Search results for: Markov chain Monte Carlo.

Authors: Ghobad Gorji, Hasan Golabi

Abstract:

The goal of this paper is to describe the design of an ultra-wideband (UWB) system that is optimized for the low-rate wireless personal area network application. To this aim, we propose a system based on direct chaotic communication (DCC) technology. Based on this system, a 2-GHz wide chaotic signal is produced into the UWB spectrum lower band, i.e., 3.1–5.1 GHz. For this system, two simple modulation schemes, namely chaotic on-off keying (COOK) and differential chaos shift keying  (DCSK) are evaluated first. We propose a modulation scheme, namely Double DCSK, to improve the performance of UWB DCC. Different characteristics of these systems, with Monte Carlo simulations based on the Additive White Gaussian Noise (AWGN) and the IEEE 802.15.4a standard channel models, are compared.

Keywords: Ultra-wideband, UWB, Direct Chaotic Communication, DCC, IEEE 802.15.4a, COOK, DCSK.

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Authors: P. Puustinen, A. Kanto

Abstract:

This paper proposes a new procedure for analyzing means-end chain data in marketing research. Most commonly the collected data is summarized in the Hierarchical Value Map (HVM) illustrating the main attribute-consequence-value linkages. This paper argues that traditionally constructed HVM may give an erroneous impression of the results of a means-end study. To justify the arguments, an alternative procedure to (1) determine the dominant attribute-consequence-value linkages and (2) construct HVM in a precise manner is presented. The current approach makes a contribution to means-end analysis, allowing marketers to address a set of marketing problems, such as advertising strategy.

Keywords: Means-end chain analysis, Laddering, Hierarchical Value Map.

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Authors: R. Sharma, M. Gopal

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This paper presents a new problem solving approach that is able to generate optimal policy solution for finite-state stochastic sequential decision-making problems with high data efficiency. The proposed algorithm iteratively builds and improves an approximate Markov Decision Process (MDP) model along with cost-to-go value approximates by generating finite length trajectories through the state-space. The approach creates a synergy between an approximate evolving model and approximate cost-to-go values to produce a sequence of improving policies finally converging to the optimal policy through an intelligent and structured search of the policy space. The approach modifies the policy update step of the policy iteration so as to result in a speedy and stable convergence to the optimal policy. We apply the algorithm to a non-holonomic mobile robot control problem and compare its performance with other Reinforcement Learning (RL) approaches, e.g., a) Q-learning, b) Watkins Q(λ), c) SARSA(λ).

Keywords: Markov Decision Process (MDP), Mobile robot, Policy iteration, Simulation.

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Authors: Ahmad Aljaafreh

Abstract:

This paper proposes a hierarchical hidden Markov model (HHMM) to model the detection of M vehicles in a wireless sensor network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications.This paper integrates several techniques to optimize the detection performance. The output of the states of the first HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Due to the statistical advantages of multisensor data fusion, we propose a heuristic based on fuzzy weighted majority voting to enhance cooperative classification of moving vehicles within a region that is monitored by a wireless sensor network. A fuzzy inference system weighs each local decision based on the signal to noise ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized as well as the temporal correlation. Simulation results demonstrate the efficiency of this scheme.

Keywords: Classification, decision fusion, fuzzy logic, hidden Markov model

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Authors: Marco Bötel, Tobias Gelau, Wendelin Gross

Abstract:

Supply chain networks are frequently hit by unplanned events which lead to disruptions and cause operational and financial consequences. It is neither possible to avoid disruption risk entirely, nor are network members able to prepare for every possible disruptive event. Therefore a continuity planning should be set up which supports effective operational responses in supply chain networks in times of emergencies. In this research network related degrees of freedom which determine the options for responsive actions are derived from interview data. The findings are further embedded into a common risk management process. The paper provides support for researchers and practitioners to identify the network related options for responsive actions and to determine the need for improving the reaction capabilities.

Keywords: Supply Chain Risk Management, Business Continuity Planning, Degrees of Freedom, Risk Management Process, Mitigation Measures.

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Authors: Jamal Laaouine, Mohammed Elhassani Charkani

Abstract:

Let p be a prime and let b be an integer. MDS b-symbol codes are a direct generalization of MDS codes. The γ-constacyclic codes of length pˢ over the finite commutative chain ring Fₚm [u]/ < u² > had been classified into four distinct types, where is a nonzero element of the field Fₚm. Let C₃ be a code of Type 3. In this paper, we obtain the b-symbol distance db(C₃) of the code C₃. Using this result, necessary and sufficient conditions under which C₃ is an MDS b-symbol code are given.

Keywords: constacyclic code, repeated-root code, maximum distance separable, MDS codes, b-symbol distance, finite chain rings

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Authors: Siva Prasad Darla, C. D. Naiju, K. Annamalai, S. S. Rajiv Sushanth

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Supply chain consists of all stages involved, directly or indirectly, includes all functions involved in fulfilling a customer demand. In two stage transportation supply chain problem, transportation costs are of a significant proportion of final product costs. It is often crucial for successful decisions making approaches in two stage supply chain to explicit account for non-linear transportation costs. In this paper, deterministic demand and finite supply of products was considered. The optimized distribution level and the routing structure from the manufacturing plants to the distribution centres and to the end customers is determined using developed mathematical model and solved by proposed particle swarm optimization based genetic algorithm. Numerical analysis of the case study is carried out to validate the model.

Keywords: Genetic Algorithm, Particle Swarm Optimization, Production, Remanufacturing

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Authors: Chun-Der Chen, Yi-Wen Fan, Cheng-Kiang Farn

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In today-s competitive global business environment, the concept of supply chain management (SCM) continues to become increasingly market-oriented, shifting the primary driver of the value chain from supply to demand. Recent recommendations encourage researchers to focus investigations on the supply chain process integration (SCPI) capabilities that integrate a focal firm with its network of suppliers and business customers to create value for it. However, theoretical and empirical researches pertaining to the antecedents and consequences of a focal firm-s SCPI capabilities have been limited and piecemeal. The purpose of this study is to investigate the critical determinants and consequences of a focal firm-s SCPI capabilities. We test our proposed research framework using a sample of 139 sales managers of manufacturing industries in Taiwan, our research findings show that (1) both perceived business customer-s power and focal firm-s market-oriented culture positively influences a focal firm-s SCPI capabilities, and (2) SCPI capabilities positively influence a focal firm-s SCM performance, both operational and strategic benefits. Implications for practitioners and researchers and suggestions for future research are also addressed in this study.

Keywords: Supply chain process integration capabilities, Perceived business customer's power, Market-oriented culture, Supply chain management performance.

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Authors: P. C. Tewari, Parveen Kumar

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The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: Markov process, milling system, availability modeling, rice milling plant.

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Authors: Dewi Nasien, Habibollah Haron, Siti SophiayatiYuhaniz

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This paper presents a comparison of metaheuristic algorithms, Genetic Algorithm (GA) and Ant Colony Optimization (ACO), in producing freeman chain code (FCC). The main problem in representing characters using FCC is the length of the FCC depends on the starting points. Isolated characters, especially the upper-case characters, usually have branches that make the traversing process difficult. The study in FCC construction using one continuous route has not been widely explored. This is our motivation to use the population-based metaheuristics. The experimental result shows that the route length using GA is better than ACO, however, ACO is better in computation time than GA.

Keywords: Handwriting Recognition, Feature Extraction, Freeman Chain Code, Genetic Algorithm and Ant ColonyOptimization.

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Authors: Mohsen Shirani, Shadi Asadzandi, Micaela Demichela

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This paper presents the results of a study to assess crucial aspects and the strength of the scientific basis of a typically interdisciplinary, applied field: food supply chain risk assessment research. Our approach is based on an advanced scientometrics analysis that is a quantitative study of the disciplines of science based on published literature to measure interdisciplinary. This paper aims to describe the quantity and quality of the publication trends in food supply chain risk assessment. The publication under study was composed of 266 articles from database web of science. The results were analyzed based on date of publication, type of document, language of the documents, source of publications, subject areas, authors and their affiliations, and the countries involved in developing the articles.

Keywords: Food Supply Chain, Risk Assessment, Scientometrics, Web of science.

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Authors: Chaimongkol Limpianchob

Abstract:

This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case.

Keywords: Aromatic coconut, supply chain management, production planning, mixed-integer linear programming.

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Authors: Bassam Istanbouli

Abstract:

Organizations are living in a very competitive and dynamic environment which is constantly changing. In order to achieve a high level of service, the products and processes of these organizations need to be flexible and evolvable. If the supply chains are not modular and well designed, changes can bring combinatorial effects to most areas of a company from its management, financial, documentation, logistics and its information structure. Applying the normalized system’s concept to segments of the supply chain may help in reducing those ripple effects, but it may also increase lead times. Lead times are important and can become a decisive element in gaining customers. Industries are always under the pressure in providing good quality products, at competitive prices, when and how the customer wants them. Most of the time, the customers want their orders now, if not yesterday. The above concept will be proven by examining lead times in a manufacturing example before and after applying normalized systems concept to that segment of the chain. We will then show that although we can minimize the combinatorial effects when changes occur, the lead times will be increased.

Keywords: Supply chain, lead time, normalization, modular.

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Authors: Nirjhar Roy, V. R. Komma, Jitendra Kumar

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In the supply chain management customer is the most significant component and mass customization is mostly related to customers because it is the capability of any industry or organization to deliver highly customized products and its services to the respective customers with flexibility and integration, providing such a variety of products that nearly everyone can find what they want. Today all over the world many companies and markets are facing varied situations that at one side customers are demanding that their orders should be completed as quickly as possible while on other hand it requires highly customized products and services. By applying mass customization some companies face unwanted cost and complexity. Now they are realizing that they should completely examine what kind of customization would be best suited for their companies. In this paper authors review some approaches and principles which show effect in supply chain management that can be adopted and used by companies for quickly meeting the customer orders at reduced cost, with minimum amount of inventory and maximum efficiency.

Keywords: Mass customization and supply chain management.

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Authors: Sana Bembli, Nahla Khraief Haddad, Safya Belghith

Abstract:

This paper deals with a robust model free terminal sliding mode with gravity compensation approach used to control an exoskeleton-upper limb system. The considered system is a 2-DoF robot in interaction with an upper limb used for rehabilitation. The aim of this paper is to control the flexion/extension movement of the shoulder and the elbow joints in presence of matched disturbances. In the first part, we present the exoskeleton-upper limb system modeling. Then, we controlled the considered system by the model free terminal sliding mode with gravity compensation. A stability study is realized. To prove the controller performance, a robustness analysis was needed. Simulation results are provided to confirm the robustness of the gravity compensation combined with to the Model free terminal sliding mode in presence of uncertainties.

Keywords: Exoskeleton-upper limb system, gravity compensation, model free terminal sliding mode, robustness analysis, Monte Carlo, H∞ methods.

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Authors: Musa'ed N. Almarshad, Saleh A. Alshebeili, Mourad Barkat

Abstract:

A challenging problem in radar signal processing is to achieve reliable target detection in the presence of interferences. In this paper, we propose a novel algorithm for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithm, termed the forward automatic censored cell averaging detector (F-ACCAD), consists of two steps: removing the corrupted reference cells (censoring) and the actual detection. Both steps are performed dynamically by using a suitable set of ranked cells to estimate the unknown background level and set the adaptive thresholds accordingly. The F-ACCAD algorithm does not require any prior information about the clutter parameters nor does it require the number of interfering targets. The effectiveness of the F-ACCAD algorithm is assessed by computing, using Monte Carlo simulations, the probability of censoring and the probability of detection in different background environments.

Keywords: CFAR, Log-normal clutter, Censoring, Probabilityof detection, Probability of false alarm, Probability of falsecensoring.

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Authors: Randa Ibrahim Elanwar, Mohsen Rashwan, Samia Mashali

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In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentation-recognition using HMMs of unique design trained using online features most of which are novel. The HMM output characters boundaries represent the proposed segmentation points (PSP) which are then validated by rules-based post stage without any contextual information help to solve different segmentation errors. The HMM has been designed and tested using a self collected dataset (OHASD) [1]. Most errors cases are cured and remarkable segmentation enhancement is achieved. Very promising word and character segmentation rates are obtained regarding the unconstrained Arabic handwriting difficulty and not using context help.

Keywords: Arabic, Hidden Markov Models, online handwriting, word segmentation

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Authors: Yohei Saika, Hiroki Sakaematsu, Shota Akiyama

Abstract:

We investigated statistical performance of Bayesian inference using maximum entropy and MAP estimation for several models which approximated wave-fronts in remote sensing using SAR interferometry. Using Monte Carlo simulation for a set of wave-fronts generated by assumed true prior, we found that the method of maximum entropy realized the optimal performance around the Bayes-optimal conditions by using model of the true prior and the likelihood representing optical measurement due to the interferometer. Also, we found that the MAP estimation regarded as a deterministic limit of maximum entropy almost achieved the same performance as the Bayes-optimal solution for the set of wave-fronts. Then, we clarified that the MAP estimation perfectly carried out phase unwrapping without using prior information, and also that the MAP estimation realized accurate phase unwrapping using conjugate gradient (CG) method, if we assumed the model of the true prior appropriately.

Keywords: Bayesian inference using maximum entropy, MAP estimation using conjugate gradient method, SAR interferometry.

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Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha

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This paper investigates the performance of a speech recognizer in an interactive voice response system for various coded speech signals, coded by using a vector quantization technique namely Multi Switched Split Vector Quantization Technique. The process of recognizing the coded output can be used in Voice banking application. The recognition technique used for the recognition of the coded speech signals is the Hidden Markov Model technique. The spectral distortion performance, computational complexity, and memory requirements of Multi Switched Split Vector Quantization Technique and the performance of the speech recognizer at various bit rates have been computed. From results it is found that the speech recognizer is showing better performance at 24 bits/frame and it is found that the percentage of recognition is being varied from 100% to 93.33% for various bit rates.

Keywords: Linear predictive coding, Speech Recognition, Voice banking, Multi Switched Split Vector Quantization, Hidden Markov Model, Linear Predictive Coefficients.

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Authors: Brandtner Patrick, Staberhofer Franz

Abstract:

Logistics and Supply Chain Management are of crucial importance for organisational success. In the era of Digitalization, several implications and improvement potentials for these domains arise, which at the same time could lead to decreased competitiveness and could endanger long-term company success if ignored or neglected. However, empirical research on the issue of Digitalization and benefits purported to it by practitioners is scarce and mainly focused on single technologies or separate, isolated Supply Chain blocks as e.g. distribution logistics or procurement only. The current paper applies a holistic focus group approach to elaborate practitioner use cases at the nexus of the concepts of Supply Chain Management (SCM) and Digitalization. In the course of three focus group workshops with over 45 participants from more than 20 organisations, a comprehensive set of benefit entitlements and areas for improvement in terms of applying digitalization to SCM is developed. The main results of the paper indicate the relevance of Digitalization being realized in practice. In the form of seventeen concrete research action fields, the benefit entitlements are aggregated and transformed into potential starting points for future research projects in this area. The main contribution of this paper is an empirically grounded basis for future research projects and an overview of actual research action fields from practitioners’ point of view.

Keywords: Digital transformation, supply chain management, digital supply chain, value networks.

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Authors: Yohei Saika, Yuji Haraguchi

Abstract:

We constructed a method of noise reduction for JPEG-compressed image based on Bayesian inference using the maximizer of the posterior marginal (MPM) estimate. In this method, we tried the MPM estimate using two kinds of likelihood, both of which enhance grayscale images converted into the JPEG-compressed image through the lossy JPEG image compression. One is the deterministic model of the likelihood and the other is the probabilistic one expressed by the Gaussian distribution. Then, using the Monte Carlo simulation for grayscale images, such as the 256-grayscale standard image “Lena" with 256 × 256 pixels, we examined the performance of the MPM estimate based on the performance measure using the mean square error. We clarified that the MPM estimate via the Gaussian probabilistic model of the likelihood is effective for reducing noises, such as the blocking artifacts and the mosquito noise, if we set parameters appropriately. On the other hand, we found that the MPM estimate via the deterministic model of the likelihood is not effective for noise reduction due to the low acceptance ratio of the Metropolis algorithm.

Keywords: Noise reduction, JPEG-compressed image, Bayesian inference, the maximizer of the posterior marginal estimate

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Authors: Z. Sakka, I. Assakkaf, T. Al-Yaqoub, J. Parol

Abstract:

reliability-based methodology for the assessment and evaluation of reinforced concrete (R/C) structural elements of concrete structures is presented herein. The results of the reliability analysis and assessment for R/C structural elements were verified by the results obtained through deterministic methods. The outcomes of the reliability-based analysis were compared against currently adopted safety limits that are incorporated in the reliability indices β’s, according to international standards and codes. The methodology is based on probabilistic analysis using reliability concepts and statistics of the main random variables that are relevant to the subject matter, and for which they are to be used in the performance-function equation(s) associated with the structural elements under study. These methodology techniques can result in reliability index β, which is commonly known as the reliability index or reliability measure value that can be utilized to assess and evaluate the safety, human risk, and functionality of the structural component. Also, these methods can result in revised partial safety factor values for certain target reliability indices that can be used for the purpose of redesigning the R/C elements of the building and in which they could assist in considering some other remedial actions to improve the safety and functionality of the member.

Keywords: Concrete Structures, FORM, Monte Carlo Simulation, Structural Reliability.

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Authors: P. C. Tewari, Rajiv Kumar, Dinesh Khanduja

Abstract:

This paper discusses the performance modeling and availability analysis of Yarn Dyeing System of a Textile Industry. The Textile Industry is a complex and repairable engineering system. Yarn Dyeing System of Textile Industry consists of five subsystems arranged in series configuration. For performance modeling and analysis of availability, a performance evaluating model has been developed with the help of mathematical formulation based on Markov-Birth-Death Process. The differential equations have been developed on the basis of Probabilistic Approach using a Transition Diagram. These equations have further been solved using normalizing condition in order to develop the steady state availability, a performance measure of the system concerned. The system performance has been further analyzed with the help of decision matrices. These matrices provide various availability levels for different combinations of failure and repair rates for various subsystems. The findings of this paper are therefore, considered to be useful for the analysis of availability and determination of the best possible maintenance strategies which can be implemented in future to enhance the system performance.

Keywords: Availability Analysis, Markov Process, Performance Modeling, Steady State Availability.

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Authors: Abderrahmane Amrouche, Jean Michel Rouvaen

Abstract:

In this paper we present an efficient system for independent speaker speech recognition based on neural network approach. The proposed architecture comprises two phases: a preprocessing phase which consists in segmental normalization and features extraction and a classification phase which uses neural networks based on nonparametric density estimation namely the general regression neural network (GRNN). The relative performances of the proposed model are compared to the similar recognition systems based on the Multilayer Perceptron (MLP), the Recurrent Neural Network (RNN) and the well known Discrete Hidden Markov Model (HMM-VQ) that we have achieved also. Experimental results obtained with Arabic digits have shown that the use of nonparametric density estimation with an appropriate smoothing factor (spread) improves the generalization power of the neural network. The word error rate (WER) is reduced significantly over the baseline HMM method. GRNN computation is a successful alternative to the other neural network and DHMM.

Keywords: Speech Recognition, General Regression NeuralNetwork, Hidden Markov Model, Recurrent Neural Network, ArabicDigits.

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Authors: Leila Jafari, Viliam Makis, Akram Khaleghei G.B.

Abstract:

In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1 which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed, illustrated by a numerical example.

Keywords: Condition-Based Maintenance, Semi-Markov Decision Process, Multivariate Bayesian Control Chart, Partially Observable System, Two-unit System.

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Authors: A.M. Al-Fahed Nuseirat, R. Abu-Zitar

Abstract:

In this paper the design of maximally flat linear phase finite impulse response (FIR) filters is considered. The problem is handled with totally two different approaches. The first one is completely deterministic numerical approach where the problem is formulated as a Linear Complementarity Problem (LCP). The other one is based on a combination of Markov Random Fields (MRF's) approach with messy genetic algorithm (MGA). Markov Random Fields (MRFs) are a class of probabilistic models that have been applied for many years to the analysis of visual patterns or textures. Our objective is to establish MRFs as an interesting approach to modeling messy genetic algorithms. We establish a theoretical result that every genetic algorithm problem can be characterized in terms of a MRF model. This allows us to construct an explicit probabilistic model of the MGA fitness function and introduce the Ising MGA. Experimentations done with Ising MGA are less costly than those done with standard MGA since much less computations are involved. The least computations of all is for the LCP. Results of the LCP, random search, random seeded search, MGA, and Ising MGA are discussed.

Keywords: Filter design, FIR digital filters, LCP, Ising model, MGA, Ising MGA.

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Authors: Hamed Afrasiab, Saeed Khodaygan

Abstract:

Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Keywords: Process capability, machining error, dimensional and geometrical tolerances, uncertainty analysis.

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Authors: K. Bommanna Raja, M.Madheswaran, K.Thyagarajah

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A higher order spline interpolated contour obtained with up-sampling of homogenously distributed coordinates for segmentation of kidney region in different classes of ultrasound kidney images has been developed and presented in this paper. The performance of the proposed method is measured and compared with modified snake model contour, Markov random field contour and expert outlined contour. The validation of the method is made in correspondence with expert outlined contour using maximum coordinate distance, Hausdorff distance and mean radial distance metrics. The results obtained reveal that proposed scheme provides optimum contour that agrees well with expert outlined contour. Moreover this technique helps to preserve the pixels-of-interest which in specific defines the functional characteristic of kidney. This explores various possibilities in implementing computer-aided diagnosis system exclusively for US kidney images.

Keywords: Ultrasound Kidney Image – Kidney Segmentation –Active Contour – Markov Random Field – Higher Order SplineInterpolation

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Authors: H. Erbiyik, A. Bal, M. Sirakaya, Ö. Yesildal, E. Yolcu

Abstract:

The emergence of many environmental issues began with the Industrial Revolution. The depletion of natural resources and emerging environmental challenges over time requires enterprises and managers to take into consideration environmental factors while managing business. If we take notice of these causes; the design and implementation of environmentally friendly green purchasing, production and waste management systems become very important at green logistics systems. Companies can adopt green supply chain with the awareness of these facts. The concept of green supply chain constitutes from green purchasing, green production, green logistics, waste management and reverse logistics. In this study, we wanted to identify the concept of green supply chain and why green supply chain should be applied. In the practice part of the study an analytic hierarchy process (AHP) study is conducted on an aluminum production company to evaluate suppliers.

Keywords: Aluminum sector, analytic hierarchy process, decision making, green logistics.

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Authors: Gee-Sern Jison Hsu

Abstract:

A fusion classifier composed of two modules, one made by a hidden Markov model (HMM) and the other by a support vector machine (SVM), is proposed to recognize faces with pose variations in open-set recognition settings. The HMM module captures the evolution of facial features across a subject-s face using the subject-s facial images only, without referencing to the faces of others. Because of the captured evolutionary process of facial features, the HMM module retains certain robustness against pose variations, yielding low false rejection rates (FRR) for recognizing faces across poses. This is, however, on the price of poor false acceptance rates (FAR) when recognizing other faces because it is built upon withinclass samples only. The SVM module in the proposed model is developed following a special design able to substantially diminish the FAR and further lower down the FRR. The proposed fusion classifier has been evaluated in performance using the CMU PIE database, and proven effective for open-set face recognition with pose variations. Experiments have also shown that it outperforms the face classifier made by HMM or SVM alone.

Keywords: Face recognition, open-set identification, hidden Markov model, support vector machines.

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