Search results for: back propagation algorithm

Authors: Qianhua He, Weili Zhou, Aiwu Chen

Abstract:

A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.

Keywords: Speech denoising, sparse representation, K-singular value decomposition, orthogonal matching pursuit.

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Authors: T. Garlan, I. Gabelotaud, S. Lucas, E. Marchès

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Production of a global sedimentological seabed map has been initiated in 1995 to provide the necessary tool for searches of aircraft and boats lost at sea, to give sedimentary information for nautical charts, and to provide input data for acoustic propagation modelling. This original approach had already been initiated one century ago when the French hydrographic service and the University of Nancy had produced maps of the distribution of marine sediments of the French coasts and then sediment maps of the continental shelves of Europe and North America. The current map of the sediment of oceans presented was initiated with a UNESCO's general map of the deep ocean floor. This map was adapted using a unique sediment classification to present all types of sediments: from beaches to the deep seabed and from glacial deposits to tropical sediments. In order to allow good visualization and to be adapted to the different applications, only the granularity of sediments is represented. The published seabed maps are studied, if they present an interest, the nature of the seabed is extracted from them, the sediment classification is transcribed and the resulted map is integrated in the world map. Data come also from interpretations of Multibeam Echo Sounder (MES) imagery of large hydrographic surveys of deep-ocean. These allow a very high-quality mapping of areas that until then were represented as homogeneous. The third and principal source of data comes from the integration of regional maps produced specifically for this project. These regional maps are carried out using all the bathymetric and sedimentary data of a region. This step makes it possible to produce a regional synthesis map, with the realization of generalizations in the case of over-precise data. 86 regional maps of the Atlantic Ocean, the Mediterranean Sea, and the Indian Ocean have been produced and integrated into the world sedimentary map. This work is permanent and permits a digital version every two years, with the integration of some new maps. This article describes the choices made in terms of sediment classification, the scale of source data and the zonation of the variability of the quality. This map is the final step in a system comprising the Shom Sedimentary Database, enriched by more than one million punctual and surface items of data, and four series of coastal seabed maps at 1:10,000, 1:50,000, 1:200,000 and 1:1,000,000. This step by step approach makes it possible to take into account the progresses in knowledge made in the field of seabed characterization during the last decades. Thus, the arrival of new classification systems for seafloor has improved the recent seabed maps, and the compilation of these new maps with those previously published allows a gradual enrichment of the world sedimentary map. But there is still a lot of work to enhance some regions, which are still based on data acquired more than half a century ago.

Keywords: Marine sedimentology, seabed map, sediment classification, World Ocean.

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Authors: Nisar Ahmed Memon, Anwar Majid Mirza, S.A.M. Gilani

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Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT generally first segment the area of interest (lung) and then analyze the separately obtained area for nodule detection in order to diagnosis the disease. For normal lung, segmentation can be performed by making use of excellent contrast between air and surrounding tissues. However this approach fails when lung is affected by high density pathology. Dense pathologies are present in approximately a fifth of clinical scans, and for computer analysis such as detection and quantification of abnormal areas it is vital that the entire and perfectly lung part of the image is provided and no part, as present in the original image be eradicated. In this paper we have proposed a lung segmentation technique which accurately segment the lung parenchyma from lung CT Scan images. The algorithm was tested against the 25 datasets of different patients received from Ackron Univeristy, USA and AGA Khan Medical University, Karachi, Pakistan.

Keywords: Computer Aided Diagnosis, Medical ImageProcessing, Region Growing, Segmentation, Thresholding,

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Authors: S. Cagin, X. Fischer, E. Delacourt, N. Bourabaa, C. Morin, D. Coutellier, B. Carré, S. Loumé

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The complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).

Keywords: Diesel engine, Design optimization, Model reduction, Neural network, NTF algorithm, Scavenging.

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Authors: Siliang Wang, Minghui Wang, Jun Hu

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A novel path planning approach is presented to solve optimal path in stochastic, time-varying networks under priori traffic information. Most existing studies make use of dynamic programming to find optimal path. However, those methods are proved to be unable to obtain global optimal value, moreover, how to design efficient algorithms is also another challenge. This paper employs a decision theoretic framework for defining optimal path: for a given source S and destination D in urban transit network, we seek an S - D path of lowest expected travel time where its link travel times are discrete random variables. To solve deficiency caused by the methods of dynamic programming, such as curse of dimensionality and violation of optimal principle, an integer programming model is built to realize assignment of discrete travel time variables to arcs. Simultaneously, pruning techniques are also applied to reduce computation complexity in the algorithm. The final experiments show the feasibility of the novel approach.

Keywords: pruning method, stochastic, time-varying networks, optimal path planning.

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Authors: A. Nuchitprasittichai, N. Lerdritsirikoon, T. Khamsing

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Simulation modeling can be used to solve real world problems. It provides an understanding of a complex system. To develop a simplified model of process simulation, a suitable experimental design is required to be able to capture surface characteristics. This paper presents the experimental design and algorithm used to model the process simulation for optimization problem. The CO₂ liquefaction based on external refrigeration with two refrigeration circuits was used as a simulation case study. Latin Hypercube Sampling (LHS) was purposed to combine with existing Central Composite Design (CCD) samples to improve the performance of CCD in generating the second order model of the system. The second order model was then used as the objective function of the optimization problem. The results showed that adding LHS samples to CCD samples can help capture surface curvature characteristics. Suitable number of LHS sample points should be considered in order to get an accurate nonlinear model with minimum number of simulation experiments.

Keywords: Central composite design, CO2 liquefaction, Latin Hypercube Sampling, simulation – based optimization.

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Authors: Zohreh Orshesh

Abstract:

In this study, a 3D combustion chamber was simulated using FLUENT 6.32. Aim to obtain detailed information on combustion characteristics and _ nitrogen oxides in the furnace and the effect of oxygen enrichment in a combustion process. Oxygenenriched combustion is an effective way to reduce emissions. This paper analyzes NO emission, including thermal NO and prompt NO. Flow rate ratio of air to fuel is varied as 1.3, 3.2 and 5.1 and the oxygen enriched flow rates are 28, 54 and 68 lit/min. The 3D Reynolds Averaged Navier Stokes (RANS) equations with standard k-ε turbulence model are solved together by Fluent 6.32 software. First order upwind scheme is used to model governing equations and the SIMPLE algorithm is used as pressure velocity coupling. Results show that for AF=1.3, increase the oxygen flow rate of oxygen reduction in NO emissions is Lance. Moreover, in a fixed oxygen enrichment condition, increasing the air to fuel ratio will increase the temperature peak, but not the NO emission rate. As a result, oxygen enrichment can reduce the NO emission at this kind of furnace in low air to fuel rates.

Keywords: Combustion chamber, Oxygen enrichment, Reynolds Averaged Navier- Stokes, NO emission

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Authors: Yu-Jen Chang, Yun Chen, Hei-Lam Wong

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The Economic Lot Scheduling Problem (ELSP) is a valuable mathematical model that can support decision-makers to make scheduling decisions. The basic period approach is effective for solving the ELSP. The assumption for applying the basic period approach is that a product must use its maximum production rate to be produced. However, a product can lower its production rate to reduce the average total cost when a facility has extra idle time. The past researches discussed how a product adjusts its production rate under the common cycle approach. To the best of our knowledge, no studies have addressed how a product lowers its production rate under the basic period approach. This research is the first paper to discuss this topic. The research develops a simple fixed rate approach that adjusts the production rate of a product under the basic period approach to solve the ELSP. Our numerical example shows our approach can find a better solution than the traditional basic period approach. Our mathematical model that applies the fixed rate approach under the basic period approach can serve as a reference for other related researches.

Keywords: Economic Lot, Basic Period, Genetic Algorithm, Fixed Rate.

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Authors: Emin Islam Tatlı, Dirk Stegemann, Stefan Lucks

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Encryption protects communication partners from disclosure of their secret messages but cannot prevent traffic analysis and the leakage of information about “who communicates with whom". In the presence of collaborating adversaries, this linkability of actions can danger anonymity. However, reliably providing anonymity is crucial in many applications. Especially in contextaware mobile business, where mobile users equipped with PDAs request and receive services from service providers, providing anonymous communication is mission-critical and challenging at the same time. Firstly, the limited performance of mobile devices does not allow for heavy use of expensive public-key operations which are commonly used in anonymity protocols. Moreover, the demands for security depend on the application (e.g., mobile dating vs. pizza delivery service), but different users (e.g., a celebrity vs. a normal person) may even require different security levels for the same application. Considering both hardware limitations of mobile devices and different sensitivity of users, we propose an anonymity framework that is dynamically configurable according to user and application preferences. Our framework is based on Chaum-s mixnet. We explain the proposed framework, its configuration parameters for the dynamic behavior and the algorithm to enforce dynamic anonymity.

Keywords: Anonymity, context-awareness, mix-net, mobile business, policy management

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Authors: Manpreet Singh, Parvinder Singh Sandhu, Manjinder Singh Kahlon

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There are many classical algorithms for finding routing in FPGA. But Using DNA computing we can solve the routes efficiently and fast. The run time complexity of DNA algorithms is much less than other classical algorithms which are used for solving routing in FPGA. The research in DNA computing is in a primary level. High information density of DNA molecules and massive parallelism involved in the DNA reactions make DNA computing a powerful tool. It has been proved by many research accomplishments that any procedure that can be programmed in a silicon computer can be realized as a DNA computing procedure. In this paper we have proposed two tier approaches for the FPGA routing solution. First, geometric FPGA detailed routing task is solved by transforming it into a Boolean satisfiability equation with the property that any assignment of input variables that satisfies the equation specifies a valid routing. Satisfying assignment for particular route will result in a valid routing and absence of a satisfying assignment implies that the layout is un-routable. In second step, DNA search algorithm is applied on this Boolean equation for solving routing alternatives utilizing the properties of DNA computation. The simulated results are satisfactory and give the indication of applicability of DNA computing for solving the FPGA Routing problem.

Keywords: FPGA, Routing, DNA Computing.

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: Concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC.

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Authors: S. Karthika, K. Velayutham, P. Rathika, D. Devaraj

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The electric power supplied by a photovoltaic power generation systems depends on the solar irradiation and temperature. The PV system can supply the maximum power to the load at a particular operating point which is generally called as maximum power point (MPP), at which the entire PV system operates with maximum efficiency and produces its maximum power. Hence, a Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. The proposed MPPT controller is designed for 10kW solar PV system installed at Cape Institute of Technology. This paper presents the fuzzy logic based MPPT algorithm. However, instead of one type of membership function, different structures of fuzzy membership functions are used in the FLC design. The proposed controller is combined with the system and the results are obtained for each membership functions in Matlab/Simulink environment. Simulation results are decided that which membership function is more suitable for this system.

Keywords: MPPT, DC-DC Converter, Fuzzy logic controller, Photovoltaic (PV) system.

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Authors: Zeynep Şahin, Züleyha Yılmaz, Fikret Çankaya

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In today's business world, in which it is difficult to survive, the economic life of products, services or knowledge is considerably reduced. Competitors produce similar products or extra-featured ones instantly. In this environment, the contribution of companies to the social and economic environment is a preferred criterion by consumers alongside products or services. Therefore, consumers need to obtain more detailed information about companies. Besides, this drastic change in the market encourages companies to become sustainable. Sustainable business means the company puts consumed products back. Corporate sustainability, corresponds to sustainability at the level of the company, and gives equal importance to company growth and profitability together with environmental and social issues. The BIST Sustainability Index started to be calculated by the Istanbul Stock Exchange (BIST) in 2014 to evaluate the sustainability performance of companies in Turkey. The main objective of this study is to present the importance of sustainability reports in Turkey. To this aim, the performances of 15 companies in the BIST Sustainability Index were compared the periods before and after entering the index. On the other hand, sustainability reporting practices should be encouraged to increase studies on this issue. In this context, to remain on the agenda of the issue is a further objective of this study. To achieve these objectives, the financial data of the companies in the period before and after entering to the BIST Sustainability Index were analyzed using t-test in Statistical Package for the Social Sciences (SPSS) package. The results of the study showed that no significant difference between the performances of the companies in terms of the net profit margin, the return on assets and equity capital in these periods could be found. Therefore, it can be said that insufficient importance is given to sustainability issues in Turkey. The reasons for this situation might be considered as a lack of awareness due to the recent introduction and calculation of the index. It is expected that the awareness of firms and investors about sustainability will increase, and that they will demonstrate the necessary importance to this issue over time.

Keywords: BIST sustainability index, firm performance, sustainability, sustainability reporting.

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Authors: Ninlawat Phuangchoke, Waraporn Viyanon, Setta Sasananan

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The most important process of the water treatment plant process is coagulation, which uses alum and poly aluminum chloride (PACL). Therefore, determining the dosage of alum and PACL is the most important factor to be prescribed. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for chemical dose prediction, as used for coagulation, such as alum and PACL, with input data consisting of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of the Bangkhen Water Treatment Plant (BKWTP), under the authority of the Metropolitan Waterworks Authority of Thailand. The data were collected from 1 January 2019 to 31 December 2019 in order to cover the changing seasons of Thailand. The input data of ANN are divided into three groups: training set, test set, and validation set. The coefficient of determination and the mean absolute errors of the alum model are 0.73, 3.18 and the PACL model are 0.59, 3.21, respectively.

Keywords: Soft jar test, jar test, water treatment plant process, artificial neural network.

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Authors: Badr M. Alshammari, T. Guesmi

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In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO₂, NO_x, and SO₂. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.

Keywords: Power dispatch, valve point loading effects, multiobjective optimization, Pareto solutions.

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Authors: Mohammad Amin Safi, Mahmud Ashrafizaadeh, Amir Ali Ashrafizaadeh

Abstract:

A highly optimized implementation of binary mixture diffusion with no initial bulk velocity on graphics processors is presented. The lattice Boltzmann model is employed for simulating the binary diffusion of oxygen and nitrogen into each other with different initial concentration distributions. Simulations have been performed using the latest proposed lattice Boltzmann model that satisfies both the indifferentiability principle and the H-theorem for multi-component gas mixtures. Contemporary numerical optimization techniques such as memory alignment and increasing the multiprocessor occupancy are exploited along with some novel optimization strategies to enhance the computational performance on graphics processors using the C for CUDA programming language. Speedup of more than two orders of magnitude over single-core processors is achieved on a variety of Graphical Processing Unit (GPU) devices ranging from conventional graphics cards to advanced, high-end GPUs, while the numerical results are in excellent agreement with the available analytical and numerical data in the literature.

Keywords: Lattice Boltzmann model, Graphical processing unit, Binary mixture diffusion, 2D flow simulations, Optimized algorithm.

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Authors: Feras Al-Obeidat, Nabil Belacel, Juan A. Carretero, Prabhat Mahanti,

Abstract:

In this paper, a new learning algorithm based on a hybrid metaheuristic integrating Differential Evolution (DE) and Reduced Variable Neighborhood Search (RVNS) is introduced to train the classification method PROAFTN. To apply PROAFTN, values of several parameters need to be determined prior to classification. These parameters include boundaries of intervals and relative weights for each attribute. Based on these requirements, the hybrid approach, named DEPRO-RVNS, is presented in this study. In some cases, the major problem when applying DE to some classification problems was the premature convergence of some individuals to local optima. To eliminate this shortcoming and to improve the exploration and exploitation capabilities of DE, such individuals were set to iteratively re-explored using RVNS. Based on the generated results on both training and testing data, it is shown that the performance of PROAFTN is significantly improved. Furthermore, the experimental study shows that DEPRO-RVNS outperforms well-known machine learning classifiers in a variety of problems.

Keywords: Knowledge Discovery, Differential Evolution, Reduced Variable Neighborhood Search, Multiple criteria classification, PROAFTN, Supervised Learning.

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Authors: M. Nayeemuddin, T. Bramhananda Reddy, M. Vijaya Kumar

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Acoustic noise becoming ever more obnoxious radiated by voltage source inverter fed induction motor drive in modern and industrial applications. The drive utilized for industrial and modern applications should use “spread spectrum” innovation known as Random pulse width modulation (PWM) algorithms where acoustic noise emanates through the machine should be critically concerned. This paper illustrates three types of random PWM control algorithms with fixed switching frequency namely 1) Random modulating PWM 2) Random carrier PWM and 3) Random modulating-carrier PWM. The spectrum plots of the motor stator current demonstrate the strength and robustness of the proposed PWM algorithms. To affirm the proposed algorithms, experimental tests have been conducted using dSPACE rt1104 control board on a v/f control three phase induction motor drive fed by DC link cascaded multilevel inverter.

Keywords: Multilevel inverter, acoustic noise, CSVPWM, total harmonic distortion, random PWM algorithm.

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: Data fusion, Dempster-Shafer theory, data mining, event detection.

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Authors: Flur R. Ismagilov, Irek Kh. Khayrullin, Vyacheslav E. Vavilov, Ruslan D. Karimov, Anton S. Gorbunov, Danis R. Farrakhov

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The paper presents another structural scheme of high-temperature starter-generator with external rotor to be installed on High Pressure Shaft (HPS) of aircraft engines (AE) to implement More Electrical Engine concept. The basic materials to make this starter-generator (SG) were selected and justified. Multi-criteria optimization of the developed structural scheme was performed using a genetic algorithm and Pareto method. The optimum (in Pareto terms) active length and thickness of permanent magnets of SG were selected as a result of the optimization. Using the dimensions obtained, allowed to reduce the weight of the designed SG by 10 kg relative to a base option at constant thermal loads. Multidisciplinary computer simulation was performed on the basis of the optimum geometric dimensions, which proved performance efficiency of the design. We further plan to make a full-scale sample of SG of HPS and publish the results of its experimental research.

Keywords: High-temperature starter-generator, More electrical engine, multi-criteria optimization, permanent magnet.

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Authors: C Hisham, K Komal, Amit K Mishra

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Full search block matching algorithm is widely used for hardware implementation of motion estimators in video compression algorithms. In this paper we are proposing a new architecture, which consists of a 2D parallel processing unit and a 1D unit both working in parallel. The proposed architecture reduces both data access power and computational power which are the main causes of power consumption in integer motion estimation. It also completes the operations with nearly the same number of clock cycles as compared to a 2D systolic array architecture. In this work sum of absolute difference (SAD)-the most repeated operation in block matching, is calculated in two steps. The first step is to calculate the SAD for alternate rows by a 2D parallel unit. If the SAD calculated by the parallel unit is less than the stored minimum SAD, the SAD of the remaining rows is calculated by the 1D unit. Early termination, which stops avoidable computations has been achieved with the help of alternate rows method proposed in this paper and by finding a low initial SAD value based on motion vector prediction. Data reuse has been applied to the reference blocks in the same search area which significantly reduced the memory access.

Keywords: Sum of absolute difference, high speed DSP.

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Authors: Srilekha Mukherjee, Subha Ash, Goutam Sanyal

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Information security plays a major role in uplifting the standard of secured communications via global media. In this paper, we have suggested a technique of encryption followed by insertion before transmission. Here, we have implemented two different concepts to carry out the above-specified tasks. We have used a two-point crossover technique of the genetic algorithm to facilitate the encryption process. For each of the uniquely identified rows of pixels, different mathematical methodologies are applied for several conditions checking, in order to figure out all the parent pixels on which we perform the crossover operation. This is done by selecting two crossover points within the pixels thereby producing the newly encrypted child pixels, and hence the encrypted cover image. In the next lap, the first and second order derivative operators are evaluated to increase the security and robustness. The last lap further ensures reapplication of the crossover procedure to form the final stego-image. The complexity of this system as a whole is huge, thereby dissuading the third party interferences. Also, the embedding capacity is very high. Therefore, a larger amount of secret image information can be hidden. The imperceptible vision of the obtained stego-image clearly proves the proficiency of this approach.

Keywords: Steganography, Crossover, Differential Calculus, Peak Signal to Noise Ratio, Cross-correlation Coefficient.

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Authors: M. Moslehpour, S. Khorsandi

Abstract:

As transition to widespread use of IPv6 addresses has gained momentum, it has been shown to be vulnerable to certain security attacks such as those targeting Neighbor Discovery Protocol (NDP) which provides the address resolution functionality in IPv6. To protect this protocol, Secure Neighbor Discovery (SEND) is introduced. This protocol uses Cryptographically Generated Address (CGA) and asymmetric cryptography as a defense against threats on integrity and identity of NDP. Although SEND protects NDP against attacks, it is computationally intensive due to Hash2 condition in CGA. To improve the CGA computation speed, we parallelized CGA generation process and used the available resources in a trusted network. Furthermore, we focused on the influence of the existence of malicious nodes on the overall load of un-malicious ones in the network. According to the evaluation results, malicious nodes have adverse impacts on the average CGA generation time and on the average number of tries. We utilized a Trust Management that is capable of detecting and isolating the malicious node to remove possible incentives for malicious behavior. We have demonstrated the effectiveness of the Trust Management System in detecting the malicious nodes and hence improving the overall system performance.

Keywords: NDP, SEND, CGA, modifier, malicious node.

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Authors: Worawit Somha, Hiroyuki Yamauchi

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This paper proposes, for the first time, how the challenges facing the guard-band designs including the margin assist-circuits scheme for the screening-test in the coming process generations should be addressed. The increased screening error impacts are discussed based on the proposed statistical analysis models. It has been shown that the yield-loss caused by the misjudgment on the screening test would become 5-orders of magnitude larger than that for the conventional one when the amplitude of random telegraph noise (RTN) caused variations approaches to that of random dopant fluctuation. Three fitting methods to approximate the RTN caused complex Gamma mixtures distributions by the simple Gaussian mixtures model (GMM) are proposed and compared. It has been verified that the proposed methods can reduce the error of the fail-bit predictions by 4-orders of magnitude.

Keywords: Mixtures of Gaussian, Random telegraph noise, EM algorithm, Long-tail distribution, Fail-bit analysis, Static random access memory, Guard band design.

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Authors: Yansong Zhang, Xueyuan Li, Junjie Zhou, Xufeng Yin, Shihua Yuan, Shuxian Liu

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Aiming at the verification of control algorithms for skid-steering vehicles, a vehicle simulation model of 6×6 electric skid-steering unmanned vehicle was established based on Trucksim and Simulink. The original transmission and steering mechanism of Trucksim are removed, and the electric skid-steering model and a closed-loop controller for the vehicle speed and yaw rate are built in Simulink. The simulation results are compared with the ones got by theoretical formulas. The results show that the predicted tire mechanics and vehicle kinematics of Trucksim-Simulink simulation model are closed to the theoretical results. Therefore, it can be used as an effective approach to study the dynamic performance and control algorithm of skid-steering vehicle. In this paper, a method of motion control based on feed forward control is also designed. The simulation results show that the feed forward control strategy can make the vehicle follow the target yaw rate more quickly and accurately, which makes the vehicle have more maneuverability.

Keywords: Skid-steering, Trucksim-Simulink, feedforward control, dynamics.

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Authors: Rabia Korkmaz Tan, Şebnem Bora

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The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies.

Keywords: Parameter tuning, agent based modeling and simulation, metaheuristic algorithms, complex systems.

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Authors: Jesús A. Pérez, Sam Coppieters, Dimitri Debruyne

Abstract:

In the past decade, the use of digital image correlation (DIC) techniques has increased significantly in the area of experimental mechanics, especially for materials behavior characterization. This non-contact tool enables full field displacement and strain measurements over a complete region of interest. The DIC algorithm requires a random contrast pattern on the surface of the specimen in order to perform properly. To create this pattern, the specimen is usually first coated using a white matt paint. Next, a black random speckle pattern is applied using any suitable method. If the applied paint coating is too thick, its top surface may not be able to exactly follow the deformation of the specimen, and consequently, the strain measurement might be underestimated. In the present article, a study of the influence of the paint thickness on the strain underestimation is performed for different strain levels. The results are then compared to typical paint coating thicknesses applied by experienced DIC users. A slight strain underestimation was observed for paint coatings thicker than about 30μm. On the other hand, this value was found to be uncommonly high compared to coating thicknesses applied by DIC users.

Keywords: Digital Image Correlation, paint coating thickness, strain.

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Authors: Parvinder S. Sandhu, Hardeep Singh

Abstract:

Automatic reusability appraisal could be helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this paper, we have mentioned two-tier approach by studying the structural attributes as well as usability or relevancy of the component to a particular domain. Latent semantic analysis is used for the feature vector representation of various software domains. It exploits the fact that FeatureVector codes can be seen as documents containing terms -the idenifiers present in the components- and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. Further, we devised Neuro- Fuzzy hybrid Inference System, which takes structural metric values as input and calculates the reusability of the software component. Decision tree algorithm is used to decide initial set of fuzzy rules for the Neuro-fuzzy system. The results obtained are convincing enough to propose the system for economical identification and retrieval of reusable software components.

Keywords: Clustering, ID3, LSA, Neuro-fuzzy System, SVD

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Authors: Hazem M. El-Bakry, Ahmed A. Mohammed

Abstract:

In this paper, an intelligent algorithm for optimal document archiving is presented. It is kown that electronic archives are very important for information system management. Minimizing the size of the stored data in electronic archive is a main issue to reduce the physical storage area. Here, the effect of different types of Arabic fonts on electronic archives size is discussed. Simulation results show that PDF is the best file format for storage of the Arabic documents in electronic archive. Furthermore, fast information detection in a given PDF file is introduced. Such approach uses fast neural networks (FNNs) implemented in the frequency domain. The operation of these networks relies on performing cross correlation in the frequency domain rather than spatial one. It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations.

Keywords: Information Storage and Retrieval, Electronic Archiving, Fast Information Detection, Cross Correlation, Frequency Domain.

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Authors: Vishal Awasthi, Atul Kumar Agnihotri

Abstract:

Face is a non-intrusive strong biometrics for identification of original and dummy facial by different artificial means. Face recognition is extremely important in the contexts of computer vision, psychology, surveillance, pattern recognition, neural network, content based video processing. The availability of a widespread face database is crucial to test the performance of these face recognition algorithms. The openly available face databases include face images with a wide range of poses, illumination, gestures and face occlusions but there is no dummy face database accessible in public domain. This paper presents a face detection algorithm based on the image segmentation in terms of distance from a fixed point and template matching methods. This proposed work is having the most appropriate number of nodal points resulting in most appropriate outcomes in terms of face recognition and detection. The time taken to identify and extract distinctive facial features is improved in the range of 90 to 110 sec. with the increment of efficiency by 3%.

Keywords: Face recognition, Principal Component Analysis, PCA, Linear Discriminant Analysis, LDA, Improved Support Vector Machine, iSVM, elastic bunch mapping technique.

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