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A Image Thresholding Method Based on Binary Coded Ant Colony Algorithm

A Image Thresholding Method Based on Binary Coded Ant Colony...

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international workshop on Intelligent Systems and applications, ISA

作者： Zhiwei Ye Zhengbing Hu Huamin Wang Wei Liu Sch. of Comput. Sci. Hubei Univ. of Technol. Wuhan China Dept. of Inf. Technol. Huazhong Normal Univ. Wuhan China Sch. of Software Wuhan Univ. Wuhan China

Image segmentation is the most significant step in image analysis and is a long-term difficult problem, which hasn't been fully solved. Many segmentation methods have been brought forward to deal with image segmentation, among these methods thresholding is the simple and important method in image segmentation. In practical work, 2-dimension (2D) entropy method is often used. It segments images by using the gray value of the pixel and the local average gray value of it, and thus provides better results than that of one-dimension entropy. However, for more accurate thresholding, much more time has to pay. Thus, this paper employs a novel approach to 2D threshold selection based on binary coded ant colony optimization algorithm. The proposed approach has been implemented and tested on several real images. Experiments results indicate that proposed method performs well which is a good method to help select optimum 2D thresholds.

关键词： Image segmentation Entropy Pixel Ant colony optimization Computer science Image edge detection Image analysis Digital images Software algorithms Testing

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Application of Soft Computing in the Control of Hydroelectric Generating Unit

Application of Soft Computing in the Control of Hydroelectri...

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international workshop on Intelligent Systems and applications, ISA

作者： Shuqing Wang Zipeng Zhang Liqin Xue Hubei University of Technology Wuhan China Shandong University of Science and Technology Qingdao China

Hydroelectric generating unit system of water plant is a non-linear and complicated system. Conventional controller cannot get good controlling performance in control. In this study, an advanced soft computing technique based on fuzzy, neural network and genetic algorithm is used in the control of hydroelectric generating unit system of hydropower plant. Fuzzy reasoning system is used as controller and genetic algorithm is employed to optimize the parameters and rules of fuzzy controller in the design of controller or real-time control process. Dynamic identification model of control system based on RBF neural networks is designed to appraise the controlling performance of fuzzy controller. In the design, improved genetic algorithm is adopted based on general genetic algorithm and the character of control system. The improved genetic algorithm quickens optimizing speed and makes fuzzy controller acquire knowledge effectively. Simulation results show that the designed soft computing optimization control system can well control hydroelectric generating unit and its control performance is superior to conventional controller.

关键词： Computer applications Hydroelectric power generation Control systems Genetic algorithms Fuzzy control Fuzzy systems Control system synthesis Fuzzy neural networks Neural networks Fuzzy reasoning

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Personal tour planning incorporating standard tour routes and tourist satisfaction

Personal tour planning incorporating standard tour routes an...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Takashi Hasuike Hiroe Tsubaki Hideki Katagiri Hiroshi Tsuda Graduate School of Information Science and Technology Osaka University Suita Osaka Japan Graduate School of Engineering Hiroshima University Higashi Hiroshima Japan Department of Data Science Institute of Statistical Science Tachikawa Tokyo Japan Faculty of Science and Engineering Doshisha University Kyotanabe Kyoto Japan

This paper proposes a personal tour planning problem incorporating general tour routes and tourists satisfactions for sightseeing places based on some purposes of tourism. In order to construct a tour route, time-dependent satisfactions, traveling and activity duration times for sightseeing should be considered, but it is difficult to represent the time-dependent model using general static network models. Therefore, Time-Expanded Network (TEN) is introduced, which contains a copy to the set of nodes in the underlying static network for each discrete time step, and it turns the problem of determining an optimal flow over time into a classical static network flow problem. The proposed model is formulated as a bi-objective 0-1 integer programming problem maximizing the total satisfaction value and minimizing the difference from the best plan of standard tour routes. It also equivalently transformed into several existing tour planning problems using some natural assumptions and the concept of Pareto optimal solution, which can be applied existing useful combinatorial optimization and soft computing algorithms.

关键词： Planning Standards Programming Pareto optimization Algorithm design and analysis Educational institutions

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Motion segmentation and tracking optimization with edge relaxation in the cellular nonlinear network architecture

Motion segmentation and tracking optimization with edge rela...

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IEEE international workshop on Cellular Nanoscale Networks and their applications, (CNNA)

作者： L. Czuni T. Sziranyi Department of Image Processing and Neurocomputing University of Veszpém Veszprem Hungary Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary

We have developed a high-level set of cellular neural net (CNN) functions for finding the segment-borders of moving objects through spatio-temporal relaxation optimization. They are analogic algorithms based on simple CNN instructions considering their implementability in analogic VLSI chips. Motion information extraction from video series is very power consuming. Most computing effort is devoted to motion vector field estimation, object definition and boundary determination. Finding interrelations among small segments, obtained by oversegmentation, involves optimization through merging or separation. In our algorithm the process starts from an oversegmented image, then the segments are merged using information from spatial and temporal auxiliary data: motion fields and motion history. This grouping process is based on neighboring segment similarity in color, speed and time-depth. There is also a feedback to accept or refuse the cancellation of a segment-border. Our parallel approach is independent of the number of segments or objects. We use simple VLSI functions. We develop grouping by stochastic optimization. This relaxation-based motion segmentation can be a basic step of the effective coding of image-series and other automatic motion tracking systems. The proposed system is planned to implement in a cellular nonlinear network chip-set architecture.

关键词： Motion segmentation Computer vision Tracking Image segmentation Cellular neural networks Very large scale integration Neural networks Data mining Motion estimation Merging

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Advances in modeling and optimization techniques for microwave design

Advances in modeling and optimization techniques for microwa...

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IEEE MTT-S international Microwave workshop Series (IMWS)

作者： Qijun Zhang Carleton University Canada Tianjin University China

Summary form only given. Modeling and optimization of microwave and high-frequency/high-speed microelectronic devices and systems are important for achieving next generation information technology and communication systems. Increasing circuit size and design complexity, coupled with stringent design specifications and shorter design cycles, demand tools that are faster, more accurate and automated than possible today. Electromagnetic (EM)/physics-based design accuracies will be needed not only at the component level, but also at the circuit and system levels. Models with EM/physics-oriented accuracy but without the computational expense of direct EM/physics simulations are necessary. Recent applications of neural networks and space mapping for microwave design lead to new modeling and Computer-aided Design (CAD) with potential advantages in both speed and accuracy. applications are being made in modeling and design of microstrip and CPW circuits, multilayer interconnects, embedded passives, printed antennas, LTCC circuits, semiconductor devices, measurement standards, filters, amplifiers, mixers and so on. New CAD methods for optimization, statistical design, global modeling, and computational electromagnetics exploiting computational intelligence concepts are being developed. Advanced microwave CAD methodologies such as knowledge-aided design, knowledge-based neural networks, and space mapping are being developed. This leads to new opportunities combining equivalent circuit/empirical models, EM/physics simulation, behavioral modeling, neural network and space mapping optimization algorithms for fast and accurate design of high-frequency components and systems. This presentation provides an overview of the state of the art in these emerging directions. The presentations highlight implementable methodologies for automated modeling and design of RF/microwave components, circuits and systems. The presentation covers fundamental concepts and methodologies, industrial ap

关键词： Integrated circuit modeling Solid modeling Computational modeling Microwave filters Microwave amplifiers Microwave circuits Microwave communication

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Performance-Driven Floorplanning Technique Based on Collaboration of Software and Hardware

Performance-Driven Floorplanning Technique Based on Collabor...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Masaya Yoshikawa Masahiro Fukui Hidekazu Terai Department of VLSI System Design Ritsumeikan University Kusatsu Shiga Japan

The floorplanning problem is an essential design step in VLSI layout design and it is how to place rectangular modules as density as possible. In this paper, we proposed a novel performance driven floorplanning technique. The proposed algorithms based on genetic algorithm (GA) is adopted to a sequence pair. The GA is one of the most powerful optimization methods based on the mechanics of natural evolution. However, the problem of the processing time stemming from a population based search exits in GA. In order to reduce the processing time, a novel technique of collaboration of software (SW) and hardware(HW) also is introduced. Experimental results evaluating the proposed algorithm are shown good performance.

关键词： Collaborative software Software performance Hardware Very large scale integration Routing Application software optimization methods Collaboration Circuits Genetic algorithms

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Method of Selecting and Determining the Free Parameters of Swarm Intelligent algorithms for Optimizing Solutions in GIS

Method of Selecting and Determining the Free Parameters of S...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Dmytro Uhryn Vasyl Lytvyn Taras Lendiuk Chernivtsi faculty of the National Technical University “Kharkiv Polytechnic Institute” Chernivtsi Ukraine Lviv Polytechnic National University Lviv Ukraine West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9781665426060

The article proposes information technology for solving the discrete optimization problems in Geographic Information Systems (GIS). The information model of geographic data (geodata) was further developed, which consists of a formalized combination of their spatial and attributive components. It takes into account relational, semantic and frame models for representing the knowledge of the attributive component in order to unify the branch geodata. The conceptual model of decision support in sectoral GIS has been further developed, which takes into account the information model of geodata and the dynamics of their change, the hierarchy of tasks of sectoral GIS, the input operational data, the function of preferences and the criterion of decision-making. A new method of decision support for GIS construction was developed on the base of the proposed conceptual model. The method of selection and determination of free parameters of swarm intelligence algorithms is developed to increase the effectiveness of GIS. It is suggested to determine the free parameters for individual swarm algorithms based on machine learning with reinforcement, namely the Q-Learning method. Based on this method Markov chains for the swarm algorithms were constructed. Reinforcement consisted in the expert analysis of the results obtained by a certain swarm algorithm. On the example of territorial administration, optimal values of the parameters for individual swarm algorithms were found.

关键词： Training Machine learning algorithms Q-learning Semantics Data models Task analysis Particle swarm optimization

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Self Supervised Dictionary Learning Using Kernel Matching

Self Supervised Dictionary Learning Using Kernel Matching

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IEEE workshop on Machine Learning for Signal Processing

作者： Shubham Choudhary Paul Masset Demba Ba School of Engineering and Applied Science Harvard University Kempner Institute for the study of Natural and Artificial Intelligence Harvard University Department of Psychology McGill University

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

We introduce a self supervised framework for learning representations in the context of dictionary learning. We cast the problem as a kernel matching task between the input and the representation space, with constraints on the latent kernel. By adjusting these constraints, we demonstrate how the framework can adapt to different learning objectives. We then formulate a novel Alternate Direction Method of Multipli-ers (ADMM) based algorithm to solve the optimization problem and connect the dynamics to classical alternate minimization techniques. This approach offers a unique way of learning representations with kernel constraints, that enable us implicitly learn a generative map for the data from the learned representations which can have broad applications in representation learning tasks both in machine learning and neuro-science.

关键词： Representation learning Schedules Machine learning algorithms Dictionaries Heuristic algorithms Signal processing algorithms Signal processing Minimization Kernel optimization

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Block-Term Tensor Decomposition Via Constrained Matrix Factorization

Block-Term Tensor Decomposition Via Constrained Matrix Facto...

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IEEE workshop on Machine Learning for Signal Processing

作者： Xiao Fu Kejun Huang School of EECS Oregon State University Corvallis OR United States Department of CISE University of Florida Gainsville FL United States

In this work, we consider the problem of factoring a third-order tensor into multilinear rank-(L r , L r , 1) terms. This model, referred to as the rank-(L r , L r , 1) block-term decomposition (BTD), finds many applications in signal processing, especially blind separation of smooth sources and unmixing spectral-spatial data (e.g., hyperspectral image). On the other hand, finding latent factors of rank-(L r , L r , 1) BTD poses a very challenging optimization problem. Some computational tools designed for canonical polyadic decomposition (CPD) (e.g., alternating least squares (ALS) and Levenberg-Marquardt (LM) based algorithms) can be modified to handle rank(L r , L r , 1) BTD. Nonetheless, these methods essentially treat rank(L r , L r , 1) BTD as a special CPD problem. This raises a number of challenges, since rank-(L r , L r , 1) BTD can be viewed as a CPD problem with rank-deficient latent factors and high CP rank-and these are known as hard cases for CPD algorithms. In this work, we reformulate the rank-(L r , L r , 1) BTD problem as a matrix rank - constrained matrix factorization problem. We propose a simple algorithm that combines alternating optimization and projected gradient. This way, the per-iteration complexity is much smaller than those of the ALS and LM - based algorithms. We also show that the algorithm converges to a stationary point of the problem of interest at a sublinear rate-although nonconvex constraints are involved. Numerical experiments are conducted to showcase the effectiveness of our algorithm.

关键词： Tensile stress optimization Signal processing algorithms Approximation algorithms Computational modeling Data models Tools

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Fast evidence-based information fusion

Fast evidence-based information fusion

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international workshop on Cognitive Information Processing (CIP)

作者： Uwe Mönks Volker Lohweg InIT - Institute Industrial IT Ostwestfalen-Lippe University of Applied Sciences Lemgo Germany

Information fusion systems are crucial for the success of the upcoming fourth industrial revolution. In this emerging field, cyber-physicals systems play a major role. These are physical processing systems equipped with sensory devices which interconnect over communication networks for distributed cognitive information processing applications. Cyber-physical systems are generally limited in computational resources. Due to this fact, signal processing algorithms cannot be implemented one-to-one. Instead, efforts must be spent in algorithm optimisation towards resource efficiency and reduced computational complexity. In this contribution, we present our optimisation approach by matrix decomposition of an evidence-based conflict-reducing fusion approach which after optimisation is applicable in resource-limited devices for cognitive signal processing. We evaluate the results by comparison with the algorithm's original definition and show the improvements achieved.

关键词： Matrix decomposition Signal processing algorithms optimization Vectors Sparse matrices Computational complexity Silicon

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