motility control of the underwater robot. According to feedback control principle and dynamic performance index of controlled object, the disturbance observer parameter was designed. Digital simulation of propulsion system indicated that system had a better inhibition effect to the disturbance and the propulsive motility control performance of the underwater robot had also been improved. So all the experimental results showed that the disturbance observer has good robustness, also it is very useful to deal with disturbance rejection, which shows its practical value and good performance in motion control. © 2010 Springer-Verlag Berlin Heidelberg.

Keywords: Disturbance observer | Propulsion control | Simulation platform | Sub-mini underwater robot

Abstract: This Twenty-first century global systems such as climate change models, energy systems, and international trade, have traditionally trusted conventional logic and mathematics to reduce complexity. The 2008 failing of conventional predictive models (in all economical, political, and social spheres) proved that a new approach is needed to understand and predict the behaviour of man-made complex systems. This paper is an attempt to introduce a fuzzy logic methodology for formulating energy policies. A new definition of an energy system is given based on its fuzzy functionality and complex inter-relational properties. Instead of crisp numbers, fuzzy values are proposed to be defined and used for evaluating policy parameters. Unknown or poorly known factors can be taken into account through adding fuzzy constants to otherwise linear equations established between the system parameters. Fuzzyness of the variables are transferred from the inputs to the outputs without being unduly magnified or eliminated. Results of a fuzzy model of energy policy can be expressed in fuzzy values, reflecting the realities and providing flexibility in implementation. © 2009 IEEE.

Keywords: Artificial intelligenc | Complexity | Energy policy | Energy system | Fuzzy logic

Abstract: Early human civilizations developed along water corridors. With increasing dependency of human activities on energy, sustainability of future civilizations would be largely linked to sustainability of energy resources and systems. The tie between energy and socio-environmental sustainability, though obvious remains nebulous mostly because, neither energy nor sustainability are clearly defined. While thermodynamic definition of energy is relatively clear, its nature (as linked to human activities and sustainable development) is not well understood. This paper is an attempt to present a metric for the components and attributes of energy resources and technologies as interfaced with human civilization. A fuzzy logic model is used to scale energy systems based on their valued attributes (such as storability, transformability, quality, transportability, availability, environmental value and resource sustainability). The model is used to predict future energy corridors and their association with economical growth and sustainability. It is shown that green energy systems should be developed not in isolation but integrated in intelligent "synergetic" systems to meet the energy demands of future human civilizations. © 2009 Springer Berlin Heidelberg.

Keywords: Fuzzy Metric | Renewable Energy | Sustainability

Abstract: Fuel cell stack systems are under intensive development for mobile and stationary power applications. In particular, Proton Exchange Membrane (PEM) Fuel Cells (also known as Polymer Electrolyte Membrane Fuel Cells) are currently in a more mature stage for ground vehicle applications. This paper proposes a theoretical innovative approach to the analysis of the electrochemical transient behavior (anode-cathode). The transient behavior due to the electrochemical dynamic may impact the behavior of the resulting load current. Boundary conditions influence the resulting electric field, the boundary condition are strongly depending of H<inf>2</inf> and O<inf>2</inf> physical parameters. Maxwell's equations are used in order to describe the model. Solutions through dyadic harmonic wavelets at different levels of resolution are presented. Wavelets approach, through their different space-time levels of resolution, can favorable describe the segmented space structure of the stack. In the meantime, transient dynamic inside of the stack can be adaptively studied. An outlook closes the paper. © Springer-Verlag Berlin Heidelberg 2006.