Search results for: parameter averaging

Authors: Won Jung, Wan Emri

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Ferrite magnet is widely used in many automotive components such as motors and alternators. Magnets used inside the components must be in good quality to ensure the high level of performance. The purpose of this study is to design input parameters that optimize the ferrite magnet production process to ensure the quality and reliability of manufactured products. Design of Experiments (DOE) and Statistical Process Control (SPC) are used as mutual supplementations to optimize the process. DOE and SPC are quality tools being used in the industry to monitor and improve the manufacturing process condition. These tools are practically used to maintain the process on target and within the limits of natural variation. A mixed Taguchi method is utilized for optimization purpose as a part of DOE analysis. SPC with proportion data is applied to assess the output parameters to determine the optimal operating conditions. An example of case involving the monitoring and optimization of ferrite magnet process was presented to demonstrate the effectiveness of this approach. Through the utilization of these tools, reliable magnets can be produced by following the step by step procedures of proposed framework. One of the main contributions of this study was producing the crack free magnets by applying the proposed parameter design.

Keywords: ferrite magnet, crack, reliability, process optimization, Taguchi method

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Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

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Authors: Arnab Mondal, Sanjeev Kumar Sharma, Ranjit Kumar Upadhyay

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We study the spatiotemporal dynamics of a neuronal cable. The processes of one- dimensional (1D) and 2D diffusion are considered for a single variable, which is the membrane voltage, i.e., membrane voltage diffusively interacts for spatiotemporal pattern formalism. The recovery and other variables interact through the membrane voltage. A 3D Leech-Heart (LH) model is introduced to investigate the nonlinear responses of an excitable neuronal cable. The deterministic LH model shows different types of firing properties. We explore the parameter space of the uncoupled LH model and based on the bifurcation diagram, considering v_k2_ashift as a bifurcation parameter, we analyze the 1D diffusion dynamics in three regimes: bursting, regular spiking, and a quiescent state. Depending on parameters, it is shown that the diffusive system may generate regular and irregular bursting or spiking behavior. Further, it is explored a 2D diffusion acting on the membrane voltage, where different types of patterns can be observed. The results show that the LH neurons with different firing characteristics depending on the control parameters participate in a collective behavior of an information processing system that depends on the overall network.

Keywords: bifurcation, pattern formation, spatio-temporal dynamics, stability analysis

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Authors: Amir Hadi Ziaie

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In the present work, we revisit the collapse process of a spherically symmetric homogeneous scalar field (in FRW background) minimally coupled to gravity, when the phase-space deformations are taken into account. Such a deformation is mathematically introduced as a particular type of noncommutativity between the canonical momenta of the scale factor and of the scalar field. In the absence of such deformation, the collapse culminates in a spacetime singularity. However, when the phase-space is deformed, we find that the singularity is removed by a non-singular bounce, beyond which the collapsing cloud re-expands to infinity. More precisely, for negative values of the deformation parameter, we identify the appearance of a negative pressure, which decelerates the collapse to finally avoid the singularity formation. While in the un-deformed case, the horizon curve monotonically decreases to finally cover the singularity, in the deformed case the horizon has a minimum value that this value depends on deformation parameter and initial configuration of the collapse. Such a setting predicts a threshold mass for black hole formation in stellar collapse and manifests the role of non-commutative geometry in physics and especially in stellar collapse and supernova explosion.

Keywords: gravitational collapse, non-commutative geometry, spacetime singularity, black hole physics

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Authors: Chao-Yang Huang, Rwey-Hua Cherng, Chung-Lin Fu, Yuan-Lung Lo

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Solar energy is one of the replaceable choices to reduce the CO2 emission produced by conventional power plants in the modern society. As an island which is frequently visited by strong typhoons and earthquakes, it is an urgent issue for Taiwan to make an effort in revising the local regulations to strengthen the safety design of photovoltaic systems. Currently, the Taiwanese code for wind resistant design of structures does not have a clear explanation on photovoltaic systems, especially when the systems are arranged in arrayed format. Furthermore, when the arrayed photovoltaic system is mounted on the rooftop, the approaching flow is significantly altered by the building and led to different pressure pattern in the different area of the photovoltaic system. In this study, L-shape arrayed photovoltaic system is mounted on the ground of the wind tunnel and then mounted on the building rooftop. The system is consisted of 60 PV models. Each panel model is equivalent to a full size of 3.0 m in depth and 10.0 m in length. Six pressure taps are installed on the upper surface of the panel model and the other six are on the bottom surface to measure the net pressures. Wind attack angle is varied from 0° to 360° in a 10° interval for the worst concern due to wind direction. The sampling rate of the pressure scanning system is set as high enough to precisely estimate the peak pressure and at least 20 samples are recorded for good ensemble average stability. Each sample is equivalent to 10-minute time length in full scale. All the scale factors, including timescale, length scale, and velocity scale, are properly verified by similarity rules in low wind speed wind tunnel environment. The purpose of L-shape arrayed system is for the understanding the pressure characteristics at the corner area. Extreme value analysis is applied to obtain the design pressure coefficient for each net pressure. The commonly utilized Cook-and-Mayne coefficient, 78%, is set to the target non-exceedance probability for design pressure coefficients under Gumbel distribution. Best linear unbiased estimator method is utilized for the Gumbel parameter identification. Careful time moving averaging method is also concerned in data processing. Results show that when the arrayed photovoltaic system is mounted on the ground, the first row of the panels reveals stronger positive pressure than that mounted on the rooftop. Due to the flow separation occurring at the building edge, the first row of the panels on the rooftop is most in negative pressures; the last row, on the other hand, shows positive pressures because of the flow reattachment. Different areas also have different pressure patterns, which corresponds well to the regulations in ASCE7-16 describing the area division for design values. Several minor observations are found according to parametric studies, such as rooftop edge effect, parapet effect, building aspect effect, row interval effect, and so on. General comments are then made for the proposal of regulation revision in Taiwanese code.

Keywords: aerodynamic force coefficient, ground-mounted, roof-mounted, wind tunnel test, photovoltaic

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Authors: Ashish Ashish

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In the last few decades, the discrete chaos of difference equations has gained a massive attention of academicians and scholars due to its tremendous applications in each and every branch of science, such as cryptography, traffic control models, secure communications, weather forecasting, and engineering. In this article, a generalized logistic discrete map is established and discrete chaos is reported through period doubling bifurcation, period three orbit and Lyapunov exponent. It is interesting to see that the generalized logistic map exhibits superior chaos due to the presence of an extra degree of freedom of an ordered parameter. The period doubling bifurcation and Lyapunov exponent are demonstrated for some particular values of parameter and the discrete chaos is determined in the sense of Devaney's definition of chaos theoretically as well as numerically. Moreover, the study discusses an extended chaos based image encryption and decryption scheme in cryptography using this novel system. Surprisingly, a larger key space for coding and more sensitive dependence on initial conditions are examined for encryption and decryption of text messages, images and videos which secure the system strongly from external cyber attacks, coding attacks, statistic attacks and differential attacks.

Keywords: chaos, period-doubling, logistic map, Lyapunov exponent, image encryption

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Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

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Authors: M. Y. Ismail, M. Inam

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This paper presents the design and analysis of Liquid Crystal (LC) based tunable reflectarray antenna with slot embedded patch element configurations within X-band frequency range. The slots are shown to modify the surface current distribution on the patch element of reflectarray which causes the resonant patch element to provide different resonant frequencies depending on the slot dimensions. The simulated results are supported and verified by waveguide scattering parameter measurements of different reflectarray unit cells. Different rectangular slots on patch element have been fabricated and a change in resonant frequency from 10.46GHz to 8.78GHz has been demonstrated as the width of the rectangular slot is varied from 0.2W to 0.6W. The rectangular slot in the center of the patch element has also been utilized for the frequency tunable reflectarray antenna design based on K-15 Nematic LC. For the active reflectarray antenna design, a frequency tunability of 1.2% from 10GHz to 9.88GHz has been demonstrated with a dynamic phase range of 103° provided by the measured scattering parameter results. Time consumed by liquid crystals for reconfiguration, which is one of the drawback of LC based design, has also been disused in this paper.

Keywords: liquid crystal, tunable reflectarray, frequency tunability, dynamic phase range

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Thang Minh Le, Hadi Khabbaz

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The coefficient of consolidation is a parameter governing the rate at which saturated soil particularly clay undergoes consolidation when subjected to an increase in pressure. The rate and amount of compression in soil varies with the rate that pore water is lost; and hence depends on soil permeability. Over many years, various methods have been proposed to determine the coefficient of consolidation, cv, which is an indication of the rate of foundation settlement on soft ground. However, defining this parameter is often problematic and heavily relies on graphical techniques, which are subject to some uncertainties. This paper initially presents an overview of many well-established methods to determine the vertical coefficient of consolidation from the incremental loading consolidation tests. An array of consolidation tests was conducted on the undisturbed clay samples, collected at various depths from a site in Nakdong river delta, Busan, South Korea. The consolidation test results on these soft sensitive clay samples were employed to evaluate the targeted methods to predict the settlement rate of Busan clay. In relationship of time-displacement-velocity, a total of 3 method groups from 10 common procedures were classified and compared together. Discussions on study results will be also provided.

Keywords: Busan clay, coefficient of consolidation, constant rate of strain, incremental loading

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Authors: Masni Mat Yusoff, Michael H. Gordon, Keshavan Niranjan

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The study reports on the effect of aqueous enzymatic extraction (AEE) parameters on the Moringa oleifera (MO) oil yield and the formation of emulsion at the end of the process. A mixture of protease and cellulase enzymes was used at 3:1 (w/w) ratio. The highest oil yield of 19% (g oil/g sample) was recovered with the use of a mixture of pH 6, 1:4 material/moisture ratio, and incubation temperature, time, and shaking speed of 50 ⁰C, 12.5 hr, and 300 stroke/min, respectively. The use of pH 6 and 8 resulted in grain emulsions, while solid-intact emulsion was observed at pH 4. Upon fixing certain parameters, higher oil yield was extracted with the use of lower material/moisture ratio and higher shaking speed. Longer incubation time of 24 hr resulted in significantly (p < 0.05) similar oil yield with that of 12.5 hr, and an incubation temperature of 50 ⁰C resulted in significantly (p < 0.05) higher oil yield than that of 60 ⁰C. In overall, each AEE parameter showed significant effects on both the MO oil yields and the emulsions formed. One of the major disadvantages of an AEE process is the formation of emulsions which require further de-emulsification step for higher oil recovery. Therefore, critical studies on the effect of each AEE parameter may assist in minimizing the amount of emulsions formed whilst extracting highest total MO oil yield possible.

Keywords: enzyme, emulsion, Moringa oleifera, oil yield

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Authors: Anwar Beg, Sireetorn Kuharat, Rashid Mehmood, Rabil Tabassum, Meisam Babaie

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Nano-polymeric solar paints and sol-gels have emerged as a major new development in solar cell/collector coatings offering significant improvements in durability, anti-corrosion and thermal efficiency. They also exhibit substantial viscosity variation with temperature which can be exploited in solar collector designs. Modern manufacturing processes for such nano-rheological materials frequently employ stagnation flow dynamics under high temperature which invokes radiative heat transfer. Motivated by elaborating in further detail the nanoscale heat, mass and momentum characteristics of such sol gels, the present article presents a mathematical and computational study of the steady, two-dimensional, non-aligned thermo-fluid boundary layer transport of copper metal-doped water-based nano-polymeric sol gels under radiative heat flux. To simulate real nano-polymer boundary interface dynamics, thermal slip is analysed at the wall. A temperature-dependent viscosity is also considered. The Tiwari-Das nanofluid model is deployed which features a volume fraction for the nanoparticle concentration. This approach also features a Maxwell-Garnet model for the nanofluid thermal conductivity. The conservation equations for mass, normal and tangential momentum and energy (heat) are normalized via appropriate transformations to generate a multi-degree, ordinary differential, non-linear, coupled boundary value problem. Numerical solutions are obtained via the stable, efficient Runge-Kutta-Fehlberg scheme with shooting quadrature in MATLAB symbolic software. Validation of solutions is achieved with a Variational Iterative Method (VIM) utilizing Langrangian multipliers. The impact of key emerging dimensionless parameters i.e. obliqueness parameter, radiation-conduction Rosseland number (Rd), thermal slip parameter (α), viscosity parameter (m), nanoparticles volume fraction (ϕ) on non-dimensional normal and tangential velocity components, temperature, wall shear stress, local heat flux and streamline distributions is visualized graphically. Shear stress and temperature are boosted with increasing radiative effect whereas local heat flux is reduced. Increasing wall thermal slip parameter depletes temperatures. With greater volume fraction of copper nanoparticles temperature and thermal boundary layer thickness is elevated. Streamlines are found to be skewed markedly towards the left with positive obliqueness parameter.

Keywords: non-orthogonal stagnation-point heat transfer, solar nano-polymer coating, MATLAB numerical quadrature, Variational Iterative Method (VIM)

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Authors: Felix Givois, Nicolas R. Gauger, Matthias Kabel

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The efficient digital material characterization is of great interest to many fields of application. It consists of the following three steps. First, a 3D reconstruction of 2D scans must be performed. Then, the resulting gray-value image of the material sample is enhanced by image processing methods. Finally, partial differential equations (PDE) are solved on the segmented image, and by averaging the resulting solutions fields, effective properties like stiffness or conductivity can be computed. Due to the high resolution of current CT images, the latter is typically performed with matrix-free solvers. Among them, a solver that uses the explicit formula of the Green-Eshelby operator in Fourier space has been proposed by Moulinec and Suquet. Its algorithmic, most complex part is the Fast Fourier Transformation (FFT). In our talk, we will discuss the potential quantum advantage that can be obtained by replacing the FFT with the Quantum Fourier Transformation (QFT). We will especially show that the data transfer for noisy intermediate-scale quantum (NISQ) devices can be improved by using appropriate boundary conditions for the PDE, which also allows using semi-classical versions of the QFT. In the end, we will compare the results of the QFT-based algorithm for simple geometries with the results of the FFT-based homogenization method.

Keywords: most likelihood amplitude estimation (MLQAE), numerical homogenization, quantum Fourier transformation (QFT), NISQ devises

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Authors: Amirhossein Mohajerzadeh, Abolghasem Mohajerzadeh

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In this paper, target parameter is estimated with desirable precision in hierarchical wireless sensor networks (WSN) while the proposed algorithm also tries to prolong network lifetime as much as possible, using efficient data collecting algorithm. Target parameter distribution function is considered unknown. Sensor nodes sense the environment and send the data to the base station called fusion center (FC) using hierarchical data collecting algorithm. FC builds underlying phenomena based on collected data. Considering the aggregation level, x, the goal is providing the essential infrastructure to find the best value for aggregation level in order to prolong network lifetime as much as possible, while desirable accuracy is guaranteed (required sample size is fully depended on desirable precision). First, the sample size calculation algorithm is discussed, second, the average queue length based on M/M[x]/1/K queue model is determined and it is used for energy consumption calculation. Nodes can decrease transmission cost by aggregating incoming data. Furthermore, the performance of the new algorithm is evaluated in terms of lifetime and estimation accuracy.

Keywords: aggregation, estimation, queuing, wireless sensor network

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Authors: Balraju Vadlakonda, Narasimha Mangadoddy

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The hydrodynamic behavior in a laboratory column flotation was analyzed using particle image velocimetry. For complete characterization of column flotation, it is necessary to determine the flow velocity induced by bubbles in the liquid phase, the bubble velocity and bubble characteristics:diameter,shape and bubble size distribution. An experimental procedure for analyzing simultaneous, phase-separated velocity measurements in two-phase flows was introduced. The non-invasive PIV technique has used to quantify the instantaneous flow field, as well as the time averaged flow patterns in selected planes of the column. Using the novel particle velocimetry (PIV) technique by the combination of fluorescent tracer particles, shadowgraphy and digital phase separation with masking technique measured the bubble velocity as well as the Reynolds stresses in the column. Axial and radial mean velocities as well as fluctuating components were determined for both phases by averaging the sufficient number of double images. Bubble size distribution was cross validated with high speed video camera. Average turbulent kinetic energy of bubble were analyzed. Different air flow rates were considered in the experiments.

Keywords: particle image velocimetry (PIV), bubble velocity, bubble diameter, turbulent kinetic energy

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Authors: Sung-Min Park, Chan Kwon Park, Chae-Bogk Kim

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The purpose of this study is to analyze the relationship between transaction fairness, social capital, supply chain integration and sustainability. Based on the previous studies, measurement items were determined by using SPSS 22 and exploratory factor analysis was performed, and again, using AMOS 21 for confirmatory factor analysis and path analysis was performed by using study items that satisfy reliability, validity, and appropriateness of measurement model. It has shown that transaction fairness has a (+) significant effect on social capital, social capital on supply chain integration, supply chain integration on economic sustainability and social sustainability, and has a (+), but not significant effect on environmental sustainability. It has shown that supply chain integration has been proven to play a role as a parameter between social capital and economic and social sustainability, but not as a parameter between environmental sustainability. Through this study, it is suggested that clearly examining the relationship between fairness of trade, social capital, supply chain integration and sustainability, maintaining fairness of the transaction make formation of social capital, and further integration of supply chain, and achieve sustainability of entire supply chain.

Keywords: transaction fairness, social capital, supply chain integration, sustainability

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Authors: Nandhini Ravi, Muthukumaran Shanmugam

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Metals are being replaced by thermoplastic polymer composites in automotive industries because of their low density, easiness to fabricate, low cost and good wear resistance. Complex polymer components consist of assemblies of smaller parts which can be joined by friction stir welding. This study deals with the additive friction stir welding of 15 wt.% glass fiber reinforced polyamide 66 composite which is a modified technique of the conventional friction stir welding by the addition of a filler plate for the heating of the composite work piece through the tool during the welding process. Welding at different combinations of tool rotational speed, travel speed and tool plunge depth was done after which the tensile strength of the respective experiments was determined. The maximum tensile strength obtained was 77 MPa which was 80% of the strength of the base material. The process parameters were optimized using the L9 orthogonal array and also the effect of individual welding parameter on the tensile strength was studied. The optimum parameter combination was determined with the help of ANOVA studies. The hardness of the welded joints was studied with the help of Shore Durometer which yielded the maximum of D 75.

Keywords: additive friction stir welding, polyamide 66, process parameters, thermoplastic polymer composite

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Authors: Shank Kulkarni, Alireza Tabarraei

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The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear hyper-viscoelastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive loading conditions at various strain rates. Four parameter Ogden model and Mooney Rivlin model are used to modeling the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using both a three-parameter standard linear solid (SLS) model and a K-BKZ model. Comparison of the modeling results with experiments shows that Odgen and SLS model can more accurately predict the behavior of polyurea. The material parameters of the model are found by curve fitting of the proposed model to the uniaxial compression test data. The proposed model can closely reproduce the stress-strain behavior of polyurea for strain rates up to 6500 /s.

Keywords: constitutive modelling, ogden model, polyurea, SLS model, uniaxial compression test

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Authors: Abdulrahman Aljabri, Essam R. I. Mahmoud, Hamad Almohamedi, Zhengyi Jiang

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Cold rolled thin strip has received intensive attention through technological and theoretical progress in the rolling process, as well as researchers have focused on its control during rolling as an essential parameter for producing thinner strip with good shape and profile. An advanced approach has been proposed to analysis the thin strip profile in cold rolling of pair roll crossing and shifting mill using Finite Element Analysis (FEA) with an ALE technique. The ALE (Arbitrary Lagrangian-Eulerian) techniques to enable more flexibility of the ALE technique in the adjustment of the finite element mesh, which provides a significant tool for simulating the thin strip under realistic rolling process constraint and provide accurate model results. The FEA can provide theoretical basis for the 3D model of controlling the strip shape and profile in thin strip rolling, and deliver an optimal rolling process parameter, and suggest corrective changes during cold rolling of thin strip.

Keywords: pair roll crossing, work roll shifting, strip shape and profile, finite element modeling

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Authors: Lavanya Srinivasan

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Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat, and case recorded using longwave infrared, short wave infrared, medium wave infrared, and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using YOLO, background subtraction, silhouettes extraction, and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the principal component analysis and recognised using different classifiers. The comparative results with the different classifier show that linear discriminant analysis outperforms other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, YOLO

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Authors: Dadasaheb R. Jawre, Maya G. Unde

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Prioritization of sub-watershed plays important role in watershed management. It shows the requirement of watershed to give a treatment for the green growth of the region and conservation of the sub-watersheds. There is a number of factors like topography of the region, climatic characteristics like rainfall and runoff, land-use land-cover, social factors which are related to the development of watershed for agricultural uses and domestic purposes in the region. The present research is throwing a focus on how morphometric parameters in association with GIS analysis will help in identifying the ranking of the sub-watersheds for further development which help of suggested watershed structures. Shevgaon and Pathardi tahsils are drought prone tahsils of Ahmednagar district in Maharashtra. These tahsils come under the semi-arid region. Sub-watershed prioritization is necessary for proper planning and management of natural resources for sustainable development of the study area. Less rainfall and increasing population pressure on the land as well as water resources lead to scarcity of the water in the region. Hence, researcher has selected Shevgaon and Pathardi tahsils for sub-watershed prioritization. There are seven sub-watersheds which selected for the present research paper. In the morphological analysis linear aspects, aerial aspects and relief aspects are considered for the prioritization. The largest sub-watershed is Erdha which is located at Karanji in Pathardi tahsil having an area of 145.06 km2 and smallest sub-watershed is Erandgaon which is located in Shevgaon tahsil having an area of 40.143 km2. For all seven sub-watersheds, seven morphometric parameters were considered for calculating the compound parameter values. Finally, compound parameter values are grouped into three groups such as, high priority (below 4.0), moderate priority (4.0 to 5.0) and low priority (above 5.0) according to the compound value Erandgaon, Chapadgaon and Tarak sub-watersheds comes under high priority group, Erdha and Domeshwar sub-watersheds come under moderate priority group and Chandani and Kasichi sub-watershed come under low priority group. Both the tahsils falls in drought prone area, after getting the watershed structure overall development of the region will take place.

Keywords: sub-watersheds, GIS and remote sensing, morphometric analysis, compound parameter value, prioritization

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Authors: Mohammad Obeidat, Ayman Mansour

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In this paper a unique issue rising from feedback control of Atrial Fibrillation monitoring system with embedded communication channels has been investigated. One of the important factors to measure the performance of the feedback control closed loop system is disturbance and noise attenuation factor. It is important that the feedback system can attenuate such disturbances on the atrial fibrillation heart rate signals. Communication channels depend on network traffic conditions and deliver different throughput, implying that the sampling intervals may change. Since signal estimation is updated on the arrival of new data, its dynamics actually change with the sampling interval. Consequently, interaction among sampling, signal estimation, and the controller will introduce new issues in remotely controlled Atrial Fibrillation system. This paper treats a remotely controlled atrial fibrillation system with one communication channel which connects between the heart rate and rhythm measurements to the remote controller. Typical and optimal signal estimation schemes is represented by a signal averaging filter with its time constant derived from the step size of the signal estimation algorithm.

Keywords: atrial fibrillation, communication channels, closed loop, estimation

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Authors: Haw-Long Lee, Win-Jin Chang, Yu-Ching Yang

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In the paper, the coupled equation of motion for the dynamic displacement of a fullerene moving in a (10,10) single-walled carbon nanotube (SWCNT) is derived using nonlocal Timoshenko beam theory, including the effects of rotary inertia and shear deformation. The effects of confined stiffness between the fullerene and nanotube, foundation stiffness, and nonlocal parameter on the dynamic behavior are analyzed using the Runge-Kutta Method. The numerical solution is in agreement with the analytical result for the special case. The numerical results show that increasing the confined stiffness and foundation stiffness decrease the dynamic displacement of SWCNT. However, the dynamic displacement increases with increasing the nonlocal parameter. In addition, result using the Euler beam theory and the Timoshenko beam theory are compared. It can be found that ignoring the effects of rotary inertia and shear deformation leads to an underestimation of the displacement.

Keywords: single-walled carbon nanotube, nanoparticle delivery, Nonlocal Timoshenko beam theory, Runge-Kutta Method, Van der Waals force

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Authors: Victoria Goodall, Paul Fatti, Norman Owen-Smith

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Hidden Markov Models have become popular for the analysis of animal tracking data. These models are being used to model the movements of a variety of species in many areas around the world. A common assumption of the model is that the observations need to have regular time steps. In many ecological studies, this will not be the case. The objective of the research is to modify the movement model to allow for irregularly spaced locations and investigate the effect on the inferences which can be made about the latent states. A modification of the likelihood function to allow for these irregular spaced locations is investigated, without using interpolation or averaging the movement rate. The suitability of the modification is investigated using GPS tracking data for lion (Panthera leo) in South Africa, with many observations obtained during the night, and few observations during the day. Many nocturnal predator tracking studies are set up in this way, to obtain many locations at night when the animal is most active and is difficult to observe. Few observations are obtained during the day, when the animal is expected to rest and is potentially easier to observe. Modifying the likelihood function allows the popular Hidden Markov Model framework to be used to model these irregular spaced locations, making use of all the observed data.

Keywords: hidden Markov Models, irregular observations, animal movement modelling, nocturnal predator

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Authors: Amir Mahyar Khorasani, Ian Gibson, Moshe Goldberg, Mohammad Masoud Movahedi, Guy Littlefair

Abstract:

One of the important factors in manufacturing processes especially machining operations is surface quality. Improving this parameter results in improving fatigue strength, corrosion resistance, creep life and surface friction. The reliability and clearance of removable joints such as thread and nuts are highly related to the surface roughness. In this work, the effect of different cutting parameters such as cutting fluid pressure, feed rate and cutting speed on the surface quality of the crest of thread in the high-speed milling of Brass C3600 have been determined. Two popular neural networks containing MLP and RBF coupling with Taguchi L32 have been used to model surface roughness which was shown to be highly adept for such tasks. The contribution of this work is modelling surface roughness on the crest of the thread by using precise profilometer with nanoscale resolution. Experimental tests have been carried out for validation and approved suitable accuracy of the proposed model. Also analysing the interaction of parameters two by two showed that the most effective cutting parameter on the surface value is feed rate followed by cutting speed and cutting fluid pressure.

Keywords: artificial neural networks, cutting conditions, high-speed machining, surface roughness, thread milling

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Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

Abstract:

The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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Authors: Sofiane Bououden, Ilyes Boulkaibet

Abstract:

In this paper, a robust fault-tolerant predictive control (FTPC) strategy is proposed for systems with linear parameter varying (LPV) models and input constraints subject to sensor faults. Generally, virtual observers are used for improving the observation precision and reduce the impacts of sensor faults and uncertainties in the system. However, this type of observer lacks certain system measurements which substantially reduce its accuracy. To deal with this issue, a real observer is then designed based on the virtual observer, and consequently a real observer-based robust predictive control is designed for polytopic LPV systems. Moreover, the proposed observer can entirely assure that all system states and sensor faults are estimated. As a result, and based on both observers, a robust fault-tolerant predictive control is then established via the Lyapunov method where sufficient conditions are proposed, for stability analysis and control purposes, in linear matrix inequalities (LMIs) form. Finally, simulation results are given to show the effectiveness of the proposed approach.

Keywords: linear parameter varying systems, fault-tolerant predictive control, observer-based control, sensor faults, input constraints, linear matrix inequalities

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Authors: Oyindamola Kayode, Sarah George, Roberto Borrageiro, Mike Shirran

Abstract:

A phenomenon identified by our industrial partner has experienced sag on AA3104 can body stock (CBS) transfer bar during transportation of the slab from the breakdown mill to the finishing mill. Excessive sag results in bottom scuffing of the slab onto the roller table, resulting in surface defects on the final product. It has been found that increasing the strain rate on the breakdown mill final pass results in a slab resistant to sag. The creep response for materials hot deformed at different Zener–Holloman parameter values needs to be evaluated experimentally to gain better understanding of the operating mechanism. This study investigates this identified phenomenon through laboratory simulation of the breakdown mill conditions for various strain rates by utilizing the Gleeble at UCT Centre for Materials Engineering. The experiment will determine the creep response for a range of conditions as well as quantifying the associated material microstructure (sub-grain size, grain structure etc). The experimental matrices were determined based on experimental conditions approximate to industrial hot breakdown rolling and carried out on the Gleeble 3800 at the Centre for Materials Engineering, University of Cape Town. Plane strain compression samples were used for this series of tests at an applied load that allow for better contact and exaggerated creep displacement. A tantalum barrier layer was used for increased conductivity and decreased risk of anvil welding. One set of tests with no in-situ hold time was performed, where the samples were quenched after deformation. The samples were retained for microstructure analysis of the micrographs from the light microscopy (LM), quantitative data and images from scanning electron microscopy (SEM) and energy dispersive X-ray (EDX), sub-grain size and grain structure from electron back scattered diffraction (EBSD).

Keywords: aluminium alloy, can-body stock, hot rolling, creep response, Zener-Hollomon parameter

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Authors: Andreas Aceranti, Guido Bighiani, Francesca Crotto, Marco Colorato, Stefania Zaghi, Marino Zanetti, Simonetta Vernocchi

Abstract:

The management of eye parameters such as convergence, accommodation, and miosis is very complex; in fact, both the neurovegetative system and the complex Oculocephalgiria system come into play. We have found the effectiveness of the "highvelocity low amplitude" technique directed on C7-T1 (where the cilio-spinal nucleus of the budge is located) in improving the convergence parameter through the measurement of the point of maximum convergence. With this research, we set out to investigate whether the improvement obtained through the High Velocity Low Amplitude maneuver lasts over time, carrying out a pre-manipulation measurement, one immediately after manipulation and one month after manipulation. We took a population of 30 subjects with both refractive and non-refractive problems. Of the 30 patients tested, 27 gave a positive result after the High Velocity Low Amplitude maneuver, giving an improvement in the point of maximum convergence. After a month, we retested all 27 subjects: some further improved the result, others kept, and three subjects slightly lost the gain obtained. None of the re-tested patients returned to the point of maximum convergence starting pre-manipulation. This result opens the door to a multidisciplinary approach between ophthalmologists and osteopaths with the aim of addressing oculomotricity and convergence deficits that increasingly afflict our society due to the massive use of devices and for the conduct of life in closed and restricted environments.

Keywords: point of maximum convergence, HVLA, improvement in PPC, convergence

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Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

Abstract:

In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: artificial neural network, Taguchi method, real estate valuation model, investors

Procedia PDF Downloads 460