Distance Concentration-Based Artificial Immune Algorithm
Institute of Scientific and Technical Information of China (English)
LIU Tao; WANG Yao-cai; WANG Zhi-jie; MENG Jiang
2005-01-01
The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentration-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune algorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the problem of precocity,holding the diversity of antibody, and enhancing convergence rate.
Immune Based Intrusion Detector Generating Algorithm
Institute of Scientific and Technical Information of China (English)
DONG Xiao-mei; YU Ge; XIANG Guang
2005-01-01
Immune-based intrusion detection approaches are studied. The methods of constructing self set and generating mature detectors are researched and improved. A binary encoding based self set construction method is applied. First,the traditional mature detector generating algorithm is improved to generate mature detectors and detect intrusions faster. Then, a novel mature detector generating algorithm is proposed based on the negative selection mechanism. Accord ing to the algorithm, less mature detectors are needed to detect the abnormal activities in the network. Therefore, the speed of generating mature detectors and intrusion detection is improved. By comparing with those based on existing algo rithms, the intrusion detection system based on the algorithm has higher speed and accuracy.
Generalized Rule Induction Based on Immune Algorithm
Institute of Scientific and Technical Information of China (English)
郑建国; 刘芳; 焦李成
2002-01-01
A generalized rule induction mechanism, immune algorithm, for knowledge bases is building an inheritance hierarchy of classes based on the content of their knowledge objects. This hierarchy facilitates group-related processing tasks such as answering set queries, discriminating between objects, finding similarities among objects, etc. Building this hierarchy is a difficult task for knowledge engineers. Conceptual induction may be used to automate or assist engineers in the creation of such a classification structure. This paper introduces a new conceptual rule induction method, which addresses the problem of clustering large amounts of structured objects. The conditions under which the method is applicable are discussed.
Evolutionary Algorithm Based on Immune Strategy
Institute of Scientific and Technical Information of China (English)
WANG Lei; JIAO Licheng
2001-01-01
A novel evolutionary algorithm,evolution-immunity strategies(EIS), is proposed with reference to the immune theory in biology, which constructs an immune operator accomplished by two steps, a vaccination and an immune selection. The aim of introducing the immune concepts and methods into ES is for finding the ways and means obtaining the optimal solution of difficult problems with locally characteristic information. The detail processes of realizing EIS are presented which contain 6 steps. EIS is analyzed with Markovian theory and it is approved to be convergent with probability 1. In EIS, an immune operator is an aggregation of specific operations and procedures, and methods of selecting vaccines and constructing an immune operator are given in this paper. It is shown with an example of the 442-city TSP that the EIS can restrain the degenerate phenomenon during the evolutionary process by simulated calculating result, improve the searching capability and efficiency, and therefore, increase the convergent speed greatly.
A danger-theory-based immune network optimization algorithm.
Zhang, Ruirui; Li, Tao; Xiao, Xin; Shi, Yuanquan
2013-01-01
Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.
Clonal Strategy Algorithm Based on the Immune Memory
Institute of Scientific and Technical Information of China (English)
Ruo-Chen Liu; Li-Cheng Jiao; Hai-Feng Du
2005-01-01
Based on the clonal selection theory and immune memory mechanism in the natural immune system, a novel artificial immune system algorithm, Clonal Strategy Algorithm based on the Immune Memory (CSAIM), is proposed in this paper. The algorithm realizes the evolution of antibody population and the evolution of memory unit at the same time, and by using clonal selection operator, the global optimal computation can be combined with the local searching. According to antibody-antibody (Ab-Ab) affinity and antibody-antigen (Ab-Ag) affinity, the algorithm can allot adaptively the scales of memory unit and antibody population. It is proved theoretically that CSAIM is convergent with probability 1. And with the computer simulations of eight benchmark functions and one instance of traveling salesman problem (TSP), it is shown that CSAIM has strong abilities in having high convergence speed, enhancing the diversity of the population and avoiding the premature convergence to some extent.
Surname Inherited Algorithm Research Based on Artificial Immune System
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Jing Xie
2013-06-01
Full Text Available To keep the diversity of antibodies in artificial immune system evolution process, this paper puts forward a kind of increase simulation surname inheritance algorithm based on the clonal selection algorithm, and identification and forecast the Vibration Data about CA6140 horizontal lathe machining slender shaft workpiece prone . The results show that the algorithm has the characteristics of flexible application, strong adaptability, an effective approach to improve efficiency of the algorithm, a good performance of global searching and broad application prospect.
Dynamic route guidance algorithm based algorithm based on artificial immune system
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.
Immune and Genetic Algorithm Based Assembly Sequence Planning
Institute of Scientific and Technical Information of China (English)
YANG Jian-guo; LI Bei-zhi; YU Lei; JIN Yu-song
2004-01-01
In this paper an assembly sequence planning model inspired by natural immune and genetic algorithm (ASPIG) based on the part degrees of freedom matrix (PDFM) is proposed, and a proto system - DSFAS based on the ASPIG is introduced to solve assembly sequence problem. The concept and generation of PDFM and DSFAS are also discussed. DSFAS can prevent premature convergence, and promote population diversity, and can accelerate the learning and convergence speed in behavior evolution problem.
NOVEL QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON IMMUNITY
Institute of Scientific and Technical Information of China (English)
Li Ying; Zhao Rongchun; Zhang Yanning; Jiao Licheng
2005-01-01
A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.
Multiobjective immune algorithm with nondominated neighbor-based selection.
Gong, Maoguo; Jiao, Licheng; Du, Haifeng; Bo, Liefeng
2008-01-01
Abstract Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.
Institute of Scientific and Technical Information of China (English)
Zu Yun-Xiao; Zhou Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed,and a fitness function is provided.Simulations are conducted using the adaptive niche immune genetic algorithm,the simulated annealing algorithm,the quantum genetic algorithm and the simple genetic algorithm,respectively.The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation,and has quick convergence speed and strong global searching capability,which effectively reduces the system power consumption and bit error rate.
Zu, Yun-Xiao; Zhou, Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.
Optimization of Submarine Hydrodynamic Coefficients Based on Immune Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
HU Kun; XU Yi-fan
2010-01-01
Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations, an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations. Some hydrody-namic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm. By using adaptive weight method to determine the weight and target function, the multi-objective optimization could be transla-ted into single-objective optimization. For a certain kind of submarine, three typical maneuvers were chosen to be the objects of study: overshoot maneuver in horizontal plane, overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane. From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrody-namic coefficient, the efficiency of proposed method is proved.
A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy
Institute of Scientific and Technical Information of China (English)
GONG Xin-bao; ZANG Xiao-gang; ZHOU Xi-lang
2005-01-01
A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the structure and parameters of RBF nonlinear hidden layer, and weights of RBF linear output layer are computed with least square method. By introducing the diversity control and immune memory mechanism, the algorithm improves the efficiency and overcomes the immature problem in genetic algorithm. Computer simulations demonstrate that the RBF networks designed in this method have fast convergence speed with good performances.
An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation
Mengling Zhao; Hongwei Liu
2015-01-01
As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new class...
[Non-linear rectification of sensor based on immune genetic algorithm].
Lu, Lirong; Zhou, Jinyang; Niu, Xiaodong
2014-08-01
A non-linear rectification based on immune genetic algorithm (IGA) is proposed in this paper, for the shortcoming of the non-linearity rectification. This algorithm introducing the biologic immune mechanism into the genetic algorithm can restrain the disadvantages that the poor precision, slow convergence speed and early maturity of the genetic algorithm. Computer simulations indicated that the algorithm not only keeps population diversity, but also increases the convergent speed, precision and the stability greatly. The results have shown the correctness and effectiveness of the method.
[Non-linear rectification of sensor based on immune genetic Algorithm].
Lu, Lirong; Zhou, Jinyang; Niu, Xiaodong
2014-08-01
A non-linear rectification based on immune genetic algorithm (IGA) is proposed in this paper, for the shortcoming of the non-linearity rectification. This algorithm introducing the biologic immune mechanism into the genetic algorithm can restrain the disadvantages that the poor precision, slow convergence speed and early maturity of the genetic algorithm. Computer simulations indicated that the algorithm not only keeps population diversity, but also increases the convergent speed, precision and the stability greatly. The results have shown the correctness and effectiveness of the method.
An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation
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Mengling Zhao
2015-01-01
Full Text Available As a computational intelligence method, artificial immune network (AIN algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN. The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC, and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.
A Novel Dynamic Clustering Algorithm Based on Immune Network and Tabu Search
Institute of Scientific and Technical Information of China (English)
ZHONGJiang; WUZhongfu; WUKaigui; YANGQiang
2005-01-01
It's difficult to indicate the rational number of partitions in the data set before clustering usually.The problem can't be solved by traditional clustering algorithm, such as k-means or its variations. This paper proposes a novel Dynamic clustering algorithm based on the artificial immune network and tabu search (DCBIT). It optimizes the number and the location of the clusters at the same time. The algorithm includes two phases, it begins by running immune network algorithm to find a Clustering feasible solution (CFS), then it employs tabu search to get the optimum cluster number and cluster centers on the CFS. Also, the probabilities acquiring the CFS through immune network algorithm have been discussed in this paper. Some experimental results show that new algorithm has satisfied convergent probability and convergent speed.
Application of a Genetic Algorithm Based on the Immunity for Flow Shop under Uncertainty
Institute of Scientific and Technical Information of China (English)
WANG Luchao; DENG Yongping
2006-01-01
The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then, an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature. In this method, processing sequence of products could be expressed by the character encoding and each antibody represents a feasible schedule. Affinity was used to measure the matching degree between antibody and antigen. Then several antibodies producing operators, such as swopping, moving, inverting, etc, were worked out. This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system. Promotion and inhibition of antibodies were realized by expected propagation ratio of antibodies, and in this way, premature convergence was improved. The simulation proved that this algorithm is effective.
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Litian Duan
2016-11-01
Full Text Available In the multiple-reader environment (MRE of radio frequency identification (RFID system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.
Reservoir Flood Control Operation Based on Adaptive Immune Differential Evolution Algorithm
Zou, Qiang; Lu, Jun; Yu, Shan
2017-05-01
Reservoir flood control operation (RFCO) is a high dimensional complex problem with multi-stages, multi-variables and multi-constraints, and its optimal solution is not easy to get. Differential evolution algorithm (DE) can be applied in RFCO, but its species diversity may sharply decline at the last evolution and lead into local optimal. Therefore, based on the adaptively controlling for mutation factor and crossover factor in each generation and immune clonal selection for better individuals, then adaptive immune differential evolution algorithm (AIDE) was proposed. And test function simulation verified the feasibility and efficiency of AIDE. Finally, AIDE was employed for RFCO and case study showed that AIDE could get better flood control benefit with fast convergence and high accuracy, moreover the outcomes of this research provided an effective way for RFCO.
Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm
Institute of Scientific and Technical Information of China (English)
Mao-Guo Gong; Ling-Jun Zhang; Jing-Jing Ma; Li-Cheng Jiao
2012-01-01
Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.
Dynamic recurrent Elman neural network based on immune clonal selection algorithm
Wang, Limin; Han, Xuming; Li, Ming; Sun, Haibo; Li, Qingzhao
2012-04-01
Owing to the immune clonal selection algorithm introduced into dynamic threshold strategy has better advantage on optimizing multi-parameters, therefore a novel approach that the immune clonal selection algorithm introduced into dynamic threshold strategy, is used to optimize the dynamic recursion Elman neural network is proposed in the paper. The concrete structure of the recursion neural network, the connect weight and the initial values of the contact units etc. are done by evolving training and learning automatically. Thus it could realize to construct and design for dynamic recursion Elman neural networks. It could provide a new effective approach for immune clonal selection algorithm optimizing dynamic recursion neural networks.
Alternate mutation based artificial immune algorithm for step fixed charge transportation problem
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Mahmoud Moustafa El-Sherbiny
2012-07-01
Full Text Available Step fixed charge transportation problem (SFCTP is considered as a special version of the fixed-charge transportation problem (FCTP. In SFCTP, the fixed cost is incurred for every route that is used in the solution and is proportional to the amount shipped. This cost structure causes the value of the objective function to behave like a step function. Both FCTP and SFCTP are considered to be NP-hard problems. While a lot of research has been carried out concerning FCTP, not much has been done concerning SFCTP. This paper introduces an alternate Mutation based Artificial Immune (MAI algorithm for solving SFCTPs. The proposed MAI algorithm solves both balanced and unbalanced SFCTP without introducing a dummy supplier or a dummy customer. In MAI algorithm a coding schema is designed and procedures are developed for decoding such schema and shipping units. MAI algorithm guarantees the feasibility of all the generated solutions. Due to the significant role of mutation function on the MAI algorithm’s quality, 16 mutation functions are presented and their performances are compared to select the best one. For this purpose, forty problems with different sizes have been generated at random and then a robust calibration is applied using the relative percentage deviation (RPD method. Through two illustrative problems of different sizes the performance of the MAI algorithm has been compared with most recent methods.
Securing mobile ad hoc networks using danger theory-based artificial immune algorithm.
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Maha Abdelhaq
Full Text Available A mobile ad hoc network (MANET is a set of mobile, decentralized, and self-organizing nodes that are used in special cases, such as in the military. MANET properties render the environment of this network vulnerable to different types of attacks, including black hole, wormhole and flooding-based attacks. Flooding-based attacks are one of the most dangerous attacks that aim to consume all network resources and thus paralyze the functionality of the whole network. Therefore, the objective of this paper is to investigate the capability of a danger theory-based artificial immune algorithm called the mobile dendritic cell algorithm (MDCA to detect flooding-based attacks in MANETs. The MDCA applies the dendritic cell algorithm (DCA to secure the MANET with additional improvements. The MDCA is tested and validated using Qualnet v7.1 simulation tool. This work also introduces a new simulation module for a flooding attack called the resource consumption attack (RCA using Qualnet v7.1. The results highlight the high efficiency of the MDCA in detecting RCAs in MANETs.
Securing mobile ad hoc networks using danger theory-based artificial immune algorithm.
Abdelhaq, Maha; Alsaqour, Raed; Abdelhaq, Shawkat
2015-01-01
A mobile ad hoc network (MANET) is a set of mobile, decentralized, and self-organizing nodes that are used in special cases, such as in the military. MANET properties render the environment of this network vulnerable to different types of attacks, including black hole, wormhole and flooding-based attacks. Flooding-based attacks are one of the most dangerous attacks that aim to consume all network resources and thus paralyze the functionality of the whole network. Therefore, the objective of this paper is to investigate the capability of a danger theory-based artificial immune algorithm called the mobile dendritic cell algorithm (MDCA) to detect flooding-based attacks in MANETs. The MDCA applies the dendritic cell algorithm (DCA) to secure the MANET with additional improvements. The MDCA is tested and validated using Qualnet v7.1 simulation tool. This work also introduces a new simulation module for a flooding attack called the resource consumption attack (RCA) using Qualnet v7.1. The results highlight the high efficiency of the MDCA in detecting RCAs in MANETs.
Directory of Open Access Journals (Sweden)
Haitao Zhang
2015-01-01
Full Text Available We first analyze the effect of network-induced delay on the stability of networked control systems (NCSs. Then, aiming at stochastic characteristics of the time delay, we introduce a new Smith predictor to remove the exponential function with the time delay in the closed-loop characteristic equation of the NCS. Furthermore, we combine the fuzzy PID algorithm with the fuzzy immune control algorithm and present a fuzzy immune self-adaptive PID algorithm to compensate the influence of the model deviation of the controlled object. At last, a kind of fuzzy immune self-adaptive PID algorithm based on new Smith predictor is presented to apply to the NCS. The simulation research on a DC motor is given to show the effectiveness of the proposed algorithm.
Gao, Wei; Chen, Dongliang; Wang, Xu
2016-01-01
To compute the stability of underground engineering, a constitutive model of surrounding rock must be identified. Many constitutive models for rock mass have been proposed. In this model identification study, a generalized constitutive law for an elastic-plastic constitutive model is applied. Using the generalized constitutive law, the problem of model identification is transformed to a problem of parameter identification, which is a typical and complicated optimization. To improve the efficiency of the traditional optimization method, an immunized genetic algorithm that is proposed by the author is applied in this study. In this new algorithm, the principle of artificial immune algorithm is combined with the genetic algorithm. Therefore, the entire computation efficiency of model identification will be improved. Using this new model identification method, a numerical example and an engineering example are used to verify the computing ability of the algorithm. The results show that this new model identification algorithm can significantly improve the computation efficiency and the computation effect.
Study on Immune Relevant Vector Machine Based Intelligent Fault Detection and Diagnosis Algorithm
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Zhong-hua Miao
2013-01-01
Full Text Available An immune relevant vector machine (IRVM based intelligent classification method is proposed by combining the random real-valued negative selection (RRNS algorithm and the relevant vector machine (RVM algorithm. The method proposed is aimed to handle the training problem of missing or incomplete fault sampling data and is inspired by the “self/nonself” recognition principle in the artificial immune systems. The detectors, generated by the RRNS, are treated as the “nonself” training samples and used to train the RVM model together with the “self” training samples. After the training succeeds, the “nonself” detection model, which requires only the “self” training samples, is obtained for the fault detection and diagnosis. It provides a general way solving the problems of this type and can be applied for both fault detection and fault diagnosis. The standard Fisher's Iris flower dataset is used to experimentally testify the proposed method, and the results are compared with those from the support vector data description (SVDD method. Experimental results have shown the validity and practicability of the proposed method.
Rules Extraction with an Immune Algorithm
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Deqin Yan
2007-12-01
Full Text Available In this paper, a method of extracting rules with immune algorithms from information systems is proposed. Designing an immune algorithm is based on a sharing mechanism to extract rules. The principle of sharing and competing resources in the sharing mechanism is consistent with the relationship of sharing and rivalry among rules. In order to extract rules efficiently, a new concept of flexible confidence and rule measurement is introduced. Experiments demonstrate that the proposed method is effective.
A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm
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Zhongbin Wang
2016-01-01
Full Text Available In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN and support vector machine (SVM methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.
Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm
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Yang Ou
2014-01-01
Full Text Available This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.
Human body motion tracking based on quantum-inspired immune cloning algorithm
Han, Hong; Yue, Lichuan; Jiao, Licheng; Wu, Xing
2009-10-01
In a static monocular camera system, to gain a perfect 3D human body posture is a great challenge for Computer Vision technology now. This paper presented human postures recognition from video sequences using the Quantum-Inspired Immune Cloning Algorithm (QICA). The algorithm included three parts. Firstly, prior knowledge of human beings was used, the key joint points of human could be detected automatically from the human contours and skeletons which could be thinning from the contours; And due to the complexity of human movement, a forecasting mechanism of occlusion joint points was addressed to get optimum 2D key joint points of human body; And then pose estimation recovered by optimizing between the 2D projection of 3D human key joint points and 2D detection key joint points using QICA, which recovered the movement of human body perfectly, because this algorithm could acquire not only the global optimal solution, but the local optimal solution.
Institute of Scientific and Technical Information of China (English)
Yu-xiang LI; Yin-liang ZHAO‡; Bin LIU; Shuo JI
2015-01-01
Thread partition plays an important role in speculative multithreading (SpMT) for automatic parallelization of ir-regular programs. Using unified values of partition parameters to partition different applications leads to the fact that every ap-plication cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold (DT), lower limit of thread size (TSL), upper limit of thread size (TSU), lower limit of spawning distance (SDL), and upper limit of spawning distance (SDU). Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all com-binations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm (AIA) to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden bench-marks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00%on a 4-core platform compared with a machine learning based approach, and 8.92%compared with a heuristics-based approach.
Institute of Scientific and Technical Information of China (English)
杨淑霞
2008-01-01
Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.
Immunity clone algorithm with particle swarm evolution
Institute of Scientific and Technical Information of China (English)
LIU Li-jue; CAI Zi-xing; CHEN Hong
2006-01-01
Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algorithm lies in two aspects.Via immunity operation, the diversity of the antibodies was maintained, and the speed of convergent was improved by using particle swarm evolution equations. Simulation programme and three functions were used to check the effect of the algorithm. The advanced algorithm were compared with clonal selection algorithm and particle swarm algorithm. The results show that this advanced algorithm can converge to the global optimum at a great rate in a given range, the performance of optimization is improved effectively.
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Hui Du
2016-01-01
Full Text Available To produce the final product, parts need to be fabricated in the process stages and thereafter several parts are joined under the assembly operations based on the predefined bill of materials. But assembly relationship between the assembly parts and components has not been considered in general job shop scheduling problem model. The aim of this research is to find the schedule which minimizes completion time of Assembly Job Shop Scheduling Problem (AJSSP. Since the complexity of AJSSP is NP-hard, a hybrid particle swarm optimization (HPSO algorithm integrated PSO with Artificial Immune is proposed and developed to solve AJSSP. The selection strategy based on antibody density makes the particles of HPSO maintain the diversity during the iterative process, thus overcoming the defect of premature convergence. Then HPSO algorithm is applied into a case study development from classical FT06. Finally, the effect of key parameters on the proposed algorithm is analyzed and discussed regarding how to select the parameters. The experiment result confirmed its practice and effectiveness.
Directory of Open Access Journals (Sweden)
Wensheng Dong
2014-01-01
Full Text Available The critical external pressure stability calculation of stiffened penstock in the hydroelectric power station is very important work for penstock design. At present, different assumptions and boundary simplification are adopted by different calculation methods which sometimes cause huge differences too. In this paper, we present an immune based artificial neural network model via the model and stability theory of elastic ring, we study effects of some factors (such as pipe diameter, pipe wall thickness, sectional size of stiffening ring, and spacing between stiffening rings on penstock critical external pressure during huge thin-wall procedure of penstock. The results reveal that the variation of diameter and wall thickness can lead to sharp variation of penstock external pressure bearing capacity and then give the change interval of it. This paper presents an optimizing design method to optimize sectional size and spacing of stiffening rings and to determine penstock bearing capacity coordinate with the bearing capacity of stiffening rings and penstock external pressure stability coordinate with its strength safety. As a practical example, the simulation results illustrate that the method presented in this paper is available and can efficiently overcome inherent defects of BP neural network.
Using network properties to evaluate targeted immunization algorithms
Directory of Open Access Journals (Sweden)
Bita Shams
2014-09-01
Full Text Available Immunization of complex network with minimal or limited budget is a challenging issue for research community. In spite of much literature in network immunization, no comprehensive research has been conducted for evaluation and comparison of immunization algorithms. In this paper, we propose an evaluation framework for immunization algorithms regarding available amount of vaccination resources, goal of immunization program, and time complexity. The evaluation framework is designed based on network topological metrics which is extensible to all epidemic spreading model. Exploiting evaluation framework on well-known targeted immunization algorithms shows that in general, immunization based on PageRank centrality outperforms other targeting strategies in various types of networks, whereas, closeness and eigenvector centrality exhibit the worst case performance.
Immune Algorithm For Document Query Optimization
Institute of Scientific and Technical Information of China (English)
WangZiqiang; FengBoqin
2005-01-01
To efficiently retrieve relevant document from the rapid proliferation of large information collections, a novel immune algorithm for document query optimization is proposed. The essential ideal of the immune algorithm is that the crossover and mutation of operator are constructed according to its own characteristics of information retrieval. Immune operator is adopted to avoid degeneracy. Relevant documents retrieved am merged to a single document list according to rank formula. Experimental results show that the novel immune algorithm can lead to substantial improvements of relevant document retrieval effectiveness.
Institute of Scientific and Technical Information of China (English)
DOU Wei; LIU Zhan-sheng
2009-01-01
To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effec tively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.
Institute of Scientific and Technical Information of China (English)
WU Jing-min; ZUO Hong-fu; CHEN Yong
2005-01-01
A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented.Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune selection mechanisms were used to prevent the undulate phenomenon during the evolutionary process. The algorithm was introduced through an application in the direct maintenance cost (DMC) estimation of aircraft components. Experiments results show that the algorithm can compute simply and run quickly. It resolves the combinatorial optimization problem of component DMC estimation with simple and available parameters. And it has higher accuracy than individual methods, such as PLS, BP and v-SVM, and also has better performance than other combined methods, such as basic PSO and BP neural network.
Institute of Scientific and Technical Information of China (English)
Hai-tao Bo; Xiao-feng Jia; Xiao-rui Wang
2009-01-01
As in the building of deep buried long tunnels, there are complicated conditions such as great deformation, high stress, multi-variables, high non-linearity and so on, the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country, It has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying, expressing and disposing such kind of multiple variables and complicated non- linear relations. In this paper, a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm, thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory, the global search capability of the immune genetic algorithm is raised, thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel, the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency, the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationlzation design of the wall rock of the tunnel.
Design of Immune-Algorithm-Based Adaptive Fuzzy Controllers for Active Suspension Systems
Directory of Open Access Journals (Sweden)
Ming-Yuan Shieh
2014-04-01
Full Text Available The aim of this paper is to integrate the artificial immune systems and adaptive fuzzy control for the automobile suspension system, which is regarded as a multiobjective optimization problem. Moreover, the fuzzy control rules and membership controls are then introduced for identification and memorization. It leads fast convergence in the search process. Afterwards, by using the diversity of the antibody group, trapping into local optimum can be avoided, and the system possesses a global search capacity and a faster local search for finding a global optimal solution. Experimental results show that the artificial immune system with the recognition and memory functions allows the system to rapidly converge and search for the global optimal approximate solutions.
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed,which is called the immune genetic algorithm with the elitism (IGAE).In IGAE,the new methods for computing antibody similarity,expected reproduction probability,and clonal selection probability were given.IGAE has three features.The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage,which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively.The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem.The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter β,which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly.Two different complex multi-modal functions were selected to test the validity of IGAE.The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly.The experimental results also confirm that IGAE is of better performance in convergence speed,solution variation behavior,and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.
Directory of Open Access Journals (Sweden)
Wen-An Yang
2016-01-01
Full Text Available The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.
ARTIFICIAL IMMUNE ALGORITHM OF MULTICELLULAR GROUP AND ITS CONVERGENCE
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a multicellular group's artificial immune algorithm was proposed based on the germinal center reaction mechanism of natural immune systems. Main steps of the algorithm were given, including hyper-mutation, selection, memory, similarity suppression and recruitment of B cells and the convergence of it was proved. Results The algorithm has been tested to optimize various multi-modal functions, and the simulation results show that the artificial immune algorithm proposed here can find multiple extremum of these functions with lower computational cost. Conclusion The algorithm is valid and can converge on the satisfactory solution set D with probability 1 and approach to global solution and many local optimal solutions existed.
Learning Bayesian network structure with immune algorithm
Institute of Scientific and Technical Information of China (English)
Zhiqiang Cai; Shubin Si; Shudong Sun; Hongyan Dui
2015-01-01
Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network (BN), which is also necessary in promoting the application of BN. This pa-per proposes an immune algorithm based method (BN-IA) for the learning of the BN structure with the idea of vaccination. Further-more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Final y, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.
ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM
Directory of Open Access Journals (Sweden)
D. Amutha Guka
2012-01-01
Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.
Optimizing neural network forecast by immune algorithm
Institute of Scientific and Technical Information of China (English)
YANG Shu-xia; LI Xiang; LI Ning; YANG Shang-dong
2006-01-01
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
An Adaptive Immune Genetic Algorithm for Edge Detection
Li, Ying; Bai, Bendu; Zhang, Yanning
An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
Immune Algorithm for Selecting Optimum Services in Web Services Composition
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an Immune Algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.
Directory of Open Access Journals (Sweden)
S. Shojaeilangari
2012-09-01
Full Text Available In recent years, an increasing number of researches have been focused on bio-inspired algorithms to solve the elaborate engineering problems. Artificial Immune System (AIS is an artificial intelligence technique which has potential of solving problems in various fields. The immune system, due to self-regulating nature, has been an inspiration source of unsupervised learning methods for pattern recognition task. The purpose of this study is to apply the AIS to pre-process the lie-detection dataset to promote the recognition of guilty and innocent subjects. A new Unsupervised AIS (UAIS was proposed in this study as a pre-processing method before classification. Then, we applied three different classifiers on pre-processed data for Event Related Potential (ERP assessment in a P300-based Guilty Knowledge Test (GKT. Experiment results showed that UAIS is a successful pre-processing method which is able to improve the classification rate. In our experiments, we observed that the classification accuracies for three different classifiers: K-Nearest Neighbourhood (KNN, Support Vector Machine (SVM and Linear Discriminant Analysis (LDA were increased after applying UAIS pre-processing. Using of scattering criterion to assessment the features before and after pre-processing proved that our proposed method was able to perform data mapping from a primary feature space to a new area where the data separability was improved significantly.
An Improved Artificial Immune Algorithm with a Dynamic Threshold
Institute of Scientific and Technical Information of China (English)
Zhang Qiao; Xu Xu; Liang Yan-chun
2006-01-01
An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody's fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.
Artificial immune algorithm for multi-depot vehicle scheduling problems
Wu, Zhongyi; Wang, Donggen; Xia, Linyuan; Chen, Xiaoling
2008-10-01
In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP) help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving MDVSP problems.
Artificial immune system algorithm in VLSI circuit configuration
Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd
2017-08-01
In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.
Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches
Directory of Open Access Journals (Sweden)
Ufuk Çelik
2015-01-01
Full Text Available The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.
IMMUNE GENETIC ALGORITHM FOR THE PATH PLANNING OF TIGHTLY COORDINATED TWO-ROBOT MANIPULATORS
Institute of Scientific and Technical Information of China (English)
Gao Sheng; Zhao Jie; Cai Hegao
2004-01-01
A novel algorithm, the immune genetic algorithm based on multi-agent, is proposed for the path planning of tightly coordinated two-robot manipulators, which constructs mainly immune operators accomplished by three steps: defining strategies and methods of multi-agent, calculating virtual forces acting on an agent, and constructing immune operators and performing immunization during the evolutionary process. It is illustrated to be able to restrain the degenerate phenomenon effectively and improve the searching ability with high converging speed.
Job-Shop scheduling based on improved immune cloning algorithm%改进免疫克隆算法的Job Shop调度
Institute of Scientific and Technical Information of China (English)
刘爱军; 杨育; 邢青松; 姚豪; 张煜东; 周振宇
2011-01-01
Parallel immune clone algorithm is proposed based on population coevolution theory and parallel computing affinity of individual at multiple compute nodes.Introducing the immune memory mechanism,the evolution processes of antibody population and memory units are conducted simultaneously,meanwhile,it improves mutual cooperation among antibodies,and ensures solution set approaching optimal solution from the inside of feasible region or infeasible region border.Clone proliferation,high frequency variation and operation of crossover operators increase the chance that better individuals gain affinity maturation by the operation of clone expansion,improve diversity of antibody population distribution,achieve the balance of optimization between depth and range,and ensure the convergence of the algorithm and the diversity of the search range.A computational study for a standard data set is carried out to test the validity of the algorithm,and the effect of algorithm parameters on the results is analyzed.The simulation results show that the global search capability,local search capability,algorithm stability and computing speed of the algorithm are all superior to conventional optimization algorithms such as normal immune clone optimization algorithm,genetic algorithm,etc.%提出了基于种群协同进化的并行免疫克隆算法,将种群中个体的亲和度计算并行在多个计算节点上同时进行。引入免疫记忆机制,使抗体种群的演化过程和记忆单元的演化过程并行进行,更好地实现了抗体间的相互协作,保证了解集从可行域内部和不可行域边缘向着最优解逼近。采用了克隆增殖变异和交叉算子的操作,增加了种群中优秀个体获得克隆增殖实现亲和度成熟的机会,提高抗体群分布的多样性,在深度搜索和广度寻优之间取得了平衡。从而保证了算法较强的收敛性以及搜索空间的多样性。利用标准问题库对算法进行测试,并分析算法参
Handwritten digits recognition based on immune network
Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe
2011-11-01
With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.
Application of Immune Algorithm to Evaluation of Soil Resource Quality
Institute of Scientific and Technical Information of China (English)
YANG Hai-Dong; HU Yue-Ming; DENG Fei-Qi; CHEN Fei-Xiang; WANG Fei
2005-01-01
Based on the geographic information system (GIS) technology, ArcInfo software was adopted to collect, process and analyze spatial data of Guangdong Province for an evaluation of soil resource quality. The overlay analysis method was used in combining evaluation factors of Guangdong soil resource quality to determine the evaluation units. Because of its favorable convergent speed and its ability to search solutions, the immune algorithm was applied to the soil resource quality evaluation model. At the same time, the evaluation results of this newly proposed method were compared to two other methods: sum of index and fuzzy synthetic. The results indicated that the immune algorithm reflected the actual condition of soil resource quality more exactly.
Improved Artificial Immune Algorithm Based on Weapon-Target Assignment%基于改进人工免疫算法的火力分配
Institute of Scientific and Technical Information of China (English)
刘洪引; 李体方; 王立安
2014-01-01
随着现代武器杀伤力的极大提高，任何来袭目标的突防都可能造成极大的破坏，这对传统的火力分配提出了挑战。提出一种新的火力集中原则，在满足对来袭目标一定杀伤的前提下，适当转移火力，实现火力总的集中，据此建立了火力分配优化模型。通过改进人工免疫算法的抗体群，提高模型的求解速度，缩短方案的寻优时间。通过实例进行仿真，结果表明，基于改进人工免疫算法能较快速实现火力分配，算法具有一定的可行性。%With the great improvement of modern weapons,any target penetration may cause great damage,which challenges the traditional fire distribution. So a new firepower-concentrated principle is put forward in this paper. According to the principle,the fire total concentration can be obtained through transferring fire appropriately,and a fire distribution model is founded. An optimized fire distribution scheme is given based on the improved artificial immune algorithm. The simulation results show that the improved artificial immune algorithm is capable of distributing the firepower effectively and quickly,the method has certain feasibility.
A novel algorithm of artificial immune system for high-dimensional function numerical optimization
Institute of Scientific and Technical Information of China (English)
DU Haifeng; GONG Maoguo; JIAO Licheng; LIU Ruochen
2005-01-01
Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed.
Schedule Algorithm for Grid Task Based on Immune Principles%基于免疫原理的网格任务调度算法
Institute of Scientific and Technical Information of China (English)
吴成茂
2011-01-01
针对网格资源管理的任务调度问题,提出一种网格任务免疫调度算法.算法遵循克隆选择、亲和度成熟2个免疫原理,求解网格任务调度问题的全局最优解.讨论种群代数设置和算法参数的设置时该算法性能的影响.仿真实验结果表明,与传统的网格任务调度算法相比,该算法具有任务调度速度快、资源分配时间短、运行稳定等优点.%To address the task schedule problem in grid resource management, an immunity schedule algorithm for grid task is put forward. The algorithm abides by the immune principles of clonal selection and affinity maturation, and seeks the all-around excellent result to the schedule problem for grid task. The settings of group generation and parameters of the algorithm are discussed, which can influence the performance of the algorithm. Experimental results of simulation demonstrate that the algorithm, compared with the conventional algorithm of grid task schedule, has the virtues of rapid task schedule, short-time resource allocation and steady function.
Quantum-inspired immune clonal algorithm for global optimization.
Jiao, Licheng; Li, Yangyang; Gong, Maoguo; Zhang, Xiangrong
2008-10-01
Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum not gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.
Web Advertisement Allocation Optimization Based on Adaptive Immune Algorithms%基于自适应免疫算法的网站广告分配优化
Institute of Scientific and Technical Information of China (English)
闫涛; 闫继涛
2012-01-01
It is the key to web advertising that how to optimize its distribution to benefit both the web owner and advertisers. According to the properties of web advertisement, a hybrid pricing strategy based model is proposed. It is modeled as a constrained optimization problem to maximize the total revenue of the web. An adaptive immune algorithm is proposed to solve it. The adaptive characteristics lie in that, according to antibody affinity, antibody populations are dynamically divided into memory antibody units and general antibody units. It combines global search with local search effectively and hence improves the solution accuracy and the convergence rate. According to the characteristics of the solving problem, the relative immune operators are designed. The simulation results show that, the algorithm balances the revenues between the advertisement clients and web owners and the results are more practical.%如何优化网站广告分配,实现网站运行商和广告客户收益的双赢,是网站广告分配问题的关键.针对网站广告的特点,本文提出了一个基于混合定价策略的网站广告资源配置优化模型,将其建模为一个最大化网站总收益的约束优化问题,并通过自适应免疫克隆算法进行求解.自适应特性主要表现在:根据亲和度动态分配记忆单元和一般抗体单元,从而将全局搜索和局部搜索有效结合起来,有效提高了求解精度和收敛速度.同时,根据网站广告分配问题特点,设计了其它相关的免疫算子,如编码、克隆变异、克隆选择.仿真结果表明,算法有效平衡了网站广告客户和网站运营商的利益,求解结果更加实用.
基于免疫算法的水火电联合调度模型%Hydrothermal coordination scheduling model based on immune algorithm
Institute of Scientific and Technical Information of China (English)
周德建; 杨莉; 郭文明
2013-01-01
由于梯级水电站的一系列电站间水能联系紧密,本文提出一种以集合形式处理梯级水电站的机组组合免疫算法.算法利用了免疫算法的记忆调节机制和便于处理离散变量的优势,将存在水能联系的梯级水电站机组看作一个集合进行统一编码,精简了含有梯级水电站的水火电联合调度机组组合的有效寻优空间,提高了求解效率.此外,由于免疫算法的特点是高变异率,本文考虑了电力系统中峰荷、腰荷、基荷的概念,提出变异率根据机组类型设定,以提高寻优的效率.最后以一个修正的IEEE 118系统算例验证了本文算法的有效性和合理性.%A set theory is proposed for encoding hydro-units of cascade reservoirs according to their close hydraulic connections. Such encoding technique can make use of the memory adjustment mechanism of immune algorithm to simplify discrete variables booking. Through unified encoding of all the related hydro-units, the optimization region of hydro-thermal coordination scheduling that involves cascade reservoirs, is greatly reduced and hence the efficiency of solution is enhanced. This paper also puts forth a method of varying the mutation rates according to generator types and considers peak load, middle load and base load, so as to achieve a high efficiency of optimization under the high mutation rate of immune algorithm. A modified IEEE 118-bus system is given to demonstrate the effectiveness of the proposed scheduling approach.
Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection
Greensmith, Julie; Cayzer, Steve
2010-01-01
Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.
Institute of Scientific and Technical Information of China (English)
蒋尚亭
2014-01-01
随着信息化技术在高校的推广，我国部分高校已经开始利用计算机技术来进行高校运动会的管理，但赛程编排工作一直困扰着运动会管理人员，经过分析和研究提出一种基于免疫遗传算法的高校运动会赛程编排方法。免疫遗传算法将生物免疫原理引入到传统遗传算法中，通过接种疫苗来提高抗体的适应度，从而防止种群快速退化。最后通过相关实验说明免疫遗传算法与传统遗传算的性能差异，并利用免疫遗传算法来解决高校运动会赛程编排问题。%With the popularization of information technology in colleges and universities, some universities in China begin to use computer technology in college sports meeting management, but the arrangement work has been plagued by sports management personnel, through the analysis and study, it presents a method of arranging the movement will be based on immune genetic algorithm. Immune genetic algorithm to the biological immune principle is introduced to the traditional genetic algorithm, through vaccination to improve antibody fitness, so as to prevent the rapid degradation of population. Finally this paper illustrate the performance differences of immune genetic algorithm and traditional genetic algorithm through experiments, and the use of immune genetic algorithm to solve the sports meeting schedule problem.
Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm
Directory of Open Access Journals (Sweden)
Yi Li
2013-01-01
Full Text Available We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.
基于免疫量子遗传算法的多峰函数寻优%Multi-modal function optimization based on immune quantum genetic algorithm
Institute of Scientific and Technical Information of China (English)
徐雪松; 王四春
2012-01-01
针对多峰函数优化中的全局及局部寻优问题,提出了一种结合免疫克隆算子的量子遗传算法,给出了实现流程.该方法针对量子遗传算法在复杂连续函数优化中收敛速度慢、易陷入局部极值等缺点,采用免疫克隆操作及交叉策略提高抗体成熟力及亲和性,增强抗体群分布的多样性及稳定性,有效克服了量子遗传算法容易陷于局部最优及计算缓慢的不足.通过对多峰函数的全局寻优仿真实验,并与基本遗传算法、量子遗传算法的计算结果进行比较,结果表明在相同条件下,所提算法所需循环代数少,并且其鲁棒性高于普通量子遗传算法和遗传算法.%In order to balance the global optimization and local optimization in multi-modal function, an improved quantum genetic algorithm with immune operator was introduced. This algorithm included the idea of immune clonal, operation and cross strategy. Through this operator, the diversity of antibody and affinity maturation rate got enhanced. It not only overcame the flaw of the common quantum genetic algorithm which relapsed into local optimum result but also avoided the flaw of the common immune clone algorithm which calculated slowly. Having done the global optimization experiment on the multimodal function in the same condition, the result indicates that this algorithm can settle the problem of searching the global optimization result with less iteration, and is of more robust stability compared to common genetic algorithm and common quantum genetic algorithm.
基于文化免疫克隆算法的关联规则挖掘研究%Mining association rules based on cultured immune clone algorithm
Institute of Scientific and Technical Information of China (English)
杨光军
2013-01-01
针对关联规则挖掘问题，给出一种基于文化免疫克隆算法的关联规则挖掘方法，该方法将免疫克隆算法嵌入到文化算法的框架中，采用双层进化机制，利用免疫克隆算法的智能搜索能力和文化算法信念空间形成的公共认知信念的引导挖掘规则。该方法重新给出了文化算法中状况知识和历史知识的描述，设计了一种变异算子，能够自适应调节变异尺度，提高免疫克隆算法全局搜索能力。实验表明，该算法的运行速度和所得关联规则的准确率优于免疫克隆算法。%For the association rules mining, a method of mining association rules based on cultured immune clone algorithm is proposed. This method uses two-layer evolutionary mechanism and embeds the immune clone algorithm in the culture algorithm framework. It uses the intelligent searching ability of the immune clone algorithm and the commonly accepted knowledge in the culture algorithm to guide the rules mining. The situational knowledge and history knowledge in the culture algorithm are rede-fined, and a new mutation operator is put forward. This operator has the adaptive adjustment of mutation measure to improve the global search ability of immune clone algorithm. The experiments show that the new algorithm is superior to immune clone algo-rithm in performance speed and the rules’accuracy.
Directory of Open Access Journals (Sweden)
T. Karpagam
2012-01-01
Full Text Available Problem statement: Network topology design problems find application in several real life scenario. Approach: Most designs in the past either optimize for a single criterion like shortest or cost minimization or maximum flow. Results: This study discussed about solving a multi objective network topology design problem for a realistic traffic model specifically in the pipeline transportation. Here flow based algorithm focusing to transport liquid goods with maximum capacity with shortest distance, this algorithm developed with the sense of basic pert and critical path method. Conclusion/Recommendations: This flow based algorithm helps to give optimal result for transporting maximum capacity with minimum cost. It could be used in the juice factory, milk industry and its best alternate for the vehicle routing problem.
Institute of Scientific and Technical Information of China (English)
Xiao-rui Wang; Yuan-han Wang; Xiao-feng Jia
2009-01-01
Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and, mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively unproved the recommended values in the original prospecting data. This is of
Immune System Model Calibration by Genetic Algorithm
Presbitero, A.; Krzhizhanovskaya, V.; Mancini, E.; Brands, R.; Sloot, P.
2016-01-01
We aim to develop a mathematical model of the human immune system for advanced individualized healthcare where medication plan is fine-tuned to fit a patient's conditions through monitored biochemical processes. One of the challenges is calibrating model parameters to satisfy existing experimental
A novel quantum-inspired immune clonal algorithm with the evolutionary game approach
Institute of Scientific and Technical Information of China (English)
Qiuyi Wu; Licheng Jiao; Yangyang Li; Xiaozheng Deng
2009-01-01
The quantum-inspired immune clonal algorithm (QICA) is a rising intelligence algorithm. Based on evolutionary game theory and QICA, a quantum-inspired immune algorithm embedded with evolutionary game (EGQICA) is proposed to solve combination optimi-zation problems. In this paper, we map the quantum antibody's finding the optimal solution to player's pursuing maximum utility by choosing strategies in evolutionary games. Replicator dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this work. Experimental results indicate that the proposed approach maintains a good diversity and achieves superior performance.
Institute of Scientific and Technical Information of China (English)
史旭华; 钱锋
2012-01-01
Based on the immune mechanics and multi-agent technology, a multi-agent artificial immune network (Maopt-aiNet) algorithm is introduced. Maopt-aiNet makes use of the agent ability of sensing and acting to overcome premature problem, and combines the global and local search in the searching process. The performance of the proposed method is examined with 6 benchmark problems and compared with other well-known intelligent algorithms. The experiments show that Maopt-aiNet outperforms the other algorithms in these benchmark functions. Furthermore, Maopt-aiNet is applied to determine the Murphree efficiency of distillation column and satisfactory results are obtained.
Cooperative Automated Worm Response and Detection Immune Algorithm
Kim, Jungwon; Aickelin, Uwe; McLeod, Julie
2010-01-01
The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.
Institute of Scientific and Technical Information of China (English)
张思; 黄民翔; 陈丽莉
2012-01-01
According to the discontinuous and nonlinear characteristics of the optimization of short circuit current limiting strategies for power system, a parallel immune particle swarm optimization algorithm is proposed. The algorithm combines self-regulation mechanism of immune algorithm with particle swarm optimization algorithm, and adopts the selection mechanism based on particle similarity to ensure the diversity of particles in the optimization process. The introduction of vaccination concept according to the characteristics of particle code effectively eliminates the chance of missing the best pieces of particles, thus both convergence accuracy and convergence rate can be ensured. This paper achieves parallelism of immune particle swarm optimization algorithms on Matlab parallel computing platform. The results show that the proposed algorithm has strong global optimization ability and convergence stability, and optimization time is short, so it is practicable.%针对电力系统限流措施优化问题不连续、非线性的特点,提出一种并行免疫粒子群算法.该算法将免疫算法的自我调节机制引入粒子群算法,采用基于粒子相似度的选择机制,保证优化过程中粒子的多样性.根据粒子编码的特点引入疫苗接种概念,有效减少了粒子最优片段丢失的概率,保证算法的收敛精度和收敛速度.并在Matlab并行计算平台上实现免疫粒子群算法的并行化.算例表明该算法具有较强的全局优化能力和收敛稳定性,且计算时间短,有较强的实用意义.
An immune-tabu hybrid algorithm for thermal unit commitment of electric power systems
Institute of Scientific and Technical Information of China (English)
Wei LI; Hao-yu PENG; Wei-hang ZHU; De-ren SHENG; Jian-hong CHEN
2009-01-01
This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment (TUC) problem in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in modern power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the objective function of the TUC problem is considered as an antigen and the solutions are considered as antibodies,which are determined by the affinity computation. The code length of an antibody is shortened by encoding the continuous operating time, and the optimum searching speed is improved. Each feasible individual in the immune algorithm (IA) is used as the initial solution of the tabu search (TS) algorithm after certain generations of IA iteration. As examples, the proposed method has been applied to several thermal unit systems for a period of 24 h. The computation results demonstrate the good global optimum searching performance of the proposed immune-tabu hybrid algorithm. The presented algorithm can also be used to solve other optimization problems in fields such as the chemical industry and the power industry.
Multithreshold Segmentation Based on Artificial Immune Systems
Directory of Open Access Journals (Sweden)
Erik Cuevas
2012-01-01
Full Text Available Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS technique, also known as theclonal selection algorithm (CSA. It follows the clonal selection principle (CSP from the human immune system which basically generates a response according to the relationship between antigens (Ag, that is, patterns to be recognized and antibodies (Ab, that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.
Institute of Scientific and Technical Information of China (English)
刘朝华; 张英杰; 章兢; 吴建辉
2011-01-01
The active disturbance rejection control (ADRC) has the property of requiring no knowledge about the precise mathematical model, but the parameters of controller is difficult to be tuned. An active disturbance rejection control based on immune binary-state particle swarm optimization algorithm (IBPSO-ADRC) is proposed, which takes advantage of the combination of the artificial immune systems (AIS) and particle swarm optimization (PSO). The proposed algorithm is applied to optimize the parameters of ADRC and then to control chaotic system. Furthermore, a new ADRC for the chaotic system is constructed. The simulation experiments indicate that the scheme requires no knowledge about the mathematical model with fast response speed, while restraining the parameter perturbation of chaotic system effectively and is robust to noise.%利用人工免疫算法及粒子群优化算法融合的优点,提出了一种免疫双态微粒群算法(immune binarystate particle swarm optimization,IBPSO)的自抗扰控制器(IBPSO-ADRC),应用于混沌系统控制,构建一种混沌系统自抗扰控制系统.实验研究表明:该控制方法无需了解动态系统精确模型,具有响应速度快,有效抑制混沌系统参数摄动及较强抗干扰能力的特点.
Unsupervised Clonal Selection Clustering Algorithm Based on Immune Algorithm%基于免疫算法的非监督克隆选择聚类算法研究
Institute of Scientific and Technical Information of China (English)
韦灵
2015-01-01
Based on the detailed analysis of clonal selection algorithm,proposes unsupervised clone selection clustering algorithm.Which is adaptive data driven by adjusting its parameters,it carries on the classification of data operations as soon as possible,improves the premature convergence problem,improves the speed of data clustering.By using several artificial and real-life data sets,comparing the performance between unsupervised clonal selection clustering algorithm K-means algorithm.The experimental results show that,this algorithm solves the K-means algorithm needs several classes of K determined in advance,and the second best value stuck faults,the classification accuracy,and it is much better than traditional K-means classification algorithm in function and with higher reliability.%在详细分析克隆选择算法的基础上,提出非监督克隆选择聚类算法.该算法是数据驱动的自适应调整其参数,它对数据进行分类的操作尽可能快,改善过早收敛的问题,提高数据聚类的速度.通过使用一些人工和现实生活中的数据集,比较非监督克隆选择聚类算法与著名的K-means算法之间的性能优劣.实验结果表明,该算法不仅解决K-means算法需事先确定的类数K和在次佳值卡住的缺点,而且在功能上比传统的K-means分类算法具有较高的分类精度和更高的可靠性.
An immunity-based technique to detect network intrusions
Institute of Scientific and Technical Information of China (English)
PAN Feng; DING Yun-fei; WANG Wei-nong
2005-01-01
This paper briefly reviews other people's works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune algorithm, multi-level negative selection algorithm, is developed. In essence, compared with Forrest's negative selection algorithm, it enhances detector generation efficiency. This algorithm integrates clonal selection process into negative selection process for the first time. After careful analyses, this algorithm was applied to network intrusion detection and achieved good results.
Institute of Scientific and Technical Information of China (English)
王毅; 孔晓琳; 牛奕龙; 齐敏; 樊养余
2012-01-01
Based on the adjustment regulation of endocrine hormone, a novel algorithm, called the hormone adjustment based quantum-inspired immune clone algorithm (HAQICA), is proposed to improve the accuracy and stability of quantum-inspired immune clone algorithm (QICA) on global optimization. In HAQICA, the clone size is calculated according to the individual fitness of the current generation and the average fitness of the previous generation, and is adjusted adaptively in terms of the population diversity and the rise law of Hill function that is the basic model of endocrine networks. HAQICA also increases the clone number of better individuals and decreases the clone number of worse individuals. Standard test functions are used to verify the algorithm, and the results of 50 random independent experiments show that the convergence speed of HAQICA is comparative with that of QICA and HAQICA is more efficient in global optimization according to the mean and variance values of optimal solutions.%为了提高量子免疫克隆算法(quantum-inspired immune clone algorithm,QICA)对函数全局寻优的精确性和稳定性,引入了内分泌激素的调节规律,根据当前个体适应度值和上一代种群的平均适应度值重新设计克隆规模,按照种群多样性和Hill函数的上升规律对其进行自适应调整,使进化各代中优秀个体的克隆得到扩增,同时减少不良个体的规模,从而提出了一种基于内分泌激素调节的量子免疫克隆算法(hormone adjustment based QICA,HAQICA).利用标准测试函数对算法进行了验证,50次随机独立实验结果表明,HAQICA算法的收敛速度与QICA算法相当,最优解的均值与方差等数据,证明了HAQICA算法在提高函数全局寻优性能上的有效性.
Detecting Resource Consumption Attack over MANET using an Artificial Immune Algorithm
Directory of Open Access Journals (Sweden)
Daud Israf
2011-09-01
Full Text Available The Human Immune System (HIS is considered as a bank of models, functions, and concepts from where Artificial Immune algorithms are inspired. These algorithms are used to secure both host-based and network-based systems. However, it is not only important to utilize the HIS in producing AIS-based algorithms as much as it is important to introduce an algorithm with high performance. Therefore, creating a balance between utilizing HIS on one side and introducing the required AIS-based intrusion detection algorithm on the other side is a crucial issue which would be valuable to investigate. Securing the mobile ad hoc network (MANET which is a collection of mobile, decentralized, and self organized nodes is another problem, which adds more challenges to the research. This is because MANET properties make it harder to be secured than the other types of static networks. We claim that AISs’ properties such as being self-healing, self-defensive and self-organizing can meet the challenges of securing the MANET environment. This paper’s objective is to utilize the biological model used in the dendritic cell algorithm (DCA to introduce a Dendritic Cell Inspired Intrusion Detection Algorithm (DCIIDA. DCIIDA is introduced to detect the Resource Consumption Attack (RCA over MANET. Furthermore, this paper proposes a DCIIDA architecture which should be applied by each node in MANET.
HISTORY BASED PROBABILISTIC BACKOFF ALGORITHM
Directory of Open Access Journals (Sweden)
Narendran Rajagopalan
2012-01-01
Full Text Available Performance of Wireless LAN can be improved at each layer of the protocol stack with respect to energy efficiency. The Media Access Control layer is responsible for the key functions like access control and flow control. During contention, Backoff algorithm is used to gain access to the medium with minimum probability of collision. After studying different variations of back off algorithms that have been proposed, a new variant called History based Probabilistic Backoff Algorithm is proposed. Through mathematical analysis and simulation results using NS-2, it is seen that proposed History based Probabilistic Backoff algorithm performs better than Binary Exponential Backoff algorithm.
A new artificial immune algorithm and its application for optimization problems
Institute of Scientific and Technical Information of China (English)
YU Zhi-gang; SONG Shen-min; DUAN Guan-ren
2006-01-01
A new artificial immune algorithm (AIA) simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies' evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem,the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount ( the iterative times). Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing multimodal optimization.
An Efficient and Effective Immune Based Classifier
Directory of Open Access Journals (Sweden)
Shahram Golzari
2011-01-01
Full Text Available Problem statement: Artificial Immune Recognition System (AIRS is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn't reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier.
Institute of Scientific and Technical Information of China (English)
陶莉莉; 孔祥东; 钟伟民; 钱锋
2012-01-01
In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.
Application of an immune algorithm to settlement prediction
Institute of Scientific and Technical Information of China (English)
Jia GUO; Jun-jie ZHENG; Yong LIU
2009-01-01
The settlement curve of the foundation endured the ramp load is an S-type curve, which is usually simulated via Poisson curve. Aimed at the difficulty of preferences in Poisson curve, an immune algorithm (IA) is used. IA is able to obtain a multiple quasi-optimum solution while maintaining the population diversity. In this paper, IA is used in an attempt to obtain accurate settlement prediction. The predicted settlements obtained by IA are compared with those predicted by the least squares fitting method (LSM), the Asaoka method and the genetic algorithm (GA). The results show that IA is a useful technique for predicting the settlement of foundations with an acceptable degree of accuracy and has much better performance than GA and the Asaoka methods.
Institute of Scientific and Technical Information of China (English)
段玉贤; 井石滚
2011-01-01
The problem of vehicle scheduling in open pit mine are analyzed to build the mathematical model of resolving the vehicle scheduling problem. The vehicle scheduling problem belongs to the NP-complete problem in fact Building the multi-objective programming is to get the minimum total volume. Finally, the application case of the vehicle scheduling optimization in Sandaozhuang open pit based on immune algorithm shows that the optimal solution can be rapidly obtained by the algorithm and it is an effective algorithm for resolving the vehicle scheduling problem.%分析露天矿车辆调度问题,建立求解矿山车辆调度问题的数学模型.矿山车辆调度问题实际上属于NP完全问题,建立多目标优化模型来求解,即解决总运量最小问题.最后,通过免疫算法在三道庄露天矿车辆调度优化中的应用实例表明,本算法可以快速求得优化解,是求解车辆调度问题的一种有效算法.
Institute of Scientific and Technical Information of China (English)
刘丽杰; 许楠; 李盼池
2012-01-01
聚焦爬虫是主题搜索引擎的核心部件。针对目前聚焦爬虫搜索策略的不足,提出基于主题相关度和页面重要性相结合的综合相关度来判别页面主题相关性,并采用自适应免疫进化算法这种搜索策略指导聚焦爬虫的爬行,实验结果证明,该算法下载的主题相关网页数所占比例明显高于最佳搜索和广度优先搜索算法的比例,具有更高的搜索效率。%Focused crawler was a core component of the topic search engine.To overcome the deficiency of focused crawler search strategy,a comprehensive value based on theme relevance and importance of page was proposed to determine the topic relevant of the page,and the adaptive immune evolutionary algorithm of this search strategy was used to guide the crawling strategy of focused crawler.The experiment results showed that the algorithm download the proportion to the number of webpage related to the themes was higher significantly than the best search and breadth first search algorithm and had higher searching efficiency.
Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji
2014-01-01
An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.
Directory of Open Access Journals (Sweden)
Liping Sun
2014-01-01
Full Text Available An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.
基于量子免疫遗传算法的火力分配优化问题%Fi re distribution and optimization based on quantum immune genetic algorithm
Institute of Scientific and Technical Information of China (English)
吴志飞; 马曲立; 翁辉; 邢焕革; 周浩
2014-01-01
In order to enhance the precision and stability of the quantum genetic algorithm ,the mecha-nism of immune clone ,immune memory and immune balance in the immune genetic algorithm is intro-duced into the quantum genetic algorithm .The problem-solving priori knowledge and local optimal in-formation are used to improve the property of the quantum genetic algorithm ,including an increase in its convergent precision and speed and its stability .In the experiments ,the effects of the antibody memory banks with different capacity on the property of the algorithm are simulated and ,the quantum immune genetic algorithm mentioned in this paper is compared with other algorithms such as the com-mon genetic algorithm ,quantum genetic algorithm ,immune and genetic algorithm as far as the differ-ent effects of fire distribution and optimization are concerned .The results indicate that the quantum immune genetic algorithm is more effective in solving the problem of fire distribution and optimization .%针对传统方法在解决火力分配优化问题时存在迭代次数多、收敛速度慢、易陷入局部极值等不足，将免疫遗传算法中的免疫克隆、免疫记忆、免疫平衡机制引入到量子遗传算法中，利用求解问题的先验知识和局部最优解信息来改善和优化量子遗传算法的性能，提高了算法的收敛精度、收敛速度和稳定性。在分析问题背景和算法实现过程的基础上，通过实例仿真，模拟了不同容量的抗体记忆库对算法性能的影响，对比了普通遗传算法、量子遗传算法、免疫遗传算法以及文中所提及的量子免疫遗传算法在解决火力分配优化问题上的不同优化效果，结果表明：该方法在解决火力分配问题时，可以有效克服早熟现象，具有收敛速度较快、稳定性较好的特性。
Institute of Scientific and Technical Information of China (English)
祁浩; 刘洲洲
2014-01-01
An algorithm of compressed sensor data reconstruction,called Q-CSDR,based on the algorithm of quantum -inspired immune clon,is proposed in this paper.Q -CSDR can increase the probability of data reconstruction through framing the data adaptively.Because of its excellent perform-ance,Q-CSDR uses the algorithm to accurately reconstruct the data.The experiment results show that, according to the sparsity of the original data,the algorithm can automatically adjust compression ratio, raise the accuracy of data reconstruction and adapt well to high sparsity data reconstruction.It is used in the field security system of Emperor Qinshihuang`s mausoleum site museum with good performance.%提出了一种基于量子免疫克隆的压缩感知数据重构算法（Q-CSDR）。算法先提出了一种能够提高数据重构概率的自适应分帧方法，然后利用量子克隆免疫算法的优化组合性能实现数据的精确重构。实验结果表明，Q-CSDR算法能够根据原始信号稀疏度自动调节压缩比率，具有重构速度快，重构精度高，能够适应于高稀疏度数据重构等优点。该算法已应用于秦始皇帝陵博物院野外文物安防系统。经实际检验，收到了良好效果。
Institute of Scientific and Technical Information of China (English)
武慧虹; 钱淑渠; 徐志丹
2013-01-01
There are some problems such as slow convergence and easy stagnation in local optima when using Genetic Algorithms ( GA) to solve high-dimensional knapsack problem. To overcome those shortcomings, a bio-inspired clonal selection immune genetic algorithm was developed to solve knapsack problem with high dimension. In the algorithm, the antibody was binary coded and the affinity of antibody was designed based on its density;in addition, the population was divided into feasible and infeasible population, and the feasible antibodies were cloned dynamically and mutated to produce the offspring population, meanwhile the infeasible antibodies were repaired towards the feasibility. The simulation experiments on the four kinds of 0/1 knapsack problem with high dimension and comparison with ETGA, RIGA and ISGA demonstrate that the proposed algorithm has better ability in handling constraints and more rapid convergence.%针对遗传算法求解高维背包问题收敛速度慢、易于陷入局部最优的缺点,基于生物免疫系统克隆选择原理,提出一种克隆选择免疫遗传算法.该算法中抗体采用二进制编码,通过抗体浓度设计抗体亲和力,进化群分离为可行群和非可行群,进化过程仅可行抗体动态克隆和突变,非可行抗体经修复算子获可行抗体.数值实验中,选取三种著名的算法用于四种高维的背包问题求解,结果表明:所提算法较其他算法具有更强的约束处理能力和快速收敛的效果.
APF-guided adaptive immune network algorithm for robot path planning
Institute of Scientific and Technical Information of China (English)
Mingxin YUAN; Sunan WANG; Canyang WU; Kunpeng LI
2009-01-01
Inspired by the mechanism of Jerne's idiotypic network hypothesis, a new adaptive immune network algorithm (AINA) is presented through the stimulation and suppression between the antigen and antibody by taking the environment and robot behavior as antigen and antibody respectively. A guiding weight is defined based on the artificial potential field (APF) method, and the guiding weight is combined with antibody vitality to construct a new antibody selection operator, which improves the searching efficiency. In addition, an updating operator of antibody vi-tality is provided based on the Baldwin effect, which results in a positive feedback mechanism of search and accelerates the convergence of the immune network. The simulation and experimental results show that the proposed algorithm is characterized by high searching speed, good convergence performance and strong planning ability, which solves the path planning well in complicated environments.
Capacitated Vehicle Routing Problem Based on Improved Immune Genetic Algorithm%基于改进免疫算法的有能力约束车辆路径问题
Institute of Scientific and Technical Information of China (English)
梁勤欧
2011-01-01
针对遗传算法、免疫遗传算法在解决车辆路径问题(VRP)中存在的问题与不足,提出了一种改进免疫遗传算法.该算法主要在检查个体的多样性程度方面进行了简化,运用多样性指数阈值控制种群个体的多样性.通过有能力约束VRP的实验验证了新算法,得到了满意的效果.%There are some problems when genetic algorithm and immune genetic algorithm are used to solve Vehicle Routing Problem ( VRP). An improved immune genetic algorithm was proposed to overcome these disadvantages. The characteristic of the improved immune genetic algorithm is that the diversity detection method was simplified and population diversity was controlled by threshold determination method. Then a simple example of capacitated vehicle routing problem was conducted, and an improved immune genetic algorithm and perfect results were obtained.
Directory of Open Access Journals (Sweden)
Bohui Zhu
2013-01-01
Full Text Available This paper presents a novel maximum margin clustering method with immune evolution (IEMMC for automatic diagnosis of electrocardiogram (ECG arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.
Institute of Scientific and Technical Information of China (English)
马国岗; 王瑞成
2014-01-01
In order to improve the efficiency, reduce the cost of aeronautical maintenance and ensure aeronautical maintenance tasks more scientiifc, reasonable and efifciently, it's necessary to study the problem of location optimization for supporting position. Through conifrming the constraint conditions, the nonlinear programming mathematical model with complex constraints of position location optimization was established, and the population individual encoding was satisifed with the constraints by using appropriate coding scheme and penalty function, the model was optimized and solved by using immune operation. An optimization algorithm based on artiifcial immune algorithm for support position location of aviation maintenance equipment was put forward and simulated. It can achieve good effects in aeronautical maintenance position location decision-making.%为了提高航空机务维修保障效率、降低保障成本以及更加科学合理、高效地进行航空机务维修保障，有必要对保障点选址优化问题进行研究。通过明确其约束条件，建立选址优化的复杂约束的非线性规划数学模型，采用适当的编码方案和罚函数使种群个体编码满足约束条件，利用免疫操作对该模型进行优化求解，提出了基于人工免疫算法的航空机务维修器材保障点优化算法，并进行了仿真。仿真结果表明，该算法在航空机务维修器材保障点选址决策中具有很好的效果。
Variables Bounding Based Retiming Algorithm
Institute of Scientific and Technical Information of China (English)
宫宗伟; 林争辉; 陈后鹏
2002-01-01
Retiming is a technique for optimizing sequential circuits. In this paper, wediscuss this problem and propose an improved retiming algorithm based on variables bounding.Through the computation of the lower and upper bounds on variables, the algorithm can signi-ficantly reduce the number of constraints and speed up the execution of retiming. Furthermore,the elements of matrixes D and W are computed in a demand-driven way, which can reducethe capacity of memory. It is shown through the experimental results on ISCAS89 benchmarksthat our algorithm is very effective for large-scale sequential circuits.
Immunity-based diagnosis for a motherboard.
Shida, Haruki; Okamoto, Takeshi; Ishida, Yoshiteru
2011-01-01
We have utilized immunity-based diagnosis to detect abnormal behavior of components on a motherboard. The immunity-based diagnostic model monitors voltages of some components, CPU temperatures, and fan speeds. We simulated abnormal behaviors of some components on the motherboard, and we utilized the immunity-based diagnostic model to evaluate motherboard sensors in two experiments. These experiments showed that the immunity-based diagnostic model was an effective method for detecting abnormal behavior of components on the motherboard.
Immunity-Based Diagnosis for a Motherboard
Yoshiteru Ishida; Takeshi Okamoto; Haruki Shida
2011-01-01
We have utilized immunity-based diagnosis to detect abnormal behavior of components on a motherboard. The immunity-based diagnostic model monitors voltages of some components, CPU temperatures, and fan speeds. We simulated abnormal behaviors of some components on the motherboard, and we utilized the immunity-based diagnostic model to evaluate motherboard sensors in two experiments. These experiments showed that the immunity-based diagnostic model was an effective method for detecting abnormal...
Immunity-Based Diagnosis for a Motherboard
Yoshiteru Ishida; Takeshi Okamoto; Haruki Shida
2011-01-01
We have utilized immunity-based diagnosis to detect abnormal behavior of components on a motherboard. The immunity-based diagnostic model monitors voltages of some components, CPU temperatures, and fan speeds. We simulated abnormal behaviors of some components on the motherboard, and we utilized the immunity-based diagnostic model to evaluate motherboard sensors in two experiments. These experiments showed that the immunity-based diagnostic model was an effective method for detecting abnormal...
Immunity Based Worm Detection System
Institute of Scientific and Technical Information of China (English)
HONG Zheng; WU Li-fa; WANG Yuan-yuan
2007-01-01
Current worm detection methods are unable to detect multi-vector polymorphic worms effectively.Based on negative selection mechanism of the immune system,a local network worm detection system that detects worms was proposed.Normal network service requests were represented by self-strings,and the detection system used self-strings to monitor the network for anomaly.According to the properties of worm propagation,a control center correlated the anomalies detected in the form of binary trees to ensure the accuracy of worm detection.Experiments show the system to be effective in detecting the traditional as well as multi-vector polymorphic worms.
Optimizing Mining Association Rules for Artificial Immune System based Classification
Directory of Open Access Journals (Sweden)
SAMEER DIXIT
2011-08-01
Full Text Available The primary function of a biological immune system is to protect the body from foreign molecules known as antigens. It has great pattern recognition capability that may be used to distinguish between foreigncells entering the body (non-self or antigen and the body cells (self. Immune systems have many characteristics such as uniqueness, autonomous, recognition of foreigners, distributed detection, and noise tolerance . Inspired by biological immune systems, Artificial Immune Systems have emerged during the last decade. They are incited by many researchers to design and build immune-based models for a variety of application domains. Artificial immune systems can be defined as a computational paradigm that is inspired by theoretical immunology, observed immune functions, principles and mechanisms. Association rule mining is one of the most important and well researched techniques of data mining. The goal of association rules is to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in thetransaction databases or other data repositories. Association rules are widely used in various areas such as inventory control, telecommunication networks, intelligent decision making, market analysis and risk management etc. Apriori is the most widely used algorithm for mining the association rules. Other popular association rule mining algorithms are frequent pattern (FP growth, Eclat, dynamic itemset counting (DIC etc. Associative classification uses association rule mining in the rule discovery process to predict the class labels of the data. This technique has shown great promise over many other classification techniques. Associative classification also integrates the process of rule discovery and classification to build the classifier for the purpose of prediction. The main problem with the associative classification approach is the discovery of highquality association rules in a very large space of
Institute of Scientific and Technical Information of China (English)
周悦; 唐世; 贾雪松; 张东伟; 臧传治
2013-01-01
For the structure health monitoring,this paper studies the structural damage detection and classification problems using the artificial immune system which has the extremely powerful capabilities of autonomy , initiative, adaptive and the bionic principle between learning and memory. An artificial immune pattern recognition and structural detection classification algorithm based on diagonal distance is proposed through imitating the immune recognition and learning mechanism. With the structure of benchmark proposed by the IASC-ASCE SHM working group as the platform, the damage detection and classification are tested. The simulation results show the classification rate based on the diagonal distance is better than Euclidean and Ma-halanobis. The relationship between the classification rate and the parameters which are clone rate and memory cell replacement threshold value is tested based on the diagonal distance, which show that the cloning rate should try to choose suitable parameter values in order to get a better classification success rate. The algorithm based on the immune learning and evolution can produce the high quality memory cells which effectively identify all kinds of structural damage model.%目的 研究人工免疫系统的自治性、主动性、自适应及学习和记忆的仿生机理,来解决结构健康监测中的结构损伤识别和分类问题.方法 通过模仿免疫识别和学习机理,提出一种基于Diagonal距离的人工免疫模式识别的结构损伤分类算法,并在IASC-ASCE SHM工作小组所提出的benchmark模型上对结构模式分类进行了实验测试.结果 仿真实验表明基于Diagonal距离所得到的分类成功率要略高于Euclidean距离和Mahalanobis距离所得到的分类成功率；基于Diagonal距离研究了克隆率和记忆细胞替代阈值对分类成功率的影响,只要选取合适的参数值,就能获得较高的分类成功率.结论 基于Diagonal距离的人工免疫模式识别的
A effective immune multi-objective algorithm for SAR imagery segmentation
Yang, Dongdong; Jiao, Licheng; Gong, Maoguo; Si, Xiaoyun; Li, Jinji; Feng, Jie
2009-10-01
A novel and effective immune multi-objective clustering algorithm (IMCA) is presented in this study. Two conflicting and complementary objectives, called compactness and connectedness of clusters, are employed as optimization targets. Besides, adaptive ranks clone, variable length chromosome crossover operation and k-nearest neighboring list based diversity holding strategies are featured by the algorithm. IMCA could automatically discover the right number of clusters with large probability. Seven complicated artificial data sets and two widely used synthetic aperture radar (SAR) imageries are used for test IMCA. Compared with FCM and VGA, IMCA has obtained good and encouraging clustering results. We believe that IMCA is an effective algorithm for solving these nine problems, which should deserve further research.
Immune Algorithm for Solving the Optimization Problems of Computer Communication Networks
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The basic problem in optimizing communication networks is to assign a proper circuit for each origindestination pair in networks so as to minimize the average network delay, and the network optimal route selection model is a multi-constrained 0-1 nonlinear programming problem. In this paper, a new stochastic optimization algorithm, Immune Algorithm, is applied to solve the optimization problem in communication networks. And the backbone network vBNS is chosen to illustrate the technique of evaluating delay in a virtual network. At last, IA is compared with the optimization method in communication networks based on Genetic Algorithm, and the result shows that IA is better than GA in global optimum finding.
Evolutionary algorithm based index assignment algorithm for noisy channel
Institute of Scientific and Technical Information of China (English)
李天昊; 余松煜
2004-01-01
A globally optimal solution to vector quantization (VQ) index assignment on noisy channel, the evolutionary algorithm based index assignment algorithm (EAIAA), is presented. The algorithm yields a significant reduction in average distortion due to channel errors, over conventional arbitrary index assignment, as confirmed by experimental results over the memoryless binary symmetric channel (BSC) for any bit error.
Hybrid Heuristic-Based Artificial Immune System for Task Scheduling
sanei, Masoomeh
2011-01-01
Task scheduling problem in heterogeneous systems is the process of allocating tasks of an application to heterogeneous processors interconnected by high-speed networks, so that minimizing the finishing time of application as much as possible. Tasks are processing units of application and have precedenceconstrained, communication and also, are presented by Directed Acyclic Graphs (DAGs). Evolutionary algorithms are well suited for solving task scheduling problem in heterogeneous environment. In this paper, we propose a hybrid heuristic-based Artificial Immune System (AIS) algorithm for solving the scheduling problem. In this regard, AIS with some heuristics and Single Neighbourhood Search (SNS) technique are hybridized. Clonning and immune-remove operators of AIS provide diversity, while heuristics and SNS provide convergence of algorithm into good solutions, that is balancing between exploration and exploitation. We have compared our method with some state-of-the art algorithms. The results of the experiments...
Artificial immune algorithm implementation for optimized multi-axis sculptured surface CNC machining
Fountas, N. A.; Kechagias, J. D.; Vaxevanidis, N. M.
2016-11-01
This paper presents the results obtained by the implementation of an artificial immune algorithm to optimize standard multi-axis tool-paths applied to machine free-form surfaces. The investigation for its applicability was based on a full factorial experimental design addressing the two additional axes for tool inclination as independent variables whilst a multi-objective response was formulated by taking into consideration surface deviation and tool path time; objectives assessed directly from computer-aided manufacturing environment A standard sculptured part was developed by scratch considering its benchmark specifications and a cutting-edge surface machining tool-path was applied to study the effects of the pattern formulated when dynamically inclining a toroidal end-mill and guiding it towards the feed direction under fixed lead and tilt inclination angles. The results obtained form the series of the experiments were used for the fitness function creation the algorithm was about to sequentially evaluate. It was found that the artificial immune algorithm employed has the ability of attaining optimal values for inclination angles facilitating thus the complexity of such manufacturing process and ensuring full potentials in multi-axis machining modelling operations for producing enhanced CNC manufacturing programs. Results suggested that the proposed algorithm implementation may reduce the mean experimental objective value to 51.5%
Institute of Scientific and Technical Information of China (English)
李雪竹
2014-01-01
For the real-time warehousing logistics vehicle scheduling problem(LVCP), an RFID-enabled vehicle dynamic scheduling algorithm based on Immune Glowworm Swarm Optimization Algorithm(IGSOA)is proposed. A mathematical model for Vehicle Routing Problem(VRP)with delivery cost is established, and the IGSOA is used to solve this model. IGSOA combines the GSO and CSA technology, and adopts a multi-layer evolution pattern. The polymorphic adaptive population mechanism and global extreme screening strategy are introduced in the low GSO operation and high immune operation, in order to improve the IGSOA convergence efficiency. Based on above analysis, a vehicle dynamic scheduling framework is presented, and the vehicle dynamic scheduling process is divided into two stages as vehicle scheduling tasks control and VRP optimization. The process of LVCP is given. Experimental results show that, the IGSOA can effectively solve large-scale LVCP.%针对物流配送实时仓储车辆调度问题，提出了一种基于RFID技术的免疫萤火虫车辆动态调度框架。建立了基于配送成本的带约束条件车辆路径问题数学模型，运用免疫萤火虫优化算法求解该模型，免疫萤火虫优化算法将萤火虫优化及免疫克隆技术融合，采用多层进化模式，在低层萤火虫操作中及高层免疫操作中分别引入多态子种群自适应机制和全局极值筛选策略，以提高算法全局收敛效率，在此基础上设计了仓储车辆动态调度框架，将车辆动态调度过程分为车辆调度任务控制和路径优化两个阶段，给出了车辆动态调度任务处理流程。实验仿真表明，该车辆动态调度算法能够有效地解决大规模动态物流车辆调度问题。
Institute of Scientific and Technical Information of China (English)
李远利; 李著信; 刘书俊
2012-01-01
The defect recognition of metal pipeline was generally based on sinai signal feature in magnetic memory testing. This method has high false detection rate and low reliability. To achieve accurate recognition of pipeline defect, a recognition model based on fuzzy-immune algorithm was established to analysis the vector combined with multiple signal features. The experiments show that the pipeline defect can be recognised by this method.%在金属管道的磁记忆检测中，管道缺陷状态的识别普遍是基于单个检测信号特征值来进行分析的。这种方法存在误检率高、可靠性低等问题。为此，构建了一个基于模糊免疫算法的智能识别模型，将磁记忆检测信号多个特征值组合为一个向量，作为识别模型的输入向量，以分析识别管道缺陷的状况。实验表明，该方法能够对管道缺陷进行准确、有效的识别。
Application of detecting algorithm based on network
Institute of Scientific and Technical Information of China (English)
张凤斌; 杨永田; 江子扬; 孙冰心
2004-01-01
Because currently intrusion detection systems cannot detect undefined intrusion behavior effectively,according to the robustness and adaptability of the genetic algorithms, this paper integrates the genetic algorithms into an intrusion detection system, and a detection algorithm based on network traffic is proposed. This algorithm is a real-time and self-study algorithm and can detect undefined intrusion behaviors effectively.
Diversity-Based Boosting Algorithm
Directory of Open Access Journals (Sweden)
Jafar A. Alzubi
2016-05-01
Full Text Available Boosting is a well known and efficient technique for constructing a classifier ensemble. An ensemble is built incrementally by altering the distribution of training data set and forcing learners to focus on misclassification errors. In this paper, an improvement to Boosting algorithm called DivBoosting algorithm is proposed and studied. Experiments on several data sets are conducted on both Boosting and DivBoosting. The experimental results show that DivBoosting is a promising method for ensemble pruning. We believe that it has many advantages over traditional boosting method because its mechanism is not solely based on selecting the most accurate base classifiers but also based on selecting the most diverse set of classifiers.
Carbohydrate-based immune adjuvants
Petrovsky, Nikolai; Cooper, Peter D
2011-01-01
The role for adjuvants in human vaccines has been a matter of vigorous scientific debate, with the field hindered by the fact that for over 80 years, aluminum salts were the only adjuvants approved for human use. To this day, alum-based adjuvants, alone or combined with additional immune activators, remain the only adjuvants approved for use in the USA. This situation has not been helped by the fact that the mechanism of action of most adjuvants has been poorly understood. A relative lack of resources and funding for adjuvant development has only helped to maintain alum’s relative monopoly. To seriously challenge alum’s supremacy a new adjuvant has many major hurdles to overcome, not least being alum’s simplicity, tolerability, safety record and minimal cost. Carbohydrate structures play critical roles in immune system function and carbohydrates also have the virtue of a strong safety and tolerability record. A number of carbohydrate compounds from plant, bacterial, yeast and synthetic sources have emerged as promising vaccine adjuvant candidates. Carbohydrates are readily biodegradable and therefore unlikely to cause problems of long-term tissue deposits seen with alum adjuvants. Above all, the Holy Grail of human adjuvant development is to identify a compound that combines potent vaccine enhancement with maximum tolerability and safety. This has proved to be a tough challenge for many adjuvant contenders. Nevertheless, carbohydrate-based compounds have many favorable properties that could place them in a unique position to challenge alum’s monopoly over human vaccine usage. PMID:21506649
Reinforcement Learning Based Artificial Immune Classifier
Directory of Open Access Journals (Sweden)
Mehmet Karakose
2013-01-01
Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.
Optimization and realization of IIR digital filter based on immune algorithms%基于免疫算法的IIR数字滤波器优化与实现
Institute of Scientific and Technical Information of China (English)
倪龙
2011-01-01
由于IIR数字滤波器设计实质上是一个非线性高维复杂函数优化问题,文中提出基于具有全局搜索能力强,收敛速度快特点的免疫算法实现IIR数字滤波器优化设计的新方法,给出了IIR滤波器优化设计的数学模型,描述了应用免疫算法优化设计IIR数字滤波器的具体实现步骤.通过低通和高通IIR数字滤波器设计的仿真结果表明方法的有效性和高效性.%Based on optimization design for IIR digital filters being a non-linear and high-dimension complex function optimization problem, immune algorithms (IA) , which has the characteristics of more powerful global and rpider convergence is applied on IIR digital filter optimization design in this paper.Firstly, the maths model of IIR digital filter optimization design is proposed. Secondly, the process of optimization design for IIR digital filters on the basis ofIA is described in detail, finally, the validity and effectivenss of the introduced method are demonstrated by experimental results on the lowpass and highpass IIR digital filters.
Institute of Scientific and Technical Information of China (English)
罗毅; 多靖赟
2012-01-01
结合免疫记忆学说和克隆选择原理,提出了一种解决多目标无功优化问题的免疫记忆克隆选择算法.该算法针对多目标无功优化问题的特点,采用以拥挤距离为适应度的自适应克隆方式,实现了种群的扩张,保证了所得解集的均匀性；引入非一致性变异算子,使该算法同时具备全局均匀搜索能力和局部精确寻优能力；采用交叉重组算子实现了抗体间的协作,促进不同抗体间信息的交流;通过抗体群更新操作,一方面保证了算法的收敛速度,另一方面确保了所得解集均匀分布；引入记忆单元概念,可以有效抑制寻优过程中出现的退化现象,确保了种群的多样性.以IEEE-14和IEEE-118节点测试系统为例进行仿真计算,结果表明该算法可以有效提高系统运行的安全性和经济性,是求解多目标无功优化问题的有效方法.%Based on immune memory theory and colonial selection principle, a new immune memory colonial selection algorithm is put forward to solve the problem of multi-objective reactive power optimization. Crowded distance is used as fitness in the adaptive cloning operation to realize the population expansion and ensure the uniformity of solution sets. By use of non consistent variation operation, the algorithm has both global well-distributed search ability and local accurate optimization ability. The cross restructuring operator can promote the information communication between different antibodies. It effectively increases the speed of the algorithm and ensures the uniformity of solution sets by use of antibody group update operation. The introduction of memory unit concept has contributed to suppression of degeneration, ensuring the diversity of population. IEEE-14 bus system and IEEE-118 bus system are used to verify the performance of the proposed algorithm, and the results show that it is an effective method for multi-objective reactive power optimization.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determi...
Immunity-Based Diagnosis for a Motherboard
Directory of Open Access Journals (Sweden)
Yoshiteru Ishida
2011-04-01
Full Text Available We have utilized immunity-based diagnosis to detect abnormal behavior of components on a motherboard. The immunity-based diagnostic model monitors voltages of some components, CPU temperatures, and fan speeds. We simulated abnormal behaviors of some components on the motherboard, and we utilized the immunity-based diagnostic model to evaluate motherboard sensors in two experiments. These experiments showed that the immunity-based diagnostic model was an effective method for detecting abnormal behavior of components on the motherboard.
基于种群分类的变尺度免疫克隆选择算法%Mutative Scale Immune Clonal Selection Algorithm Based on Multi-population
Institute of Scientific and Technical Information of China (English)
郭忠全; 王振国; 颜力
2011-01-01
Mutative Scale Immune Clonal Selection Algorithm (MSICSA) based on Multi-population is proposed. In the algorithm, the dominant position of global optimal solution was highlighted by the nonlinear scale transformation of objective function. Memory sub-population was extracted to exchange information between populations. Antibody population was divided into elite, normal and inferior sub-population. To enhance local and global search capabilities of MSICSA, adaptive Gaussian and uniform mutation were applied to elite and normal sub-population respectively and the inferior antibody was extinguished and replaced by new ones. By introducing the niche technology to increase the diversity of population distribution, the algorithm can prevent premature. Test functions and a space antenna optimization were tested. The results show that the optimization capability of MSICSA is more advanced than CLONALG and SCA, and the computational complexity is reduced.%提出了一种基于种群分类的变尺度免疫克隆选择算法.该算法通过对目标函数进行非线性尺度变换,突出了全局最优解的优势地位；建立记忆子群实现了种群代际进化信息的交换；依据亲和度将抗体分为精英子群、普通子群、劣等子群,并对其分别执行自适应高斯变异、均匀变异和消亡更新等策略,增强了算法的局部和全局搜索能力.引入小生境技术提高了抗体分布的多样性,进而克服了算法的早熟.采用经典测试函数和星载天线结构优化问题对算法进行了测试,测试结果表明本算法寻优能力较经典克隆选择算法和标准遗传算法有较大改善,且计算复杂度并无显著增加.
Optimization of S-surface controller for autonomous underwater vehicle with immune-genetic algorithm
Institute of Scientific and Technical Information of China (English)
LI Ye; ZHANG Lei; WAN Lei; LIANG Xiao
2008-01-01
To deduce error and fussy work of manual adjustment of parameters for an S-surface controller in underwater vehicle motion control, the immune-genetic optimization of S-surface controller of an underwater vehicle was proposed. The ability of producing various antibodies for the immune algorithm, the self-adjustment of antibody density, and the antigen immune memory were used to realize the rapid convergence of S-surface controller parameters. It avoided loitering near the local peak value. Deduction of the S-surface controller was given. General process of the immune-genetic algorithm was described and immune-genetic optimization of S-surface controller parameters was discussed. Definitive results were obtained from many simulation experiments and lake experiments, which indicate that the algorithm can get good effect in optimizing the nonlinear motion controller parameters of an underwater vehicle.
Xu, Ye; Wang, Ling; Wang, Shengyao; Liu, Min
2014-09-01
In this article, an effective hybrid immune algorithm (HIA) is presented to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, a decoding method is proposed to transfer a job permutation sequence to a feasible schedule considering both factory dispatching and job sequencing. Secondly, a local search with four search operators is presented based on the characteristics of the problem. Thirdly, a special crossover operator is designed for the DPFSP, and mutation and vaccination operators are also applied within the framework of the HIA to perform an immune search. The influence of parameter setting on the HIA is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on 420 small-sized instances and 720 large-sized instances are provided. The effectiveness of the HIA is demonstrated by comparison with some existing heuristic algorithms and the variable neighbourhood descent methods. New best known solutions are obtained by the HIA for 17 out of 420 small-sized instances and 585 out of 720 large-sized instances.
Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping
Institute of Scientific and Technical Information of China (English)
LI Mei-yi; CAI Zi-xing
2006-01-01
Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44 % higher than those of other algorithms.
Institute of Scientific and Technical Information of China (English)
杨淑莹; 刘旭鹏; 陶冲; 刘婷婷
2014-01-01
在矢量量化的码书设计过程中，针对传统的 LBG算法对初始码书选取的依赖性及易陷入局部最优的缺陷，提出基于免疫猫群优化算法的矢量量化码书设计。将整个种群分为搜索组和跟踪组，运用克隆扩增算子在搜寻组中进行局部搜索，根据适应度值大小调节变异个体数目，保持解的多样性。运用动态疫苗提取与接种算子使跟踪组个体基因与疫苗进行交叉变异，向最优解靠拢，防止无监督交叉变异可能引起的退化现象。通过浓度平衡算子和选择算子更新子代种群，防止种群“早熟”。将训练出全局最优码书输入到HMM模型进行训练和识别，实验结果表明，基于免疫猫群优化算法的矢量量化码书设计不依赖于初始码书选取，鲁棒性强且降低语音识别误差率。%In the process of codebook design, traditional LBG algorithm is often used for vector quantization which depends on the initial codebook selection and easily falls into local optimum. A vector quantization codebook design method based on immune cat swarm optimization algorithm ( ICSO) is proposed to solve the problems. The population is divided into searching group and tracking group. Clonal expansion operator is used for local search in the searching group, and the number of mutation individual is adjusted according to the fitness value. Moreover, dynamic vaccine extraction and vaccination operator are used for global search in the tracking group. The crossover and mutation between individual gene and vaccine make the result close to the optimal solution, and the descendant population is updated through the balance of concentration equilibrium operator and selection operator. Finally, the optimal codebook is obtained from the training vectors by the proposed algorithm and is inputted to the HMM model for training and recognition. The simulation results show that the proposed algorithm does not depend on the
AN SVAD ALGORITHM BASED ON FNNKD METHOD
Institute of Scientific and Technical Information of China (English)
Chen Dong; Zhang Yan; Kuang Jingming
2002-01-01
The capacity of mobile communication system is improved by using Voice Activity Detection (VAD) technology. In this letter, a novel VAD algorithm, SVAD algorithm based on Fuzzy Neural Network Knowledge Discovery (FNNKD) method is proposed. The performance of SVAD algorithm is discussed and compared with traditional algorithm recommended by ITU G.729B in different situations. The simulation results show that the SVAD algorithm performs better.
Institute of Scientific and Technical Information of China (English)
林菁; 江琳
2012-01-01
针对粒子群算法易陷入局部最优值的缺点，将免疫原理引入粒子群算法中，利用免疫记忆与自我调节机制促使各适应度层次的粒子维持一定浓度，保证群体的多样性，从而避免算法陷入局部最优。随后将这种改进的算法应用于支持向量机参数的选择，并在BreaStCancer等数据集上进行了实验，实验结果表明利用免疫粒子群算法选取支持向量机最优参数，能够提高支持向量机的分类正确率，具有一定的实用性，特别在经济金融应用上前景可观。%To avoid trapping into local optimization of Particle Swarm Optimization (PSO) algorithm, the principle of immune was introduced to improve the PSO algorithm for searching the optimal parameters of support vector machines (SVM).The improved method utilized the function of immune memory and the self adjustment mechanism to maintain the concentration of particles at a certain level in every layer to guarantee the diversity of population. So it avoided the problem of local optimization. The improved algorithm was verified with the Breast Cancer, Ionosphere and German datasets. The results demonstrate that the algorithm can improve the overall performance of SVM classifier and its application in the field of finance will lead to prosperous future.
A New Page Ranking Algorithm Based On WPRVOL Algorithm
Directory of Open Access Journals (Sweden)
Roja Javadian Kootenae
2013-03-01
Full Text Available The amount of information on the web is always growing, thus powerful search tools are needed to search for such a large collection. Search engines in this direction help users so they can find their desirable information among the massive volume of information in an easier way. But what is important in the search engines and causes a distinction between them is page ranking algorithm used in them. In this paper a new page ranking algorithm based on "Weighted Page Ranking based on Visits of Links (WPRVOL Algorithm" for search engines is being proposed which is called WPR'VOL for short. The proposed algorithm considers the number of visits of first and second level in-links. The original WPRVOL algorithm takes into account the number of visits of first level in-links of the pages and distributes rank scores based on the popularity of the pages whereas the proposed algorithm considers both in-links of that page (first level in-links and in-links of the pages that point to it (second level in-links in order to calculation of rank of the page, hence more related pages are displayed at the top of search result list. In the summary it is said that the proposed algorithm assigns higher rank to pages that both themselves and pages that point to them be important.
DNA Coding Based Knowledge Discovery Algorithm
Institute of Scientific and Technical Information of China (English)
LI Ji-yun; GENG Zhao-feng; SHAO Shi-huang
2002-01-01
A novel DNA coding based knowledge discovery algorithm was proposed, an example which verified its validity was given. It is proved that this algorithm can discover new simplified rules from the original rule set efficiently.
Artificial immunity-based induction motor bearing fault diagnosis
Hakan ÇALIŞ; ÇAKIR, Abdülkadir; Emre DANDIL
2013-01-01
In this study, the artificial immunity of the negative selection algorithm is used for bearing fault detection. It is implemented in MATLAB-based graphical user interface software. The developed software uses amplitudes of the vibration signal in the time and frequency domains. Outer, inner, and ball defects in the bearings of the induction motor are detected by anomaly monitoring. The time instants of the fault occurrence and fault level are determined according to the number of a...
An immune based dynamic intrusion detection model
Institute of Scientific and Technical Information of China (English)
LI Tao
2005-01-01
With the dynamic description method for self and antigen, and the concept of dynamic immune tolerance for lymphocytes in network-security domain presented in this paper, a new immune based dynamic intrusion detection model (Idid) is proposed. In Idid, the dynamic models and the corresponding recursive equations of the lifecycle of mature lymphocytes, and the immune memory are built. Therefore, the problem of the dynamic description of self and nonself in computer immune systems is solved, and the defect of the low efficiency of mature lymphocyte generating in traditional computer immune systems is overcome. Simulations of this model are performed, and the comparison experiment results show that the proposed dynamic intrusion detection model has a better adaptability than the traditional methods.
A multi-objective evolutionary algorithm for protein structure prediction with immune operators.
Judy, M V; Ravichandran, K S; Murugesan, K
2009-08-01
Genetic algorithms (GA) are often well suited for optimisation problems involving several conflicting objectives. It is more suitable to model the protein structure prediction problem as a multi-objective optimisation problem since the potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms) and experiments have shown that those types of interactions are in conflict, by using the potential energy function, Chemistry at Harvard Macromolecular Mechanics. In this paper, we have modified the immune inspired Pareto archived evolutionary strategy (I-PAES) algorithm and denoted it as MI-PAES. It can effectively exploit some prior knowledge about the hydrophobic interactions, which is one of the most important driving forces in protein folding to make vaccines. The proposed MI-PAES is comparable with other evolutionary algorithms proposed in literature, both in terms of best solution found and the computational time and often results in much better search ability than that of the canonical GA.
SIFT based algorithm for point feature tracking
Directory of Open Access Journals (Sweden)
Adrian BURLACU
2007-12-01
Full Text Available In this paper a tracking algorithm for SIFT features in image sequences is developed. For each point feature extracted using SIFT algorithm a descriptor is computed using information from its neighborhood. Using an algorithm based on minimizing the distance between two descriptors tracking point features throughout image sequences is engaged. Experimental results, obtained from image sequences that capture scaling of different geometrical type object, reveal the performances of the tracking algorithm.
Neural Network-Based Hyperspectral Algorithms
2016-06-07
Neural Network-Based Hyperspectral Algorithms Walter F. Smith, Jr. and Juanita Sandidge Naval Research Laboratory Code 7340, Bldg 1105 Stennis Space...our effort is development of robust numerical inversion algorithms , which will retrieve inherent optical properties of the water column as well as...validate the resulting inversion algorithms with in-situ data and provide estimates of the error bounds associated with the inversion algorithm . APPROACH
A Web Service Evolution Framework Based on Quantum Immune Evolution Algorithm%基于量子免疫进化算法的Web服务演化框架
Institute of Scientific and Technical Information of China (English)
王萌; 李蜀瑜
2012-01-01
如今Web服务在网络中被广泛应用,但随着需求变更、系统升级等变化的出现,Web服务如何进行相应的发展演化就成为了一个很重要的问题.为此,文中提出量子免疫进化算法的Web服务演化框架,该策略采用量子编码和量子进化操作优化服务选择,从而为服务演化的自动管理、控制提供了良好的知识基础,提高了整体Web服务的质量.为验证方法的有效性,进行了仿真实验分析,与在组合服务中常用的HTN规划方法进行了比较,实验结果表明文中所提方法在整体服务的可用性上更为优秀.%Today,Web services are widely used in the network,but as many variations appearanced,such as demand changes,system upgrades , how to make Web service correspondingly develop has become a very important issue. Thus, propose a model of Web service evolution framework which is based on quantum immune evolution algorithm ( QIEA) , and also introduce the methods of quantum coding and quantum evolution operations to optimize the service choices. Generally speaking, it provides a favorable knowledge foundation for the automatic management and control of service evolution which enhances the quality of Web service. In order to inspect and verify the effectiveness of the method, have executed the simulation experiment and compared with the HTN planning method often used in service composition. The experiment results show that the method proposed plays better in the usability.
Algorithmic governmentality: radicalisation and immune strategy of capitalism and neoliberalism?
Directory of Open Access Journals (Sweden)
Antoinette Rouvroy
2016-11-01
Full Text Available This article is a set of reflections on the question: ‘what is completely new in algorithmic governmentality compared to capitalism and neoliberalism?’ The following text is thus some preliminary, temporary and definitively uncertain intuitions in response to this question.
A New Page Ranking Algorithm Based On WPRVOL Algorithm
Roja Javadian Kootenae; Seyyed Mohsen Hashemi; mehdi afzali
2013-01-01
The amount of information on the web is always growing, thus powerful search tools are needed to search for such a large collection. Search engines in this direction help users so they can find their desirable information among the massive volume of information in an easier way. But what is important in the search engines and causes a distinction between them is page ranking algorithm used in them. In this paper a new page ranking algorithm based on "Weighted Page Ranking based on Visits of ...
Kernel method-based fuzzy clustering algorithm
Institute of Scientific and Technical Information of China (English)
Wu Zhongdong; Gao Xinbo; Xie Weixin; Yu Jianping
2005-01-01
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
Institute of Scientific and Technical Information of China (English)
陈丽安; 张培铭; 缪希仁
2003-01-01
文章用一种免疫遗传算法(Immune Genetic Algorithm,简称IGA)对智能化电磁电器进行结构及控制参数优化设计.该算法在保留基本遗传算法(Simple Genetic Algorithm,简称SGA)随机全局搜索能力的基础上,引进了生物免疫系统中的抗原记忆、抗体促进与抑制、抗体多样性保持等机制.实验结果表明,基于免疫原理的遗传算法可有效改善基本遗传算法未成熟收敛等缺陷,提高全局搜索的效率及能力,在智能化电磁电器全局优化设计中取得了满意的结果.
Institute of Scientific and Technical Information of China (English)
殷月
2016-01-01
In this paper, we analyzed the various costs contained in the location allocation of a distribution center, established the decision model to minimize the location allocation cost of the distribution center, then considering the characteristics of the immunity algorithm, used it to solve the model, and at the end, in connection with an empirical case, demonstrated the feasibility and practicality of the model and algorithm in the location allocation of cold chain logistics distribution centers.%分析了配送中心选址包含的各种成本,建立配送中心选址费用最小决策模型,考虑免疫优化算法具有全局搜索能力及高度收敛性的特点,运用免疫优化算法对模型进行优化求解,结合具体算例验证该模型及算法在冷链物流配送中心选址问题中的可行性及实用性,为物流企业的实际决策操作提供参考.
A Survey of Artificial Immune System Based Intrusion Detection
Directory of Open Access Journals (Sweden)
Hua Yang
2014-01-01
Full Text Available In the area of computer security, Intrusion Detection (ID is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS. The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs. This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.
ILU preconditioning based on the FAPINV algorithm
Directory of Open Access Journals (Sweden)
Davod Khojasteh Salkuyeh
2015-01-01
Full Text Available A technique for computing an ILU preconditioner based on the factored approximate inverse (FAPINV algorithm is presented. We show that this algorithm is well-defined for H-matrices. Moreover, when used in conjunction with Krylov-subspace-based iterative solvers such as the GMRES algorithm, results in reliable solvers. Numerical experiments on some test matrices are given to show the efficiency of the new ILU preconditioner.
Immune based computer virus detection approaches
Institute of Scientific and Technical Information of China (English)
TAN Ying; ZHANG Pengtao
2013-01-01
The computer virus is considered one of the most horrifying threats to the security of computer systems worldwide.The rapid development of evasion techniques used in virus causes the signature based computer virus detection techniques to be ineffective.Many novel computer virus detection approaches have been proposed in the past to cope with the ineffectiveness,mainly classified into three categories:static,dynamic and heuristics techniques.As the natural similarities between the biological immune system (BIS),computer security system (CSS),and the artificial immune system (AIS) were all developed as a new prototype in the community of anti-virus research.The immune mechanisms in the BIS provide the opportunities to construct computer virus detection models that are robust and adaptive with the ability to detect unseen viruses.In this paper,a variety of classic computer virus detection approaches were introduced and reviewed based on the background knowledge of the computer virus history.Next,a variety of immune based computer virus detection approaches were also discussed in detail.Promising experimental results suggest that the immune based computer virus detection approaches were able to detect new variants and unseen viruses at lower false positive rates,which have paved a new way for the anti-virus research.
Multicast Routing Based on Hybrid Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
CAO Yuan-da; CAI Gui
2005-01-01
A new multicast routing algorithm based on the hybrid genetic algorithm (HGA) is proposed. The coding pattern based on the number of routing paths is used. A fitness function that is computed easily and makes algorithm quickly convergent is proposed. A new approach that defines the HGA's parameters is provided. The simulation shows that the approach can increase largely the convergent ratio, and the fitting values of the parameters of this algorithm are different from that of the original algorithms. The optimal mutation probability of HGA equals 0.50 in HGA in the experiment, but that equals 0.07 in SGA. It has been concluded that the population size has a significant influence on the HGA's convergent ratio when it's mutation probability is bigger. The algorithm with a small population size has a high average convergent rate. The population size has little influence on HGA with the lower mutation probability.
Directory of Open Access Journals (Sweden)
Bogna MRÓWCZYŃSKA
2011-01-01
Full Text Available This paper describes an application of an evolutionary algorithm and an artificial immune systems to solve a problem of scheduling an optimal route for waste disposal garbage trucks in its daily operation. Problem of an optimisation is formulated and solved using both methods. The results are presented for an area in one of the Polish cities.
DEFF Research Database (Denmark)
Kannan, Devika; Govindan, Kannan; Soleimani, Hamed
2014-01-01
approaches are unable to solve real-world instances of such NP-hard problems in a reasonable time. These approaches involve cumbersome computational steps in real-size cases. In order to solve the mixed integer linear programming model, we develop an artificial immune system and a sheep flock algorithm...
Research on micro-strip reconfigurable antenna based on immune algorithm%基于免疫算法的多频可重构微带天线设计
Institute of Scientific and Technical Information of China (English)
许朝阳; 李媛; 李建兰
2011-01-01
频率可重构天线可以在宽频带或者超宽频带范围内改变频率而以近似相同的方向图进行工作,这对当前许多移动通信系统天线的小型化具有重要意义.介绍了免疫算法的机理,通过将1个简单的矩形微带贴片天线划分成若干小的贴片元,并应用免疫算法优化求解响应于需求的最合适的二进制序列(对应就是小贴片的存在与否的情况)来控制天线的可重构特性,设计出了1种可以在8～17 GHz范围内实现频率捷变的天线可重构方案,使天线在很宽频率范围内能够实现了多频或双频可重构.%Frequency reconfigurable antenna can change the frequency in a wide or ultra wide band but worked with nearly the same pattern,and this has important meaning to the antenna miniaturization of current mobile communication systems. Firstly, we described the mechanism of immune algorithm. And then, by dividing a simple rectangular micro-strip patch antenna into several small patches and using immune algorithm to solve the most appropriate binary sequence (the existence of small patch or not) which is response to demand, we controlled the reconfigurable features of antenna, designed a antenna which can achieve frequency agility within the range of 8-17 GHz, and made the antenna realized multi-band or dual-band reconfigurable in a very wide frequency range.
An immunity based network security risk estimation
Institute of Scientific and Technical Information of China (English)
LI Tao
2005-01-01
According to the relationship between the antibody concentration and the pathogen intrusion intensity, here we present an immunity-based model for the network security risk estimation (Insre). In Insre, the concepts and formal definitions of self,nonself, antibody, antigen and lymphocyte in the network security domain are given. Then the mathematical models of the self-tolerance, the clonal selection, the lifecycle of mature lymphocyte, immune memory and immune surveillance are established. Building upon the above models, a quantitative computation model for network security risk estimation,which is based on the calculation of antibody concentration, is thus presented. By using Insre, the types and intensity of network attacks, as well as the risk level of network security, can be calculated quantitatively and in real-time. Our theoretical analysis and experimental results show that Insre is a good solution to real-time risk evaluation for the network security.
Institute of Scientific and Technical Information of China (English)
崔凤仙; 刘阳
2011-01-01
针对配电网络规划中出现的中压配电站容量和位置不确定的情况,提出将中压配电站容量和位置连同网架结构、导线型号、线路回数等一起作为变量,采用整数编码与矩阵实数编码相结合的双重编码方式进行中压配电网络规划,其中整数编码用以确定网架结构、导线型号以及线路回数,而矩阵实数编码用以调整虚拟负荷点所带负荷量。设计了用于该规划的各项免疫遗传算法操作,并通过算例验证了该算法的有效性。%Considering the uncertainty of the capacities and the locations of the medium-voltage distribution stations in contribution network planning,this paper proposes a method for the planning,which employs a dual encoding way that takes the capacities and positions of the distribution stations,together with the structure of network,the types of the transmission lines and the number of circuit lines as parameters,and combines the integer encoding and real-matrix encoding.Here the integer encoding is for the structure of network,the types of the transmission lines and the number of circuit lines while the real-matrix encoding is for optimizing loads on the virtual load points.The operators of immune genetic algorithm for medium-voltage network planning are designed and applied to a real power distribution system,and it proves that the algorithm is effective.
Institute of Scientific and Technical Information of China (English)
王咪; 杨孔雨
2016-01-01
In this paper, we analyzed the current status of the fresh product cold chain distribution industry, pointed out the significance of route optimization therein, then considering the impact of bumping on the distribution cost of the fresh products and in connection with the fixed vehicle cost, transportation cost, energy cost, penalty cost, and cargo loss cost, etc., established the cold chain logistics vehicle distribution route optimization model, combined the 2-Opt algorithm and the immunity genetic algorithm to solve it, and at the end, through an empirical case, demonstrated the validity and practicality of the model.%分析了生鲜产品冷链配送的现状,并指出了研究生鲜产品冷链配送路径优化问题的重要意义.考虑配送过程中道路颠簸对于生鲜产品配送成本的影响,同时结合车辆固定成本、运输成本、能源成本、惩罚成本、货损成本等建立冷链物流车辆配送路径优化模型,并将2-Opt算法与免疫遗传算法相结合对该模型进行求解,最后通过实例分析,证明该模型有效实用,为相关行业的发展和企业运营提供参考.
Institute of Scientific and Technical Information of China (English)
林茂; 李孝全; 苏杨
2012-01-01
An improved adaptive immune genetic algorithm is proposed to meet the demand of diagnosis of distribution network. The new method can overcome the disability of standard GA. Through adjusting crossover and mutation probability adaptively and generating vaccines dynamically, the algorithm improve the shortcoming of standard GA for slow convergence and vaccine failure. The new method maintains the diversity of the population through get more new antibody by random born rather than delete it. Build up a new target function, take the diagnosis of a distribution network for example, the experiments show the availability, feasibility and advantage of the new method.%针对电网故障的特点,应用一种改进的免疫遗传算法对电网故障进行研究.该算法能够较好地解决传统遗传算法的不足.通过引入新的交叉和变异率,更多考虑了种群的全局特征,采用动态自适应方式提取疫苗,避免了传统遗传算法收敛速度较慢的缺点.改进的算法本着优胜劣汰的思想,删除适值较低的抗体群,取而代之的是随机生成的部分新抗体,保持种群的多样性.建立一个新的目标函数,通过对一个电网的分析,验证了方法的有效性.
Eigenvalue Decomposition-Based Modified Newton Algorithm
Directory of Open Access Journals (Sweden)
Wen-jun Wang
2013-01-01
Full Text Available When the Hessian matrix is not positive, the Newton direction may not be the descending direction. A new method named eigenvalue decomposition-based modified Newton algorithm is presented, which first takes the eigenvalue decomposition of the Hessian matrix, then replaces the negative eigenvalues with their absolute values, and finally reconstructs the Hessian matrix and modifies the searching direction. The new searching direction is always the descending direction. The convergence of the algorithm is proven and the conclusion on convergence rate is presented qualitatively. Finally, a numerical experiment is given for comparing the convergence domains of the modified algorithm and the classical algorithm.
Lane Detection Based on Machine Learning Algorithm
National Research Council Canada - National Science Library
Chao Fan; Jingbo Xu; Shuai Di
2013-01-01
In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning...
QPSO-based adaptive DNA computing algorithm.
Karakose, Mehmet; Cigdem, Ugur
2013-01-01
DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm.
QPSO-Based Adaptive DNA Computing Algorithm
Directory of Open Access Journals (Sweden)
Mehmet Karakose
2013-01-01
Full Text Available DNA (deoxyribonucleic acid computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO. Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1 parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2 adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3 numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm.
Evolutionary algorithm based configuration interaction approach
Chakraborty, Rahul
2016-01-01
A stochastic configuration interaction method based on evolutionary algorithm is designed as an affordable approximation to full configuration interaction (FCI). The algorithm comprises of initiation, propagation and termination steps, where the propagation step is performed with cloning, mutation and cross-over, taking inspiration from genetic algorithm. We have tested its accuracy in 1D Hubbard problem and a molecular system (symmetric bond breaking of water molecule). We have tested two different fitness functions based on energy of the determinants and the CI coefficients of determinants. We find that the absolute value of CI coefficients is a more suitable fitness function when combined with a fixed selection scheme.
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A new FFT algorithm has been deduced, which is called the base-6 FFT algorithm. The amount for calculating the DFT of complex sequence of N=2r by the base-6 FFT algorithm is Mr(N)=14/3*Nlog6N-4N+4 for multiplication operation of real number and Ar(N)=23/3*Nlog6N-2N+2 for addition operation of real number. The amount for calculating the DFT of real sequence is a half of it with the complex sequence.
Seizure detection algorithms based on EMG signals
DEFF Research Database (Denmark)
Conradsen, Isa
Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective......: to show whether medical signal processing of EMG data is feasible for detection of epileptic seizures. Methods: EMG signals during generalised seizures were recorded from 3 patients (with 20 seizures in total). Two possible medical signal processing algorithms were tested. The first algorithm was based...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...
Associating Memory Through Case-Based Immune Mechanisms for Dynamic Job-Shop Scheduling
Institute of Scientific and Technical Information of China (English)
尹文君; 刘民; 吴澄
2004-01-01
Knowledge plays an active role in job-shop scheduling,especially in dynamic environments.A novel case-based immune framework was developed for static and dynamic job-shop problems,using the associative memory and knowledge reuse from case-based reasoning (CBR) and immune response mechanisms.A 2-level similarity index which combines both job routing and problem solution characteristics based on DNA matching ideas was defined for both the CBR and immune algorithms.A CBR-embedded immune algorithms (CBR-IAs) framework was then developed focusing on case retrieval and adaptation methods.In static environments,the CBR-IAs have excellent population diversity and fast convergence which are necessary for dynamic problems with jobs arriving and leaving continually.The results with dynamic scheduling problems further confirm the CBR-IAs effectiveness as a problem solving method with knowledge reuse.
基于免疫反馈算法的缝纫设备振动控制研究%Research on Sewing Machine Vibration Control Based on Immune Feedback Algorithm
Institute of Scientific and Technical Information of China (English)
林君焕; 陈月芬; 张国庆
2012-01-01
The vibration is harmful to sewing performance. In order to decrease the harm, a sewing machine vibration active control system is developed, in which the giant magnetostrictive actuator is adopted, satisfying the design requirements on control precise and frequency response. The giant magnetostrictive actuator and sewing machine is modeled, on the base of the research on giant magnetostrictive actuator' s principle and sewing machine vibration character. An immune feedback controller based on artificial immune is designed, and the simulation on MATLAB with the tool of Simulink and test on the industrial sewing machine is demonstrated, the results prove that the system can effectively suppress the vibration of sewing machine from motor in sewing.%为降低缝纫设备缝纫时产生的振动对缝纫性能的影响,开发了一种缝纫设备振动主动控制系统.该系统中的执行机构采用超磁致执行器,能满足振动控制精度和频响的要求.在完成系统的总体设计后,研究了超磁致伸缩执行器工作原理以及缝纫设备机身的振动特性,并分别对他们进行建模.设计了一种基于人工免疫原理的免疫反馈控制器,采用MATLAB环境中的Simulink工具对控制系统进行建模仿真,并在工业缝纫机上进行了现场实验测试,结果证明该系统能很好地抑制缝纫时引起的机身振动.
Duality based optical flow algorithms with applications
DEFF Research Database (Denmark)
Rakêt, Lars Lau
We consider the popular TV-L1 optical flow formulation, and the so-called duality based algorithm for minimizing the TV-L1 energy. The original formulation is extended to allow for vector valued images, and minimization results are given. In addition we consider different definitions of total...... variation regularization, and related formulations of the optical flow problem that may be used with a duality based algorithm. We present a highly optimized algorithmic setup to estimate optical flows, and give five novel applications. The first application is registration of medical images, where X......-ray images of different hands, taken using different imaging devices are registered using a TV-L1 optical flow algorithm. We propose to regularize the input images, using sparsity enhancing regularization of the image gradient to improve registration results. The second application is registration of 2D...
Duality based optical flow algorithms with applications
DEFF Research Database (Denmark)
Rakêt, Lars Lau
We consider the popular TV-L1 optical flow formulation, and the so-called duality based algorithm for minimizing the TV-L1 energy. The original formulation is extended to allow for vector valued images, and minimization results are given. In addition we consider different definitions of total...... variation regularization, and related formulations of the optical flow problem that may be used with a duality based algorithm. We present a highly optimized algorithmic setup to estimate optical flows, and give five novel applications. The first application is registration of medical images, where X......-ray images of different hands, taken using different imaging devices are registered using a TV-L1 optical flow algorithm. We propose to regularize the input images, using sparsity enhancing regularization of the image gradient to improve registration results. The second application is registration of 2D...
A Novel Model of IDS Based on Fuzzy Cluster and Immune Principle
Institute of Scientific and Technical Information of China (English)
TAO Xin-min; LIU Fu-rong
2005-01-01
This paper presents a novel intrusion detection model based on fuzzy cluster and immune principle. The original rival penalized competitive learning (RPCL) algorithm is modified in order to address the problem of different variability of variables and correlation between variables, the sensitivity to initial number of clusters is also solved. Especially, we use the extended RPCL algorithm to determine the initial number of clusters in the fuzzy cluster algorithm. The genetic algorithm is used to optimize the radius deviation for the determination of characteristic function of abnormal subspace.
Function Optimization Based on Quantum Genetic Algorithm
Directory of Open Access Journals (Sweden)
Ying Sun
2014-01-01
Full Text Available Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA and Genetic Quantum Algorithm (GQA. The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.
Edge Crossing Minimization Algorithm for Hierarchical Graphs Based on Genetic Algorithms
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
We present an edge crossing minimization algorithm forhierarchical gr aphs based on genetic algorithms, and comparing it with some heuristic algorithm s. The proposed algorithm is more efficient and has the following advantages: th e frame of the algorithms is unified, the method is simple, and its implementati on and revision are easy.
Institute of Scientific and Technical Information of China (English)
宋辰; 黄海燕
2012-01-01
A new immune cultural algorithm (ICA) based on immune clone selection was proposed. In ICA,immune clone machine was used for training and testing sampling data from SRT-III furnace. One selection was taken as population space of cultural algorithm. In belief space,the knowledge extraction,expression,storage,update methods were proposed according to their evolutionary characteristics. Communication function was improved at the same time which in turn improved the capacity of algorithm evolution. The test results showed that compared with genetic algorithm ( GA) and chemotactic differential evolution algorithm (CDEA),immune cultural algorithm had much improvement in search precision and convergence speed. The algorithm was applied to the support vector machine parameter optimization for solving fault diagnosis of ethylene cracking furnace. Multi-class classifier was made by support vector machine. Compared with fault classification using the parameters optimized by genetic algorithm,the simulation results showed that the proposed algorithm achieved good result in classification accuracy,20 percentage points higher than the method without using immune cultural algorithms.
Secure OFDM communications based on hashing algorithms
Neri, Alessandro; Campisi, Patrizio; Blasi, Daniele
2007-10-01
In this paper we propose an OFDM (Orthogonal Frequency Division Multiplexing) wireless communication system that introduces mutual authentication and encryption at the physical layer, without impairing spectral efficiency, exploiting some freedom degrees of the base-band signal, and using encrypted-hash algorithms. FEC (Forward Error Correction) is instead performed through variable-rate Turbo Codes. To avoid false rejections, i.e. rejections of enrolled (authorized) users, we designed and tested a robust hash algorithm. This robustness is obtained both by a segmentation of the hash domain (based on BCH codes) and by the FEC capabilities of Turbo Codes.
Graphical model construction based on evolutionary algorithms
Institute of Scientific and Technical Information of China (English)
Youlong YANG; Yan WU; Sanyang LIU
2006-01-01
Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining.The experimental results show that the exact theoretical results and the approximations match very well.
Archaeal CRISPR-based immune systems
DEFF Research Database (Denmark)
Garrett, Roger A; Vestergaard, Gisle Alberg; Shah, Shiraz Ali
2011-01-01
CRISPR (clustered regularly interspaced short palindromic repeats)-based immune systems are essentially modular with three primary functions: the excision and integration of new spacers, the processing of CRISPR transcripts to yield mature CRISPR RNAs (crRNAs), and the targeting and cleavage...... of foreign nucleic acid. The primary target appears to be the DNA of foreign genetic elements, but the CRISPR/Cmr system that is widespread amongst archaea also specifically targets and cleaves RNA in vitro. The archaeal CRISPR systems tend to be both diverse and complex. Here we examine evidence...... of CRISPR loci and the evidence for intergenomic exchange of CRISPR systems....
A Practical Propositional Knowledge Base Revision Algorithm
Institute of Scientific and Technical Information of China (English)
陶雪红; 孙伟; 等
1997-01-01
This paper gives an outline of knowledge base revision and some recently presented complexity results about propostitional knowledge base revision.Different methods for revising propositional knowledge base have been proposed recently by several researchers,but all methods are intractable in the general case.For practical application,this paper presents a revision method for special case,and gives its corresponding polynomial algorithm.
Second Attribute Algorithm Based on Tree Expression
Institute of Scientific and Technical Information of China (English)
Su-Qing Han; Jue Wang
2006-01-01
One view of finding a personalized solution of reduct in an information system is grounded on the viewpoint that attribute order can serve as a kind of semantic representation of user requirements. Thus the problem of finding personalized solutions can be transformed into computing the reduct on an attribute order. The second attribute theorem describes the relationship between the set of attribute orders and the set of reducts, and can be used to transform the problem of searching solutions to meet user requirements into the problem of modifying reduct based on a given attribute order. An algorithm is implied based on the second attribute theorem, with computation on the discernibility matrix. Its time complexity is O(n2 × m) (n is the number of the objects and m the number of the attributes of an information system).This paper presents another effective second attribute algorithm for facilitating the use of the second attribute theorem,with computation on the tree expression of an information system. The time complexity of the new algorithm is linear in n. This algorithm is proved to be equivalent to the algorithm on the discernibility matrix.
Structure-Based Algorithms for Microvessel Classification
Smith, Amy F.
2015-02-01
© 2014 The Authors. Microcirculation published by John Wiley & Sons Ltd. Objective: Recent developments in high-resolution imaging techniques have enabled digital reconstruction of three-dimensional sections of microvascular networks down to the capillary scale. To better interpret these large data sets, our goal is to distinguish branching trees of arterioles and venules from capillaries. Methods: Two novel algorithms are presented for classifying vessels in microvascular anatomical data sets without requiring flow information. The algorithms are compared with a classification based on observed flow directions (considered the gold standard), and with an existing resistance-based method that relies only on structural data. Results: The first algorithm, developed for networks with one arteriolar and one venular tree, performs well in identifying arterioles and venules and is robust to parameter changes, but incorrectly labels a significant number of capillaries as arterioles or venules. The second algorithm, developed for networks with multiple inlets and outlets, correctly identifies more arterioles and venules, but is more sensitive to parameter changes. Conclusions: The algorithms presented here can be used to classify microvessels in large microvascular data sets lacking flow information. This provides a basis for analyzing the distinct geometrical properties and modelling the functional behavior of arterioles, capillaries, and venules.
Hu, Yifan; Ding, Yongsheng; Hao, Kuangrong; Ren, Lihong; Han, Hua
2014-03-01
The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.
Model based development of engine control algorithms
Dekker, H.J.; Sturm, W.L.
1996-01-01
Model based development of engine control systems has several advantages. The development time and costs are strongly reduced because much of the development and optimization work is carried out by simulating both engine and control system. After optimizing the control algorithm it can be executed b
Verification-Based Interval-Passing Algorithm for Compressed Sensing
Wu, Xiaofu; Yang, Zhen
2013-01-01
We propose a verification-based Interval-Passing (IP) algorithm for iteratively reconstruction of nonnegative sparse signals using parity check matrices of low-density parity check (LDPC) codes as measurement matrices. The proposed algorithm can be considered as an improved IP algorithm by further incorporation of the mechanism of verification algorithm. It is proved that the proposed algorithm performs always better than either the IP algorithm or the verification algorithm. Simulation resul...
Optimal Hops-Based Adaptive Clustering Algorithm
Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong
This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.
Numerical Algorithms Based on Biorthogonal Wavelets
Ponenti, Pj.; Liandrat, J.
1996-01-01
Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.
Algorithmic Differentiation for Calculus-based Optimization
Walther, Andrea
2010-10-01
For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.
Lane Detection Based on Machine Learning Algorithm
Directory of Open Access Journals (Sweden)
Chao Fan
2013-09-01
Full Text Available In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning. After pretreatment, a set of haar-like filters were used to calculate the eigenvalue in the gray image f(x,y and edge e(x,y. Then these features were trained by using improved boosting algorithm and the final class function g(x was obtained, which was used to judge whether the point x belonging to the lane or not. To avoid the over fitting in traditional boosting, Fisher discriminant analysis was used to initialize the weights of samples. After testing by many road in all conditions, it showed that this algorithm had good robustness and real-time to recognize the lane in all challenging conditions.
Web Based Genetic Algorithm Using Data Mining
Ashiqur Rahman; Asaduzzaman Noman; Md. Ashraful Islam; Al-Amin Gaji
2016-01-01
This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; fea...
AN OPTIMIZATION ALGORITHM BASED ON BACTERIA BEHAVIOR
Directory of Open Access Journals (Sweden)
Ricardo Contreras
2014-09-01
Full Text Available Paradigms based on competition have shown to be useful for solving difficult problems. In this paper we present a new approach for solving hard problems using a collaborative philosophy. A collaborative philosophy can produce paradigms as interesting as the ones found in algorithms based on a competitive philosophy. Furthermore, we show that the performance - in problems associated to explosive combinatorial - is comparable to the performance obtained using a classic evolutive approach.
Immune algorithm for discretization of decision systems in rough set theory
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pretreating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.
Monkey-King immune evolutionary algorithm%猴王免疫进化算法
Institute of Scientific and Technical Information of China (English)
邹艳玲; 吴启满; 李育强; 王兵
2011-01-01
猴王遗传算法具有原理简单、易于计算的优点,但存在猴王点(最优个体)附近空间局部寻优能力弱,进而影响全局搜索能力的局限.通过引入免疫进化算法,对猴王点进行免疫进化迭代优化,使得既加大对最优个体附近解空间搜索的同时,也兼顾了对最优个体附近解空间以外区域的搜索,避免了不成熟收敛；且随着迭代的进行,局部搜索能力不断得到加强,算法以更高的精度逼近全局最优解.对多个典型测试函数的计算,并与猴王遗传算法、改进后的猴王遗传算法和普通爬山算子遗传算法的优化计算结果进行了比较.结果表明猴王免疫进化算法具有更佳的寻优能力和更好的稳定性.%Monkey-King genetic algorithm has the shortages of the lower searching ability in the local area and further in the whole area at monkey-king point, in spite of the advantages of the simple principle and simplicity in calculation. Monkey-king point was optimized iteratively by using immune evolutionary algorithm. This method overcomes the premature convergence because of the optimal searching in the out as well as in of the areas at the monkey-king point. At the same time, with the process of iteration, the algorithm closes in the whole of optimal solution with the higher precision because of the gradual strengthening of local searching ability. This paper calculates typical test function and compares with several methods, such as monkey-king genetic algorithm, improved monkey-king genetic algorithm and common climbing operator genetic algorithm et al. The results show that the monkey-king immune evolutionary algorithm has the optimal searching ability and the better stability.
Directory of Open Access Journals (Sweden)
Yifan Hu
2012-01-01
Full Text Available The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs applications, and has recently been attracting growing research interests. In order to maintain k disjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.
Cloud-based Evolutionary Algorithms: An algorithmic study
Merelo, Juan-J; Mora, Antonio M; Castillo, Pedro; Romero, Gustavo; Laredo, JLJ
2011-01-01
After a proof of concept using Dropbox(tm), a free storage and synchronization service, showed that an evolutionary algorithm using several dissimilar computers connected via WiFi or Ethernet had a good scaling behavior in terms of evaluations per second, it remains to be proved whether that effect also translates to the algorithmic performance of the algorithm. In this paper we will check several different, and difficult, problems, and see what effects the automatic load-balancing and asynchrony have on the speed of resolution of problems.
Colorectal Polyposis and Immune-Based Therapies
Directory of Open Access Journals (Sweden)
Pearl Jacobson-Brown
2004-01-01
Full Text Available The progression from precancerous (adenomatous colon polyps to malignant colorectal cancer involves the complex actions of various cytokines on T cell proliferation, cell-cell adhesion, apoptosis and host immunity. A broad spectrum of new treatments, including innovative molecular therapies such as gene therapy and treatment with cytokines, is under experimental and preclinical investigation. Nonsteroidal anti-inflammatory drugs and selective cyclooxygenase-2 inhibitors have traditionally been used as inflammation-reducing agents in cases of colon adenoma. Currently, adjuvant immunotherapies such as recombinant gene therapy and antibody-cytokine fusion proteins are assuming a more significant role in the management of colorectal neoplasia. Furthermore, advances in antitumour necrosis factor antibodies for the treatment of ulcerative colitis and Crohn's disease may have potential as chemoprotective agents for the treatment of colon polyposis. The present review aims to discuss the immunological mechanisms underlying colon tumour progression and the molecular and immune-based therapies that are leading to new methods of prognosis and treatment.
A novel tree structure based watermarking algorithm
Lin, Qiwei; Feng, Gui
2008-03-01
In this paper, we propose a new blind watermarking algorithm for images which is based on tree structure. The algorithm embeds the watermark in wavelet transform domain, and the embedding positions are determined by significant coefficients wavelets tree(SCWT) structure, which has the same idea with the embedded zero-tree wavelet (EZW) compression technique. According to EZW concepts, we obtain coefficients that are related to each other by a tree structure. This relationship among the wavelet coefficients allows our technique to embed more watermark data. If the watermarked image is attacked such that the set of significant coefficients is changed, the tree structure allows the correlation-based watermark detector to recover synchronously. The algorithm also uses a visual adaptive scheme to insert the watermark to minimize watermark perceptibility. In addition to the watermark, a template is inserted into the watermarked image at the same time. The template contains synchronization information, allowing the detector to determine the geometric transformations type applied to the watermarked image. Experimental results show that the proposed watermarking algorithm is robust against most signal processing attacks, such as JPEG compression, median filtering, sharpening and rotating. And it is also an adaptive method which shows a good performance to find the best areas to insert a stronger watermark.
Fast Algorithms for Model-Based Diagnosis
Fijany, Amir; Barrett, Anthony; Vatan, Farrokh; Mackey, Ryan
2005-01-01
Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.
Research of the Kernel Operator Library Based on Cryptographic Algorithm
Institute of Scientific and Technical Information of China (English)
王以刚; 钱力; 黄素梅
2001-01-01
The variety of encryption mechanism and algorithms which were conventionally used have some limitations.The kernel operator library based on Cryptographic algorithm is put forward. Owing to the impenetrability of algorithm, the data transfer system with the cryptographic algorithm library has many remarkable advantages in algorithm rebuilding and optimization,easily adding and deleting algorithm, and improving the security power over the traditional algorithm. The user can choose any one in all algorithms with the method against any attack because the cryptographic algorithm library is extensible.
SMS Spam Filtering Technique Based on Artificial Immune System
Directory of Open Access Journals (Sweden)
Tarek M Mahmoud
2012-03-01
Full Text Available The Short Message Service (SMS have an important economic impact for end users and service providers. Spam is a serious universal problem that causes problems for almost all users. Several studies have been presented, including implementations of spam filters that prevent spam from reaching their destination. Nave Bayesian algorithm is one of the most effective approaches used in filtering techniques. The computational power of smart phones are increasing, making increasingly possible to perform spam filtering at these devices as a mobile agent application, leading to better personalization and effectiveness. The challenge of filtering SMS spam is that the short messages often consist of few words composed of abbreviations and idioms. In this paper, we propose an anti-spam technique based on Artificial Immune System (AIS for filtering SMS spam messages. The proposed technique utilizes a set of some features that can be used as inputs to spam detection model. The idea is to classify message using trained dataset that contains Phone Numbers, Spam Words, and Detectors. Our proposed technique utilizes a double collection of bulk SMS messages Spam and Ham in the training process. We state a set of stages that help us to build dataset such as tokenizer, stop word filter, and training process. Experimental results presented in this paper are based on iPhone Operating System (iOS. The results applied to the testing messages show that the proposed system can classify the SMS spam and ham with accurate compared with Nave Bayesian algorithm.
Network-based recommendation algorithms: A review
Yu, Fei; Gillard, Sebastien; Medo, Matus
2015-01-01
Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use - such as the possible influence of recommendation on the evolution of systems that use it - and finally discuss open research directions and challenges.
Network-based recommendation algorithms: A review
Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš
2016-06-01
Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.
LSB Based Quantum Image Steganography Algorithm
Jiang, Nan; Zhao, Na; Wang, Luo
2016-01-01
Quantum steganography is the technique which hides a secret message into quantum covers such as quantum images. In this paper, two blind LSB steganography algorithms in the form of quantum circuits are proposed based on the novel enhanced quantum representation (NEQR) for quantum images. One algorithm is plain LSB which uses the message bits to substitute for the pixels' LSB directly. The other is block LSB which embeds a message bit into a number of pixels that belong to one image block. The extracting circuits can regain the secret message only according to the stego cover. Analysis and simulation-based experimental results demonstrate that the invisibility is good, and the balance between the capacity and the robustness can be adjusted according to the needs of applications.
A Single Pattern Matching Algorithm Based on Character Frequency
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Based on the study of single pattern matching, MBF algorithm is proposed by imitating the string searching procedure of human. The algorithm preprocesses the pattern by using the idea of Quick Search algorithm and the already-matched pattern psefix and suffix information. In searching phase, the algorithm makes use of the!character using frequency and the continue-skip idea. The experiment shows that MBF algorithm is more efficient than other algorithms.
An Immunization Strategy Based on Propagation Mechanism
Directory of Open Access Journals (Sweden)
Yixin Zhu
2014-01-01
Full Text Available With the ubiquity of smart phones, wearable equipment, and wireless sensors, the topologies of networks composed by them change along with time. The immunization strategies in which network immune nodes are chosen by analyzing the static aggregation network topologies have been challenged. The studies about interaction propagations between two pathogens show that the interaction can change propagation threshold and the final epidemic size of each other, which provides a new thinking of immunization method. The eradication or inhibition of the virus can be achieved through the spread of its opposite party. Here, we put forward an immunization strategy whose implementation does not depend on the analysis of network topology. The immunization agents are randomly placed on a few of individuals of network and spread out from these individuals on network in a propagation method. The immunization agents prevent virus infecting their habitat nodes with certain immune success rate. The analysis and simulation of evolution equation of the model show that immune propagation has a significant impact on the spread threshold and steady-state density of virus on a finite size of BA networks. Simulations on some real-world networks also suggest that the immunization strategy is feasible and effective.
Institute of Scientific and Technical Information of China (English)
Xuhua Shi; Feng Qian
2011-01-01
Artificial Immune Network (aiNet) algorithms have become popular for global optimization in many modern industrial applications. However, high-dimensional systems using such models suffer from a potential premature convergence problem. In the existing aiNet algorithms, the premature convergence problem can be avoided by implementing various clonal selection methods, such as immune suppression and mutation approaches, both for single population and multi-population cases. This paper presents a new Multi-Agent Artificial Immune Network (Ma-aiNet) algorithm, which combines immune mechanics and multiagent technology, to overcome the premature convergence problem in high-dimensional systems and to efficiently use the agent ability of sensing and acting on the environment. Ma-aiNet integrates global and local search algorithms. The performance of the proposed method is evaluated using 10 benchmark problems, and the results are compared with other well-known intelligent algorithms. The study demonstrates that Ma-aiNet outperforms other algorithms tested. Ma-aiNet is also used to determine the Murphree efficiency of a distillation column with satisfactory results.
A Hybrid Algorithm for Satellite Data Transmission Schedule Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
LI Yun-feng; WU Xiao-yue
2008-01-01
A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission. At first, based on description of satellite data transmission request, satellite data transmission task modal and satellite data transmission scheduling problem model are established. Secondly, the conflicts in scheduling are discussed. According to the meaning of possible conflict, the method to divide possible conflict task set is given. Thirdly, a hybrid algorithm which consists of genetic algorithm and heuristic information is presented. The heuristic information comes from two concepts, conflict degree and conflict number. Finally, an example shows the algorithm's feasibility and performance better than other traditional algorithms.
KIR Genes and Patterns Given by the A Priori Algorithm: Immunity for Haematological Malignancies
Directory of Open Access Journals (Sweden)
J. Gilberto Rodríguez-Escobedo
2015-01-01
Full Text Available Killer-cell immunoglobulin-like receptors (KIRs are membrane proteins expressed by cells of innate and adaptive immunity. The KIR system consists of 17 genes and 614 alleles arranged into different haplotypes. KIR genes modulate susceptibility to haematological malignancies, viral infections, and autoimmune diseases. Molecular epidemiology studies rely on traditional statistical methods to identify associations between KIR genes and disease. We have previously described our results by applying support vector machines to identify associations between KIR genes and disease. However, rules specifying which haplotypes are associated with greater susceptibility to malignancies are lacking. Here we present the results of our investigation into the rules governing haematological malignancy susceptibility. We have studied the different haplotypic combinations of 17 KIR genes in 300 healthy individuals and 43 patients with haematological malignancies (25 with leukaemia and 18 with lymphomas. We compare two machine learning algorithms against traditional statistical analysis and show that the “a priori” algorithm is capable of discovering patterns unrevealed by previous algorithms and statistical approaches.
Continuous Attributes Discretization Algorithm based on FPGA
Directory of Open Access Journals (Sweden)
Guoqiang Sun
2013-07-01
Full Text Available The paper addresses the problem of Discretization of continuous attributes in rough set. Discretization of continuous attributes is an important part of rough set theory because most of data that we usually gain are continuous data. In order to improve processing speed of discretization, we propose a FPGA-based discretization algorithm of continuous attributes making use of the speed advantage of FPGA. Combined attributes dependency degree of rough ret, the discretization system was divided into eight modules according to block design. This method can save much time of pretreatment in rough set and improve operation efficiency. Extensive experiments on a certain fighter fault diagnosis validate the effectiveness of the algorithm.
Multi-Agent Reinforcement Learning Algorithm Based on Action Prediction
Institute of Scientific and Technical Information of China (English)
TONG Liang; LU Ji-lian
2006-01-01
Multi-agent reinforcement learning algorithms are studied. A prediction-based multi-agent reinforcement learning algorithm is presented for multi-robot cooperation task. The multi-robot cooperation experiment based on multi-agent inverted pendulum is made to test the efficency of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation strategy much faster than the primitive multiagent reinforcement learning algorithm.
Algorithm Research of Individualized Travelling Route Recommendation Based on Similarity
Directory of Open Access Journals (Sweden)
Xue Shan
2015-01-01
Full Text Available Although commercial recommendation system has made certain achievement in travelling route development, the recommendation system is facing a series of challenges because of people’s increasing interest in travelling. It is obvious that the core content of the recommendation system is recommendation algorithm. The advantages of recommendation algorithm can bring great effect to the recommendation system. Based on this, this paper applies traditional collaborative filtering algorithm for analysis. Besides, illustrating the deficiencies of the algorithm, such as the rating unicity and rating matrix sparsity, this paper proposes an improved algorithm combing the multi-similarity algorithm based on user and the element similarity algorithm based on user, so as to compensate for the deficiencies that traditional algorithm has within a controllable range. Experimental results have shown that the improved algorithm has obvious advantages in comparison with the traditional one. The improved algorithm has obvious effect on remedying the rating matrix sparsity and rating unicity.
Asian Option Pricing Based on Genetic Algorithms
Institute of Scientific and Technical Information of China (English)
YunzhongLiu; HuiyuXuan
2004-01-01
The cross-fertilization between artificial intelligence and computational finance has resulted in some of the most active research areas in financial engineering. One direction is the application of machine learning techniques to pricing financial products, which is certainly one of the most complex issues in finance. In the literature, when the interest rate,the mean rate of return and the volatility of the underlying asset follow general stochastic processes, the exact solution is usually not available. In this paper, we shall illustrate how genetic algorithms (GAs), as a numerical approach, can be potentially helpful in dealing with pricing. In particular, we test the performance of basic genetic algorithms by using it to the determination of prices of Asian options, whose exact solutions is known from Black-Scholesoption pricing theory. The solutions found by basic genetic algorithms are compared with the exact solution, and the performance of GAs is ewluated accordingly. Based on these ewluations, some limitations of GAs in option pricing are examined and possible extensions to future works are also proposed.
Improved pulse laser ranging algorithm based on high speed sampling
Gao, Xuan-yi; Qian, Rui-hai; Zhang, Yan-mei; Li, Huan; Guo, Hai-chao; He, Shi-jie; Guo, Xiao-kang
2016-10-01
Narrow pulse laser ranging achieves long-range target detection using laser pulse with low divergent beams. Pulse laser ranging is widely used in military, industrial, civil, engineering and transportation field. In this paper, an improved narrow pulse laser ranging algorithm is studied based on the high speed sampling. Firstly, theoretical simulation models have been built and analyzed including the laser emission and pulse laser ranging algorithm. An improved pulse ranging algorithm is developed. This new algorithm combines the matched filter algorithm and the constant fraction discrimination (CFD) algorithm. After the algorithm simulation, a laser ranging hardware system is set up to implement the improved algorithm. The laser ranging hardware system includes a laser diode, a laser detector and a high sample rate data logging circuit. Subsequently, using Verilog HDL language, the improved algorithm is implemented in the FPGA chip based on fusion of the matched filter algorithm and the CFD algorithm. Finally, the laser ranging experiment is carried out to test the improved algorithm ranging performance comparing to the matched filter algorithm and the CFD algorithm using the laser ranging hardware system. The test analysis result demonstrates that the laser ranging hardware system realized the high speed processing and high speed sampling data transmission. The algorithm analysis result presents that the improved algorithm achieves 0.3m distance ranging precision. The improved algorithm analysis result meets the expected effect, which is consistent with the theoretical simulation.
A new optimization algorithm based on chaos
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave's search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate.In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables optimization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.
The X-files in immunity: sex-based differences predispose immune responses.
Fish, Eleanor N
2008-09-01
Despite accumulating evidence in support of sex-based differences in innate and adaptive immune responses, in the susceptibility to infectious diseases and in the prevalence of autoimmune diseases, health research and clinical practice do not address these distinctions, and most research studies of immune responses do not stratify by sex. X-linked genes, hormones and societal context are among the many factors that contribute to disparate immune responses in males and females. It is crucial to address sex-based differences in disease pathogenesis and in the pharmacokinetics and pharmacodynamics of therapeutic medications to provide optimal disease management for both sexes.
Function Optimization Based on Quantum Genetic Algorithm
Ying Sun; Hegen Xiong
2014-01-01
Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded c...
Function Optimization Based on Quantum Genetic Algorithm
Ying Sun; Yuesheng Gu; Hegen Xiong
2013-01-01
Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on.It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed ,which is called variable-boundary-coded quantum genetic algorithm (vbQGA) in which qubit chromosomes are collapsed into variableboundary- coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained.The m...
Immune modulation by dendritic-cell-based cancer vaccines
Indian Academy of Sciences (India)
CHAITANYA KUMAR; SAKSHI KOHLI; POONAMALLE PARTHASARATHY BAPSY; ASHOK KUMAR VAID; MINISH JAIN; VENKATA SATHYA SURESH ATTILI; BANDANA SHARAN
2017-03-01
The interplay between host immunity and tumour cells has opened the possibility of targeting tumour cells bymodulation of the human immune system. Cancer immunotherapy involves the treatment of a tumour by utilizing therecombinant human immune system components to target the pro-tumour microenvironment or by revitalizing theimmune system with the ability to kill tumour cells by priming the immune cells with tumour antigens. In this review,current immunotherapy approaches to cancer with special focus on dendritic cell (DC)-based cancer vaccines arediscussed. Some of the DC-based vaccines under clinical trials for various cancer types are highlighted. Establishingtumour immunity involves a plethora of immune components and pathways; hence, combining chemotherapy,radiation therapy and various arms of immunotherapy, after analysing the benefits of individual therapeutic agents,might be beneficial to the patient.
Cognitive radio resource allocation based on coupled chaotic genetic algorithm
Institute of Scientific and Technical Information of China (English)
Zu Yun-Xiao; Zhou Jie; Zeng Chang-Chang
2010-01-01
A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.
Cognitive radio resource allocation based on coupled chaotic genetic algorithm
Zu, Yun-Xiao; Zhou, Jie; Zeng, Chang-Chang
2010-11-01
A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.
An assembly sequence planning method based on composite algorithm
Directory of Open Access Journals (Sweden)
Enfu LIU
2016-02-01
Full Text Available To solve the combination explosion problem and the blind searching problem in assembly sequence planning of complex products, an assembly sequence planning method based on composite algorithm is proposed. In the composite algorithm, a sufficient number of feasible assembly sequences are generated using formalization reasoning algorithm as the initial population of genetic algorithm. Then fuzzy knowledge of assembly is integrated into the planning process of genetic algorithm and ant algorithm to get the accurate solution. At last, an example is conducted to verify the feasibility of composite algorithm.
A Survey of Grid Based Clustering Algorithms
Directory of Open Access Journals (Sweden)
MR ILANGO
2010-08-01
Full Text Available Cluster Analysis, an automatic process to find similar objects from a database, is a fundamental operation in data mining. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. Clustering techniques have been discussed extensively in SimilaritySearch, Segmentation, Statistics, Machine Learning, Trend Analysis, Pattern Recognition and Classification [1]. Clustering methods can be classified into i Partitioning methods ii Hierarchical methods iii Density-based methods iv Grid-based methods v Model-based methods. Grid based methods quantize the object space into a finite number of cells (hyper-rectangles and then perform the required operations on the quantized space. The main advantage of Grid based method is its fast processing time which depends on number of cells in each dimension in quantized space. In this research paper, we present some of the grid based methods such as CLIQUE (CLustering In QUEst [2], STING (STatistical INformation Grid [3], MAFIA (Merging of Adaptive Intervals Approach to Spatial Data Mining [4], Wave Cluster [5]and O-CLUSTER (Orthogonal partitioning CLUSTERing [6], as a survey andalso compare their effectiveness in clustering data objects. We also present some of the latest developments in Grid Based methods such as Axis Shifted Grid Clustering Algorithm [7] and Adaptive Mesh Refinement [Wei-Keng Liao etc] [8] to improve the processing time of objects.
ALGORITHM FOR GENERATING DEM BASED ON CONE
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Digital elevation model (DEM) has a variety of applications in GIS and CAD.It is the basic model for generating three-dimensional terrain feature.Generally speaking,there are two methods for building DEM.One is based upon the digital terrain model of discrete points,and is characterized by fast speed and low precision.The other is based upon triangular digital terrain model,and slow speed and high precision are the features of the method.Combining the advantages of the two methods,an algorithm for generating DEM with discrete points is presented in this paper.When interpolating elevation,this method can create a triangle which includes interpolating point and the elevation of the interpolating point can be obtained from the triangle.The method has the advantage of fast speed,high precision and less memory.
A Genetic Algorithm-Based Feature Selection
Directory of Open Access Journals (Sweden)
Babatunde Oluleye
2014-07-01
Full Text Available This article details the exploration and application of Genetic Algorithm (GA for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100 features were extracted from set of images found in the Flavia dataset (a publicly available dataset. The extracted features are Zernike Moments (ZM, Fourier Descriptors (FD, Lengendre Moments (LM, Hu 7 Moments (Hu7M, Texture Properties (TP and Geometrical Properties (GP. The main contributions of this article are (1 detailed documentation of the GA Toolbox in MATLAB and (2 the development of a GA-based feature selector using a novel fitness function (kNN-based classification error which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy
An artificial immune approach for optical image based vision inspection
Institute of Scientific and Technical Information of China (English)
Hong Zheng(郑宏); Nanfeng Xiao(肖南风); Jinhui Lan(蓝金辉)
2003-01-01
This paper presents a novel approach of visual inspection for texture surface defects. The approach usesartificial immune theory in learning the detection of texture defects. In this paper, texture defects areregards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholdsused for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from thenatural immune system is employed to learn antibodies. Experimental results on textile image inspectionare presented to illustrate the merit and feasibility of the proposed method.
Institute of Scientific and Technical Information of China (English)
Chen Xiaofang; Gui Weihua; Wang Yalin
2005-01-01
Considering premature convergence in the searching process of genetic algorithm, a chaotic migration-based pseudo parallel genetic algorithm (CMPPGA) is proposed, which applies the idea of isolated evolution and information exchanging in distributed Parallel Genetic Algorithm by serial program structure to solve optimization problem of low real-time demand. In this algorithm,asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence. Infcrmation exchanging among sub-populations is ensured to be efficient and sufficient due to that the sequence is ergodic and stochastic. Simulation study of CMPPGA shows its strong global search ability, superiority to standard genetic algorithm and high immunity against premature convergence. According to the practice of raw material supply, an inventory prcgramming model is set up and solved by CMPPGA with satisfactory results returned.
A Trust-region-based Sequential Quadratic Programming Algorithm
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Poulsen, Niels Kjølstad
This technical note documents the trust-region-based sequential quadratic programming algorithm used in other works by the authors. The algorithm seeks to minimize a convex nonlinear cost function subject to linear inequalty constraints and nonlinear equality constraints....
Speech Enhancement based on Compressive Sensing Algorithm
Sulong, Amart; Gunawan, Teddy S.; Khalifa, Othman O.; Chebil, Jalel
2013-12-01
There are various methods, in performance of speech enhancement, have been proposed over the years. The accurate method for the speech enhancement design mainly focuses on quality and intelligibility. The method proposed with high performance level. A novel speech enhancement by using compressive sensing (CS) is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. Using CS can reduce the number of degrees of freedom of a sparse/compressible signal by permitting only certain configurations of the large and zero/small coefficients, and structured sparsity models. Therefore, CS is significantly provides a way of reconstructing a compressed version of the speech in the original signal by taking only a small amount of linear and non-adaptive measurement. The performance of overall algorithms will be evaluated based on the speech quality by optimise using informal listening test and Perceptual Evaluation of Speech Quality (PESQ). Experimental results show that the CS algorithm perform very well in a wide range of speech test and being significantly given good performance for speech enhancement method with better noise suppression ability over conventional approaches without obvious degradation of speech quality.
PDE Based Algorithms for Smooth Watersheds.
Hodneland, Erlend; Tai, Xue-Cheng; Kalisch, Henrik
2016-04-01
Watershed segmentation is useful for a number of image segmentation problems with a wide range of practical applications. Traditionally, the tracking of the immersion front is done by applying a fast sorting algorithm. In this work, we explore a continuous approach based on a geometric description of the immersion front which gives rise to a partial differential equation. The main advantage of using a partial differential equation to track the immersion front is that the method becomes versatile and may easily be stabilized by introducing regularization terms. Coupling the geometric approach with a proper "merging strategy" creates a robust algorithm which minimizes over- and under-segmentation even without predefined markers. Since reliable markers defined prior to segmentation can be difficult to construct automatically for various reasons, being able to treat marker-free situations is a major advantage of the proposed method over earlier watershed formulations. The motivation for the methods developed in this paper is taken from high-throughput screening of cells. A fully automated segmentation of single cells enables the extraction of cell properties from large data sets, which can provide substantial insight into a biological model system. Applying smoothing to the boundaries can improve the accuracy in many image analysis tasks requiring a precise delineation of the plasma membrane of the cell. The proposed segmentation method is applied to real images containing fluorescently labeled cells, and the experimental results show that our implementation is robust and reliable for a variety of challenging segmentation tasks.
A Text Categorization Algorithm Based on Sense Group
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Jing Wan
2013-02-01
Full Text Available Giving further consideration on linguistic feature, this study proposes an algorithm of Chinese text categorization based on sense group. The algorithm extracts sense group by analyzing syntactic and semantic properties of Chinese texts and builds the category sense group library. SVM is used for the experiment of text categorization. The experimental results show that the precision and recall of the new algorithm based on sense group is better than that of traditional algorithms.
POWER OPTIMIZATION ALGORITHM BASED ON XNOR/OR LOGIC
Institute of Scientific and Technical Information of China (English)
Wang Pengjun; Lu Jingang; Xu Jian; Dai Jing
2009-01-01
Based on the investigation of the XNOR/OR logical expression and the propagation algorithm of signal probability, a low power synthesis algorithm based on the XNOR/OR logic is proposed in this paper. The proposed algorithm has been implemented with C language. Fourteen Microelectronics Center North Carolina (MCNC) benchmarks are tested, and the results show that the proposed algorithm not only significantly reduces the average power consumption up to 27% without area and delay compensations, but also makes the runtime shorter.
Institute of Scientific and Technical Information of China (English)
孙玉坤; 张瑶; 黄永红; 孙晓天
2011-01-01
In order to solve the problem of the existence of gross errors in data samples for soft sensing modeling, the complexity of the dynamic recursive fuzzy neural network's structure, and the difficulty in determining the massive parameters, a soft sensor based on immune genetic algorithm and dynamic recursive fuzzy neural network is proposed.Similarities between samples are analyzed by the way of computing Mahalanobis distance, the gross errors in data sample are removed to increase the computing speed.In addition, subtractive clustering is applied to determining the number of fuzzy rules in order to simplify the network structure, and at the same time an immune genetic algorithm is introduced to optimize the model parameters to enhance and its precision and generalization ability.The method is applied to biomass concentration soft measurement in the lysine fermentation process.The simulation example shows that the model has high prediction precision and good generalization ability, and it satisfies the need of spot measurement%针对软测量建模数据中过失误差及动态递归模糊神经网络的结构复杂,大量参数难以确定的情况,提出基于免疫遗传算法的动态递归模糊神经网络软测量方法.利用样本间马氏距离进行样本相似程度分析,去除样本中过失数据以提高计算速度.此外应用减法聚类确定模糊规则数,以简化网络结构,同时应用免疫遗传算法优化模型参数以提高模型的精度和泛化能力.将该方法应用于赖氨酸发酵过程菌体浓度的软测量,仿真结果表明,该方法具有较高的预测精度,满足现场测量要求.
Directory of Open Access Journals (Sweden)
Tinggui Chen
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.
Institute of Scientific and Technical Information of China (English)
Xiao-Hui Yang; Li-Cheng Jiao; Deng-Feng Li
2009-01-01
A directional filter algorithm for intensity synthetic aperture radar (SAR) image based on nonsubsampled contourlet transform (NSCT) and immune clonal selection (ICS) is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.
Performance evaluation of sensor allocation algorithm based on covariance control
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The covariance control capability of sensor allocation algorithms based on covariance control strategy is an important index to evaluate the performance of these algorithms. Owing to lack of standard performance metric indices to evaluate covariance control capability, sensor allocation ratio, etc, there are no guides to follow in the design procedure of sensor allocation algorithm in practical applications. To meet these demands, three quantified performance metric indices are presented, which are average covariance misadjustment quantity (ACMQ), average sensor allocation ratio (ASAR) and matrix metric influence factor (MMIF), where ACMQ, ASAR and MMIF quantify the covariance control capability, the usage of sensor resources and the robustness of sensor allocation algorithm, respectively. Meanwhile, a covariance adaptive sensor allocation algorithm based on a new objective function is proposed to improve the covariance control capability of the algorithm based on information gain. The experiment results show that the proposed algorithm have the advantage over the preceding sensor allocation algorithm in covariance control capability and robustness.
Chaos-Based Multipurpose Image Watermarking Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Congxu; LIAO Xuefeng; LI Zhihua
2006-01-01
To achieve the goal of image content authentication and copyright protection simultaneously, this paper presents a novel image dual watermarking method based on chaotic map. Firstly, the host image was split into many nonoverlapping small blocks, and the block-wise discrete cosine transform (DCT) is computed. Secondly, the robust watermarks, shuffled by the chaotic sequences, are embedded in the DC coefficients of blocks to achieve the goal of copyright protection. The semi-fragile watermarks, generated by chaotic map, are embedded in the AC coefficients of blocks to obtain the aim of image authentication. Both of them can be extracted without the original image. Simulation results demonstrate the effectiveness of our algorithm in terms of robustness and fragility.
Review: Image Encryption Using Chaos Based algorithms
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Er. Ankita Gaur
2014-03-01
Full Text Available Due to the development in the field of network technology and multimedia applications, every minute thousands of messages which can be text, images, audios, videos are created and transmitted over wireless network. Improper delivery of the message may leads to the leakage of important information. So encryption is used to provide security. In last few years, variety of image encryption algorithms based on chaotic system has been proposed to protect image from unauthorized access. 1-D chaotic system using logistic maps has weak security, small key space and due to the floating of pixel values, some data lose occurs and proper decryption of image becomes impossible. In this paper different chaotic maps such as Arnold cat map, sine map, logistic map, tent map have been studied.
An intersection algorithm based on transformation
Institute of Scientific and Technical Information of China (English)
CHEN Xiao-xia; YONG Jun-hai; CHEN Yu-jian
2006-01-01
How to obtain intersection of curves and surfaces is a fundamental problem in many areas such as computer graphics,CAD/CAM,computer animation,and robotics.Especially,how to deal with singular cases,such as tangency or superposition,is a key problem in obtaining intersection results.A method for solving the intersection problem based on the coordinate transformation is presented.With the Lagrange multiplier method,the minimum distance between the center of a circle and a quadric surface is given as well.Experience shows that the coordinate transformation could significantly simplify the method for calculating intersection to the tangency condition.It can improve the stability of the intersection of given curves and surfaces in singularity cases.The new algorithm is applied in a three dimensional CAD software (GEMS),produced by Tsinghua University.
An Improved Particle Swarm Optimization Algorithm Based on Ensemble Technique
Institute of Scientific and Technical Information of China (English)
SHI Yan; HUANG Cong-ming
2006-01-01
An improved particle swarm optimization (PSO) algorithm based on ensemble technique is presented. The algorithm combines some previous best positions (pbest) of the particles to get an ensemble position (Epbest), which is used to replace the global best position (gbest). It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.
Solving Multi Objective ORPD Problem Using AIS Based Clonal Selection Algorithm with UPFC
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B. Srinivasa Rao
2017-03-01
Full Text Available In this paper, a solution for the multi objective optimal reactive power dispatch problem by using an artificial immune system (AIS based clonal selection algorithm was presented. The proposed AIS based clonal selection algorithm uses cloning of antibodies and followed by hyper maturation to minimize the voltage stability index (L-index, voltage deviations at all load buses and the transmission real power losses by incorporating the multi type FACTS device namely the UPFC. The proposed algorithm also uses concepts of non dominated sorting and crowding distance comparison procedures to solve the multi objective optimization problem. Finally, a fuzzy decision maker strategy is applied to find the best compromise solution. The algorithm was implemented and tested on two standard IEEE 30-bus and 57-bus test systems with UPFC. The proposed results are compared with and without placing the UPFC by considering two objectives for optimization.
A New Aloha Anti-Collision Algorithm Based on CDMA
Bai, Enjian; Feng, Zhu
The tags' collision is a common problem in RFID (radio frequency identification) system. The problem has affected the integrity of the data transmission during the process of communication in the RFID system. Based on analysis of the existing anti-collision algorithm, a novel anti-collision algorithm is presented. The new algorithm combines the group dynamic frame slotted Aloha algorithm with code division multiple access technology. The algorithm can effectively reduce the collision probability between tags. Under the same number of tags, the algorithm is effective in reducing the reader recognition time and improve overall system throughput rate.
A research on fast FCM algorithm based on weighted sample
Institute of Scientific and Technical Information of China (English)
KUANG Ping; ZHU Qing-xin; WANG Ming-wen; CHEN Xu-dong; QING Li
2006-01-01
To improve the computational performance of the fuzzy C-means (FCM) algorithm used in dataset clustering with large numbers,the concepts of the equivalent samples and the weighting samples based on eigenvalue distribution of the samples in the feature space were introduced and a novel fast cluster algorithm named weighted fuzzy C-means (WFCM) algorithm was put forward,which came from the traditional FCM algorithm.It was proved that the duster results were equivalent in dataset with two different cluster algorithms:WFCM and FCM.Furthermore,the WFCM algorithm had better computational performance than the ordinary FCM algorithm.The experiment of the gray image segmentation showed that the WFCM algorithm is a fast and effective cluster algorithm.
An improved localization algorithm based on genetic algorithm in wireless sensor networks.
Peng, Bo; Li, Lei
2015-04-01
Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.
CANCER IMMUNOTHERAPY BASED ON THE BLOCKADE OF IMMUNE CHECKPOINTS
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A. V. Bogolyubova
2015-01-01
Full Text Available Immune checkpoints represent the system of inhibitory mechanisms regulating the activation of the immune response, preventing the autoimmune processes and modulating the immune response by decreasing the immune cell-mediated damage of tissues and organs. Tumor cells may utilize these checkpoints to prevent the activation of tumor-specific lymphocytes, thereby acquiring resistance against the immune response. The blockade of inhibitory signal that is transduced in immune checkpoints leading to the reactivation of antitumor immune response is a promising method of tumor immunotherapy. Since the majority of immune checkpoints are based on the ligand-receptor interactions, one of contemporary modalities of anti-tumor therapy is based on the development of ligandor receptor-blocking therapeutic monoclonal antibodies, as well as soluble recombinant receptors capable of competing for a ligand and thereby modulating the signal transduction. In the past few years, this field of tumor immunotherapy experienced an impressive success; however, the potential tradeoff for altering of the natural suppressive mechanisms is the development of the autoimmune reactions.
Uzawa Type Algorithm Based on Dual Mixed Variational Formulation
Institute of Scientific and Technical Information of China (English)
王光辉; 王烈衡
2002-01-01
Based on the dual mixed variational formulation with three variants (stress,displacement, displacement on contact boundary ) and the unilateral beaming problem of finite element discretization, an Uzawa type iterative algorithm is presented. The convergence of this iterative algorithm is proved, and then the efficiency of the algorithm is tested by a numerical example.
Replication-based Inference Algorithms for Hard Computational Problems
Alamino, Roberto C.; Neirotti, Juan P.; Saad, David
2013-01-01
Inference algorithms based on evolving interactions between replicated solutions are introduced and analyzed on a prototypical NP-hard problem - the capacity of the binary Ising perceptron. The efficiency of the algorithm is examined numerically against that of the parallel tempering algorithm, showing improved performance in terms of the results obtained, computing requirements and simplicity of implementation.
Network Intrusion Detection based on GMKL Algorithm
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Li Yuxiang
2013-06-01
Full Text Available According to the 31th statistical reports of China Internet network information center (CNNIC, by the end of December 2012, the number of Chinese netizens has reached 564 million, and the scale of mobile Internet users also reached 420 million. But when the network brings great convenience to people's life, it also brings huge threat in the life of people. So through collecting and analyzing the information in the computer system or network we can detect any possible behaviors that can damage the availability, integrity and confidentiality of the computer resource, and make timely treatment to these behaviors which have important research significance to improve the operation environment of network and network service. At present, the Neural Network, Support Vector machine (SVM and Hidden Markov Model, Fuzzy inference and Genetic Algorithms are introduced into the research of network intrusion detection, trying to build a healthy and secure network operation environment. But most of these algorithms are based on the total sample and it also hypothesizes that the number of the sample is infinity. But in the field of network intrusion the collected data often cannot meet the above requirements. It often shows high latitudes, variability and small sample characteristics. For these data using traditional machine learning methods are hard to get ideal results. In view of this, this paper proposed a Generalized Multi-Kernel Learning method to applied to network intrusion detection. The Generalized Multi-Kernel Learning method can be well applied to large scale sample data, dimension complex, containing a large number of heterogeneous information and so on. The experimental results show that applying GMKL to network attack detection has high classification precision and low abnormal practical precision.
Impact of community-based immunization services.
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Sing K
1986-07-01
Full Text Available The knowledge, attitude and practice of mothers toward childhood immunization was surveyed in 2 neighborhoods in greater Bombay, India. The areas were a slum of 75,000 called Malavani, and a nearby area called Kharodi. Measles and triple (DPT or DPV vaccines were available at local health centers, 1.5 km away at the most; oral polio vaccines were given by field workers to the Malavani community to children in their homes, but only in the center for those in Kharodi. BCG tuberculosis vaccinations were available to all, but from a center 5 km away. Malavani mothers had significantly better knowledge of triple and measles vaccines, but knowledge about BCG was similar in the 2 groups. Slightly more women from Kharodi expressed negative attitudes toward immunization. Coverage of children, established from clinic records, was significantly better in the Malavani area: 91% vs. 58% for polio; 71% vs 61% for BCG (n.s.; 85% vs. 55% for triple vaccine; and 21% vs 1% for measles. Evidently, visitation by field teams with polio vaccinations affected mothers′ knowledge and practice for other immunizations available only at the center.
Sadat Hashemipour, Maryam; Soleimani, Seyed Ali
2016-01-01
Artificial immune system (AIS) algorithm based on clonal selection method can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure along with the feature selection significantly impacts on the classification accuracy rate. In this study, AIS based on Adaptive Clonal Selection (AISACS) algorithm has been used to optimise the SVM parameters and feature subset selection without degrading the SVM classification accuracy. Several public datasets of University of California Irvine machine learning (UCI) repository are employed to calculate the classification accuracy rate in order to evaluate the AISACS approach then it was compared with grid search algorithm and Genetic Algorithm (GA) approach. The experimental results show that the feature reduction rate and running time of the AISACS approach are better than the GA approach.
An incremental clustering algorithm based on Mahalanobis distance
Aik, Lim Eng; Choon, Tan Wee
2014-12-01
Classical fuzzy c-means clustering algorithm is insufficient to cluster non-spherical or elliptical distributed datasets. The paper replaces classical fuzzy c-means clustering euclidean distance with Mahalanobis distance. It applies Mahalanobis distance to incremental learning for its merits. A Mahalanobis distance based fuzzy incremental clustering learning algorithm is proposed. Experimental results show the algorithm is an effective remedy for the defect in fuzzy c-means algorithm but also increase training accuracy.
Saudi License Plate Recognition Algorithm Based on Support Vector Machine
Institute of Scientific and Technical Information of China (English)
Khaled Suwais; Rana Al-Otaibi; Ali Alshahrani
2013-01-01
License plate recognition (LPR) is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine (SVM) algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.
A new classification algorithm based on RGH-tree search
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper, we put forward a new classification algorithm based on RGH-Tree search and perform the classification analysis and comparison study. This algorithm can save computing resource and increase the classification efficiency. The experiment shows that this algorithm can get better effect in dealing with three dimensional multi-kind data. We find that the algorithm has better generalization ability for small training set and big testing result.
The Result Integration Algorithm Based on Matching Strategy
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The following paper provides a new algorithm: a result integration algorithm based on matching strategy. The algorithm extracts the title and the abstract of Web pages, calculates the relevance between the query string and the Web pages, decides the Web pages accepted, rejected and sorts them out in user interfaces. The experiment results indicate obviously that the new algorithms improve the precision of meta-search engine. This technique is very useful to meta-search engine.
An Incremental Algorithm of Text Clustering Based on Semantic Sequences
Institute of Scientific and Technical Information of China (English)
FENG Zhonghui; SHEN Junyi; BAO Junpeng
2006-01-01
This paper proposed an incremental textclustering algorithm based on semantic sequence.Using similarity relation of semantic sequences and calculating the cover of similarity semantic sequences set, the candidate cluster with minimum entropy overlap value was selected as a result cluster every time in this algorithm.The comparison of experimental results shows that the precision of the algorithm is higher than other algorithms under same conditions and this is obvious especially on long documents set.
A generalized GPU-based connected component labeling algorithm
Komura, Yukihiro
2016-01-01
We propose a generalized GPU-based connected component labeling (CCL) algorithm that can be applied to both various lattices and to non-lattice environments in a uniform fashion. We extend our recent GPU-based CCL algorithm without the use of conventional iteration to the generalized method. As an application of this algorithm, we deal with the bond percolation problem. We investigate bond percolation on the honeycomb and triangle lattices to confirm the correctness of this algorithm. Moreover, we deal with bond percolation on the Bethe lattice as a substitute for a network structure, and demonstrate the performance of this algorithm on those lattices.
Fixed-point blind source separation algorithm based on ICA
Institute of Scientific and Technical Information of China (English)
Hongyan LI; Jianfen MA; Deng'ao LI; Huakui WANG
2008-01-01
This paper introduces the fixed-point learning algorithm based on independent component analysis (ICA);the model and process of this algorithm and simulation results are presented.Kurtosis was adopted as the estimation rule of independence.The results of the experiment show that compared with the traditional ICA algorithm based on random grads,this algorithm has advantages such as fast convergence and no necessity for any dynamic parameter,etc.The algorithm is a highly efficient and reliable method in blind signal separation.
Adaptive Central Force Optimization Algorithm Based on the Stability Analysis
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Weiyi Qian
2015-01-01
Full Text Available In order to enhance the convergence capability of the central force optimization (CFO algorithm, an adaptive central force optimization (ACFO algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.
Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems
Institute of Scientific and Technical Information of China (English)
Jin-hui Yang; Liang Sun; Heow Pueh Lee; Yun Qian; Yan-chun Liang
2008-01-01
A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki's neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.
Adaptive RED algorithm based on minority game
Wei, Jiaolong; Lei, Ling; Qian, Jingjing
2007-11-01
With more and more applications appearing and the technology developing in the Internet, only relying on terminal system can not satisfy the complicated demand of QoS network. Router mechanisms must be participated into protecting responsive flows from the non-responsive. Routers mainly use active queue management mechanism (AQM) to avoid congestion. In the point of interaction between the routers, the paper applies minority game to describe the interaction of the users and observes the affection on the length of average queue. The parameters α, β of ARED being hard to confirm, adaptive RED based on minority game can depict the interactions of main body and amend the parameter α, β of ARED to the best. Adaptive RED based on minority game optimizes ARED and realizes the smoothness of average queue length. At the same time, this paper extends the network simulator plat - NS by adding new elements. Simulation has been implemented and the results show that new algorithm can reach the anticipative objects.
Web Based Genetic Algorithm Using Data Mining
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Ashiqur Rahman
2016-09-01
Full Text Available This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; feature weighting is works better than just feature selection. Many leading educational institutions are working to establish an online teaching and learning presence. Several systems with different capabilities and approaches have been developed to deliver online education in an academic setting. In particular, Michigan State University (MSU has pioneered some of these systems to provide an infrastructure for online instruction. The research presented here was performed on a part of the latest online educational system developed at MSU, the Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA
Informatics-Based Discovery of Disease-Associated Immune Profiles
Delmas, Amber; Oikonomopoulos, Angelos; Lacey, Precious N.; Fallahi, Mohammad; Hommes, Daniel W.; Sundrud, Mark S.
2016-01-01
Advances in flow and mass cytometry are enabling ultra-high resolution immune profiling in mice and humans on an unprecedented scale. However, the resulting high-content datasets challenge traditional views of cytometry data, which are both limited in scope and biased by pre-existing hypotheses. Computational solutions are now emerging (e.g., Citrus, AutoGate, SPADE) that automate cell gating or enable visualization of relative subset abundance within healthy versus diseased mice or humans. Yet these tools require significant computational fluency and fail to show quantitative relationships between discrete immune phenotypes and continuous disease variables. Here we describe a simple informatics platform that uses hierarchical clustering and nearest neighbor algorithms to associate manually gated immune phenotypes with clinical or pre-clinical disease endpoints of interest in a rapid and unbiased manner. Using this approach, we identify discrete immune profiles that correspond with either weight loss or histologic colitis in a T cell transfer model of inflammatory bowel disease (IBD), and show distinct nodes of immune dysregulation in the IBDs, Crohn’s disease and ulcerative colitis. This streamlined informatics approach for cytometry data analysis leverages publicly available software, can be applied to manually or computationally gated cytometry data, is suitable for any clinical or pre-clinical setting, and embraces ultra-high content flow and mass cytometry as a discovery engine. PMID:27669154
DYNAMIC LABELING BASED FPGA DELAY OPTIMIZATION ALGORITHM
Institute of Scientific and Technical Information of China (English)
吕宗伟; 林争辉; 张镭
2001-01-01
DAG-MAP is an FPGA technology mapping algorithm for delay optimization and the labeling phase is the algorithm's kernel. This paper studied the labeling phase and presented an improved labeling method. It is shown through the experimental results on MCNC benchmarks that the improved method is more effective than the original method while the computation time is almost the same.
ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD
Institute of Scientific and Technical Information of China (English)
SONG Kaichen; NIE Xili
2006-01-01
Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion,are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.
Gradient-based Taxis Algorithms for Network Robotics
Blum, Christian; Hafner, Verena V.
2014-01-01
Finding the physical location of a specific network node is a prototypical task for navigation inside a wireless network. In this paper, we consider in depth the implications of wireless communication as a measurement input of gradient-based taxis algorithms. We discuss how gradients can be measured and determine the errors of this estimation. We then introduce a gradient-based taxis algorithm as an example of a family of gradient-based, convergent algorithms and discuss its convergence in th...
LEACH Algorithm Based on Load Balancing
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Wangang Wang
2013-09-01
Full Text Available This paper discusses advantages of LEACH Algorithm and the existing improved model which takes the famous hierarchy clustering routing protocol LEACH Algorithm as researching object. Then the paper indicates the problem that in the algorithm capacity factor of cluster head node is not taken into account leading the structure of clusters to be not so reasonable. This research discusses an energy-uniform cluster and cluster head selecting mechanism in which “Pseudo cluster head” concept is introduced in order to coordinate with “Load Monitor” Mechanism and “Load Leisure” Mechanism to maintain load balancing of cluster head character and stability of network topology. On the basis of LEACH Protocol improving algorithm of LEACH-C, CEFL and DCHS. NS2 simulation instrument is applied to do simulation analysis on the improved algorithm. Simulation result shows that LEACH-P Protocol effectively increase energy utilization efficiency, lengthens network lifetime and balances network load.
Combined string searching algorithm based on knuth-morris- pratt and boyer-moore algorithms
Tsarev, R. Yu; Chernigovskiy, A. S.; Tsareva, E. A.; Brezitskaya, V. V.; Nikiforov, A. Yu; Smirnov, N. A.
2016-04-01
The string searching task can be classified as a classic information processing task. Users either encounter the solution of this task while working with text processors or browsers, employing standard built-in tools, or this task is solved unseen by the users, while they are working with various computer programmes. Nowadays there are many algorithms for solving the string searching problem. The main criterion of these algorithms’ effectiveness is searching speed. The larger the shift of the pattern relative to the string in case of pattern and string characters’ mismatch is, the higher is the algorithm running speed. This article offers a combined algorithm, which has been developed on the basis of well-known Knuth-Morris-Pratt and Boyer-Moore string searching algorithms. These algorithms are based on two different basic principles of pattern matching. Knuth-Morris-Pratt algorithm is based upon forward pattern matching and Boyer-Moore is based upon backward pattern matching. Having united these two algorithms, the combined algorithm allows acquiring the larger shift in case of pattern and string characters’ mismatch. The article provides an example, which illustrates the results of Boyer-Moore and Knuth-Morris- Pratt algorithms and combined algorithm’s work and shows advantage of the latter in solving string searching problem.
A chaos-based image encryption algorithm using alternate structure
Institute of Scientific and Technical Information of China (English)
ZHANG YiWei; WANG YuMin; SHEN XuBang
2007-01-01
Combined with two chaotic maps, a novel alternate structure is applied to image cryptosystem. In proposed algorithm, a general cat-map is used for permutation and diffusion, as well as the OCML (one-way coupled map lattice), which is applied for substitution. These two methods are operated alternately in every round of encryption process, where two subkeys employed in different chaotic maps are generated through the masterkey spreading. Decryption has the same structure with the encryption algorithm, but the masterkey in each round should be reversely ordered in decryption. The cryptanalysis shows that the proposed algorithm bears good immunities to many forms of attacks. Moreover, the algorithm features high execution speed and compact program, which is suitable for various software and hardware applications.
An Innovative Thinking-Based Intelligent Information Fusion Algorithm
Directory of Open Access Journals (Sweden)
Huimin Lu
2013-01-01
Full Text Available This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.
Directory of Open Access Journals (Sweden)
Jiang Ting
2010-01-01
Full Text Available We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network.
Institute of Scientific and Technical Information of China (English)
曾宪钊; 成冀; 安欣; 方礼明
2002-01-01
This paper introduces a new Air Combat Intelligence Simulation System (ACISS) in a 32 versus 32 air combat, describes three methods: Genetic Algorithms (GA) in the multi-targeting decision and Evading Missile Rule Base learning, Neural Networks (NN) in the maneuvering decision, and Time Effectiveness Algorithm (TEA) in the adjudicating an air combat and the evaluating evading missile effectiveness.
Parallel Implementation of Classification Algorithms Based on Cloud Computing Environment
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Wenbo Wang
2012-09-01
Full Text Available As an important task of data mining, Classification has been received considerable attention in many applications, such as information retrieval, web searching, etc. The enlarging volumes of information emerging by the progress of technology and the growing individual needs of data mining, makes classifying of very large scale of data a challenging task. In order to deal with the problem, many researchers try to design efficient parallel classification algorithms. This paper introduces the classification algorithms and cloud computing briefly, based on it analyses the bad points of the present parallel classification algorithms, then addresses a new model of parallel classifying algorithms. And it mainly introduces a parallel Naïve Bayes classification algorithm based on MapReduce, which is a simple yet powerful parallel programming technique. The experimental results demonstrate that the proposed algorithm improves the original algorithm performance, and it can process large datasets efficiently on commodity hardware.
SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm
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Amandeep Kaur
2014-09-01
Full Text Available Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA. In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.
New Iris Localization Method Based on Chaos Genetic Algorithm
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Jia Dongli; Muhammad Khurram Khan; Zhang Jiashu
2005-01-01
This paper present a new method based on Chaos Genetic Algorithm (CGA) to localize the human iris in a given image. First, the iris image is preprocessed to estimate the range of the iris localization, and then CGA is used to extract the boundary of the iris. Simulation results show that the proposed algorithms is efficient and robust, and can achieve sub pixel precision. Because Genetic Algorithms (GAs) can search in a large space, the algorithm does not need accurate estimation of iris center for subsequent localization, and hence can lower the requirement for original iris image processing. On this point, the present localization algirithm is superior to Daugmans algorithm.
A Wire-speed Routing Lookup Algorithm Based on TCAM
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李小勇; 王志恒; 白英彩; 刘刚
2004-01-01
An internal structure of Ternary Content Addressable Memory (TCAM) is designed and a Sorting Prefix Block (SPB) algorithm is presented, which is a wire-speed routing lookup algorithm based on TCAM. SPB algorithm makes use of the parallelism of TCAM adequately, and improves the utilization of TCAM by optimum partitions. With the aid of effective management algorithm and memory image, SPB separates critical searching from assistant searching, and improves the searching effect. One performance test indicates that this algorithm can work with different TCAM to meet the requirement of wire-speed routing lookup.
Information criterion based fast PCA adaptive algorithm
Institute of Scientific and Technical Information of China (English)
Li Jiawen; Li Congxin
2007-01-01
The novel information criterion (NIC) algorithm can find the principal subspace quickly, but it is not an actual principal component analysis (PCA) algorithm and hence it cannot find the orthonormal eigen-space which corresponds to the principal component of input vector.This defect limits its application in practice.By weighting the neural network's output of NIC, a modified novel information criterion (MNIC) algorithm is presented.MNIC extractes the principal components and corresponding eigenvectors in a parallel online learning program, and overcomes the NIC's defect.It is proved to have a single global optimum and nonquadratic convergence rate, which is superior to the conventional PCA online algorithms such as Oja and LMSER.The relationship among Oja, LMSER and MNIC is exhibited.Simulations show that MNIC could converge to the optimum fast.The validity of MNIC is proved.
Sublingual immunization with M2-based vaccine induces broad protective immunity against influenza.
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Byoung-Shik Shim
Full Text Available BACKGROUND: The ectodomain of matrix protein 2 (M2e of influenza A virus is a rationale target antigen candidate for the development of a universal vaccine against influenza as M2e undergoes little sequence variation amongst human influenza A strains. Vaccine-induced M2e-specific antibodies (Abs have been shown to display significant cross-protective activity in animal models. M2e-based vaccine constructs have been shown to be more protective when administered by the intranasal (i.n. route than after parenteral injection. However, i.n. administration of vaccines poses rare but serious safety issues associated with retrograde passage of inhaled antigens and adjuvants through the olfactory epithelium. In this study, we examined whether the sublingual (s.l. route could serve as a safe and effective alternative mucosal delivery route for administering a prototype M2e-based vaccine. The mechanism whereby s.l. immunization with M2e vaccine candidate induces broad protection against infection with different influenza virus subtypes was explored. METHODS AND RESULTS: A recombinant M2 protein with three tandem copies of the M2e (3M2eC was expressed in Escherichia coli. Parenteral immunizations of mice with 3M2eC induced high levels of M2e-specific serum Abs but failed to provide complete protection against lethal challenge with influenza virus. In contrast, s.l. immunization with 3M2eC was superior for inducing protection in mice. In the latter animals, protection was associated with specific Ab responses in the lungs. CONCLUSIONS: The results demonstrate that s.l. immunization with 3M2eC vaccine induced airway mucosal immune responses along with broad cross-protective immunity to influenza. These findings may contribute to the understanding of the M2-based vaccine approach to control epidemic and pandemic influenza infections.
A Multi-Scale Gradient Algorithm Based on Morphological Operators
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Watershed transformation is a powerful morphological tool for image segmentation. However, the performance of the image segmentation methods based on watershed transformation depends largely on the algorithm for computing the gradient of the image to be segmented. In this paper, we present a multi-scale gradient algorithm based on morphological operators for watershed-based image segmentation, with effective handling of both step and blurred edges. We also present an algorithm to eliminate the local minima produced by noise and quantization errors. Experimental results indicate that watershed transformation with the algorithms proposed in this paper produces meaningful segmentations, even without a region-merging step.
Cycle-Based Algorithm Used to Accelerate VHDL Simulation
Institute of Scientific and Technical Information of China (English)
杨勋; 刘明业
2000-01-01
Cycle-based algorithm has very high performance for the simula-tion of synchronous design, but it is confined to synchronous design and it is not as accurate as event-driven algorithm. In this paper, a revised cycle-based algorithm is proposed and implemented in VHDL simulator. Event-driven simulation engine and cycle-based simulation engine have been imbedded in the same simulation environ-ment and can be used to asynchronous design and synchronous design respectively. Thus the simulation performance is improved without losing the flexibility and ac-curacy of event-driven algorithm.
QOS-BASED MULTICAST ROUTING OPTIMIZATION ALGORITHMS FOR INTERNET
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Most of the multimedia applications require strict Quality-of-Service (QoS) guarantee during the communication between a single source and multiple destinations. The paper mainly presents a QoS Multicast Routing algorithms based on Genetic Algorithm (QMRGA). Simulation results demonstrate that the algorithm is capable of discovering a set of QoS-based near optimized, non-dominated multicast routes within a few iterations, even for the networks environment with uncertain parameters.
Institute of Scientific and Technical Information of China (English)
Mojtahedi,A.; Lotfollahi Yaghin,M.A.; Hassanzadeh,Y.; Abbasidoust,F.; Ettefagh,M.M.; Aminfar,M.H.
2012-01-01
Steel jacket-type platforms are the common kind of the offshore structures and health monitoring is an important issue in their safety assessment.In the present study,a new damage detection method is adopted for this kind of structures and inspected experimentally by use of a laboratory model.The method is investigated for developing the robust damage detection technique which is less sensitive to both measurement and analytical model uncertainties.For this purpose,incorporation of the artificial immune system with weighted attributes (AISWA) method into finite element (FE) model updating is proposed and compared with other methods for exploring its effectiveness in damage identification.Based on mimicking immune recognition,noise simulation and attributes weighting,the method offers important advantages and has high success rates.Therefore,it is proposed as a suitable method for the detection of the failures in the large civil engineering structures with complicated structural geometry,such as the considered case study.
Improved FCLSD algorithm based on LTE/LTE-A system
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Kewen Liu
2011-08-01
Full Text Available In order to meet the high data rate, large capacity and low latency in LTE, advanced MIMO technology has been introduced in LTE system, which becomes one of the core technologies in physical layer. In a variety of MIMO detection algorithms, the ZF and MMSE linear detection algorithms are the most simple, but the performance is poor. MLD algorithm can achieve optimal detection performance, but it’s too complexity to be applied in practice. CLSD algorithm has similar detection performance and lower complexity with the MLD algorithm, but the uncertainty of complexity will bring hardware difficulties. FCLSD algorithm can maximize the advantages of CLSD algorithm and solve difficult problems in practice. Based on advanced FCLSD algorithm and combined with LTE / LTE-A practical system applications, this article designed two improved algorithms. The two improved algorithms can be flexibly and adaptively used in various antenna configurations and modulation scene in LTE / LTE-A spatial multiplexing MIMO system. The Simulation results show that the improved algorithm can achieve an approximate performance to the original FCLSD algorithm; in addition, it has a fixed complexity and could be carried out by parallel processing.
A Novel Rule Induction Algorithm
Institute of Scientific and Technical Information of China (English)
ZHENG Jianguo; LIU Fang; WANG Lei; JIAO Licheng
2001-01-01
Knowledge discovery in databases is concerned with extracting useful information from databases, and the immune algorithm is a biological theory-based and globally searching algorithm. A specific immune algorithm is designed for discovering a few interesting, high-level prediction rules from databases, rather than discovering classification knowledge as usual in the literatures. Simulations show that this novel algorithm is able to improve the stability of the population, increase the holistic performance and make the rules extracted have higher precision.
List-Based Simulated Annealing Algorithm for Traveling Salesman Problem.
Zhan, Shi-hua; Lin, Juan; Zhang, Ze-jun; Zhong, Yi-wen
2016-01-01
Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters' setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.
Enterprise Human Resources Information Mining Based on Improved Apriori Algorithm
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Lei He
2013-05-01
Full Text Available With the unceasing development of information and technology in today’s modern society, enterprises’ demand of human resources information mining is getting bigger and bigger. Based on the enterprise human resources information mining situation, this paper puts forward a kind of improved Apriori algorithm based model on the enterprise human resources information mining, this model introduced data mining technology and traditional Apriori algorithm, and improved on its basis, divided the association rules mining task of the original algorithm into two subtasks of producing frequent item sets and producing rule, using SQL technology to directly generating frequent item sets, and using the method of establishing chart to extract the information which are interested to customers. The experimental results show that the improved Apriori algorithm based model on the enterprise human resources information mining is better in efficiency than the original algorithm, and the practical application test results show that the improved algorithm is practical and effective.
Clonal Selection Algorithm Based Iterative Learning Control with Random Disturbance
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Yuanyuan Ju
2013-01-01
Full Text Available Clonal selection algorithm is improved and proposed as a method to solve optimization problems in iterative learning control. And a clonal selection algorithm based optimal iterative learning control algorithm with random disturbance is proposed. In the algorithm, at the same time, the size of the search space is decreased and the convergence speed of the algorithm is increased. In addition a model modifying device is used in the algorithm to cope with the uncertainty in the plant model. In addition a model is used in the algorithm cope with the uncertainty in the plant model. Simulations show that the convergence speed is satisfactory regardless of whether or not the plant model is precise nonlinear plants. The simulation test verify the controlled system with random disturbance can reached to stability by using improved iterative learning control law but not the traditional control law.
An optimal scheduling algorithm based on task duplication
Institute of Scientific and Technical Information of China (English)
Ruan Youlin; Liu Gan; Zhu Guangxi; Lu Xiaofeng
2005-01-01
When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O ( v2 ), where v represents the number of tasks.
OPTIMIZATION BASED ON LMPROVED REAL—CODED GENETIC ALGORITHM
Institute of Scientific and Technical Information of China (English)
ShiYu; YuShenglin
2002-01-01
An improved real-coded genetic algorithm is pro-posed for global optimization of functionsl.The new algo-rithm is based om the judgement of the searching perfor-mance of basic real-coded genetic algorithm.The opera-tions of basic real-coded genetic algorithm are briefly dis-cussed and selected.A kind of chaos sequence is described in detail and added in the new algorithm ad a disturbance factor.The strategy of field partition is also used to im-prove the strcture of the new algorithm.Numerical ex-periment shows that the mew genetic algorithm can find the global optimum of complex funtions with satistaiting precision.
Robust adaptive beamforming algorithm based on Bayesian approach
Institute of Scientific and Technical Information of China (English)
Xin SONG; Jinkuan WANG; Yinghua HAN; Han WANG
2008-01-01
The performance of adaptive array beamform-ing algorithms substantially degrades in practice because of a slight mismatch between actual and presumed array res-ponses to the desired signal. A novel robust adaptive beam-forming algorithm based on Bayesian approach is therefore proposed. The algorithm responds to the current envi-ronment by estimating the direction of arrival (DOA) of the actual signal from observations. Computational com-plexity of the proposed algorithm can thus be reduced com-pared with other algorithms since the recursive method is used to obtain inverse matrix. In addition, it has strong robustness to the uncertainty of actual signal DOA and makes the mean output array signal-to-interference-plus-noise ratio (SINR) consistently approach the optimum. Simulation results show that the proposed algorithm is bet-ter in performance than conventional adaptive beamform-ing algorithms.
New Gradient-Based Variable Step Size LMS Algorithms
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Yanling Hao
2008-03-01
Full Text Available Two new gradient-based variable step size least-mean-square (VSSLMS algorithms are proposed on the basis of a concise assessment of the weaknesses of previous VSSLMS algorithms in high-measurement noise environments. The first algorithm is designed for applications where the measurement noise signal is statistically stationary and the second for statistically nonstationary noise. Steady-state performance analyses are provided for both algorithms and verified by simulations. The proposed algorithms are also confirmed by simulations to obtain both a fast convergence rate and a small steady-state excess mean square error (EMSE, and to outperform existing VSSLMS algorithms. To facilitate practical application, parameter choice guidelines are provided for the new algorithms.
Generating Decision Trees Method Based on Improved ID3 Algorithm
Institute of Scientific and Technical Information of China (English)
Yang Ming; Guo Shuxu1; Wang Jun3
2011-01-01
The ID3 algorithm is a classical learning algorithm of decision tree in data mining.The algorithm trends to choosing the attribute with more values,affect the efficiency of classification and prediction for building a decision tree.This article proposes a new approach based on an improved ID3 algorithm.The new algorithm introduces the importance factor λ when calculating the information entropy.It can strengthen the label of important attributes of a tree and reduce the label of non-important attributes.The algorithm overcomes the flaw of the traditional ID3 algorithm which tends to choose the attributes with more values,and also improves the efficiency and flexibility in the process of generating decision trees.
Fuzzy Rules for Ant Based Clustering Algorithm
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Amira Hamdi
2016-01-01
Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.
A Recommender System based on Idiotypic Artificial Immune Networks
Cayzer, Steve
2008-01-01
The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.
A Recommender System based on the Immune Network
Steve, Cayzer
2008-01-01
The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen - antibody interaction for matching and antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.
Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition
Huntsberger, Terry
2011-01-01
The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.
Dynamic detection for computer virus based on immune system
Institute of Scientific and Technical Information of China (English)
LI Tao
2008-01-01
Inspired by biological immune system,a new dynamic detection model for computer virus based on immune system is proposed.The quantitative description of the model is given.The problem of dynamic description for self and nonself in a computer virus immune system is solved,which reduces the size of self set.The new concept of dynamic tolerance,as well as the new mechanisms of gene evolution and gene coding for immature detectors is presented,improving the generating efficiency of mature detectors,reducing the false-negative and false-positive rates.Therefore,the difficult problem,in which the detector training cost is exponentially related to the size of self-set in a traditional computer immune system,is thus overcome.The theory analysis and experimental results show that the proposed model has better time efficiency and detecting ability than the classic model ARTIS.
A POCS-Based Algorithm for Blocking Artifacts Reduction
Institute of Scientific and Technical Information of China (English)
ZHAO Yi-hong; CHENG Guo-hua; YU Song-yu
2006-01-01
An algorithm for blocking artifacts reduction in DCT domain for block-based image coding was developed. The algorithm is based on the projection onto convex set (POCS) theory. Due to the fact that the DCT characteristics of shifted blocks are different caused by the blocking artifacts, a novel smoothness constraint set and the corresponding projection operator were proposed to reduce the blocking artifacts by discarding the undesired high frequency coefficients in the shifted DCT blocks. The experimental results show that the proposed algorithm outperforms the conventional algorithms in terms of objective quality, subjective quality, and convergence property.
A Parallel Encryption Algorithm Based on Piecewise Linear Chaotic Map
Directory of Open Access Journals (Sweden)
Xizhong Wang
2013-01-01
Full Text Available We introduce a parallel chaos-based encryption algorithm for taking advantage of multicore processors. The chaotic cryptosystem is generated by the piecewise linear chaotic map (PWLCM. The parallel algorithm is designed with a master/slave communication model with the Message Passing Interface (MPI. The algorithm is suitable not only for multicore processors but also for the single-processor architecture. The experimental results show that the chaos-based cryptosystem possesses good statistical properties. The parallel algorithm provides much better performance than the serial ones and would be useful to apply in encryption/decryption file with large size or multimedia.
Heuristic Reduction Algorithm Based on Pairwise Positive Region
Institute of Scientific and Technical Information of China (English)
QI Li; LIU Yu-shu
2007-01-01
To guarantee the optimal reduct set, a heuristic reduction algorithm is proposed, which considers the distinguishing information between the members of each pair decision classes. Firstly the pairwise positive region is defined, based on which the pairwise significance measure is calculated between the members of each pair classes. Finally the weighted pairwise significance of attribute is used as the attribute reduction criterion, which indicates the necessity of attributes very well. By introducing the noise tolerance factor, the new algorithm can tolerate noise to some extent. Experimental results show the advantages of our novel heuristic reduction algorithm over the traditional attribute dependency based algorithm.
Survey of gene splicing algorithms based on reads.
Si, Xiuhua; Wang, Qian; Zhang, Lei; Wu, Ruo; Ma, Jiquan
2017-09-05
Gene splicing is the process of assembling a large number of unordered short sequence fragments to the original genome sequence as accurately as possible. Several popular splicing algorithms based on reads are reviewed in this article, including reference genome algorithms and de novo splicing algorithms (Greedy-extension, Overlap-Layout-Consensus graph, De Bruijn graph). We also discuss a new splicing method based on the MapReduce strategy and Hadoop. By comparing these algorithms, some conclusions are drawn and some suggestions on gene splicing research are made.
PHC: A Fast Partition and Hierarchy-Based Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
ZHOU HaoFeng(周皓峰); YUAN QingQing(袁晴晴); CHENG ZunPing(程尊平); SHI BaiLe(施伯乐)
2003-01-01
Cluster analysis is a process to classify data in a specified data set. In this field,much attention is paid to high-efficiency clustering algorithms. In this paper, the features in thecurrent partition-based and hierarchy-based algorithms are reviewed, and a new hierarchy-basedalgorithm PHC is proposed by combining advantages of both algorithms, which uses the cohesionand the closeness to amalgamate the clusters. Compared with similar algorithms, the performanceof PHC is improved, and the quality of clustering is guaranteed. And both the features were provedby the theoretic and experimental analyses in the paper.
Institute of Scientific and Technical Information of China (English)
彭坤; 果琳丽; 向开恒; 王平; 杨雷
2014-01-01
载人登月任务中，任务中止策略设计是确保航天员安全返回的重要基础。首先结合“星座”计划飞行方案分析了载人登月任务各飞行阶段的中止策略；其次针对地月转移巡航段进行了双脉冲中止策略设计，以速度增量数值、方位角以及变轨时间间隔为控制变量，加入轨道同向、近地点高度、偏心率以及飞行时间约束，提出双脉冲变轨计算流程；最后采用人工免疫算法对该问题进行了求解和优化。仿真算例表明，双脉冲中止策略存在多组解，其全局分布特性为：飞行时间越短速度增量需求越大；飞行时间相近时，大偏心率中止轨道对应的速度增量小；故障点离地月加速点越近，所需速度增量越小。同时也验证了人工免疫算法求解双脉冲中止策略问题的有效性。%Abort strategy design for manned lunar missions is an important basis to ensure the safe return of the crew to Earth .First, abort strategy in different phases of manned lunar missions were analyzed based on the flight plan of Constellation program .Then two-impulse abort strategy for trans-lunar coast was designed , and a calculation process of two-impulse transfer was proposed , in which velocity increment value , velocity azimuth and interval of two impulse were considered as control variable , orbit in the same direction , perigee height , eccentricity and flight time were joined as con-straints .Finally , an artificial immune algorithm was used to solve and optimize this problem .Simu-lation results showed that , two-impulse abort strategy has multiple solutions , and their global distri-bution character is that shorter flight time corresponds to greater velocity increment , larger eccentric-ity abort orbit has smaller velocity increment when flight time is similar .When the failure time is closer to translunar injection , the velocity increment required is smaller .The effectiveness of artifi
Brain MR image segmentation improved algorithm based on probability
Liao, Hengxu; Liu, Gang; Guo, Xiantang
2017-08-01
Local weight voting algorithm is a kind of current mainstream segmentation algorithm. It takes full account of the influences of the likelihood of image likelihood and the prior probabilities of labels on the segmentation results. But this method still can be improved since the essence of this method is to get the label with the maximum probability. If the probability of a label is 70%, it may be acceptable in mathematics. But in the actual segmentation, it may be wrong. So we use the matrix completion algorithm as a supplement. When the probability of the former is larger, the result of the former algorithm is adopted. When the probability of the later is larger, the result of the later algorithm is adopted. This is equivalent to adding an automatic algorithm selection switch that can theoretically ensure that the accuracy of the algorithm we propose is superior to the local weight voting algorithm. At the same time, we propose an improved matrix completion algorithm based on enumeration method. In addition, this paper also uses a multi-parameter registration model to reduce the influence that the registration made on the segmentation. The experimental results show that the accuracy of the algorithm is better than the common segmentation algorithm.
Adaptive image contrast enhancement algorithm for point-based rendering
Xu, Shaoping; Liu, Xiaoping P.
2015-03-01
Surgical simulation is a major application in computer graphics and virtual reality, and most of the existing work indicates that interactive real-time cutting simulation of soft tissue is a fundamental but challenging research problem in virtual surgery simulation systems. More specifically, it is difficult to achieve a fast enough graphic update rate (at least 30 Hz) on commodity PC hardware by utilizing traditional triangle-based rendering algorithms. In recent years, point-based rendering (PBR) has been shown to offer the potential to outperform the traditional triangle-based rendering in speed when it is applied to highly complex soft tissue cutting models. Nevertheless, the PBR algorithms are still limited in visual quality due to inherent contrast distortion. We propose an adaptive image contrast enhancement algorithm as a postprocessing module for PBR, providing high visual rendering quality as well as acceptable rendering efficiency. Our approach is based on a perceptible image quality technique with automatic parameter selection, resulting in a visual quality comparable to existing conventional PBR algorithms. Experimental results show that our adaptive image contrast enhancement algorithm produces encouraging results both visually and numerically compared to representative algorithms, and experiments conducted on the latest hardware demonstrate that the proposed PBR framework with the postprocessing module is superior to the conventional PBR algorithm and that the proposed contrast enhancement algorithm can be utilized in (or compatible with) various variants of the conventional PBR algorithm.
Research on Algorithms for Mining Distance-Based Outliers
Institute of Scientific and Technical Information of China (English)
WANGLizhen; ZOULikun
2005-01-01
The outlier detection is an important and valuable research in KDD (Knowledge discover in database). The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even weather forecast. In existing methods that we have seen for finding outliers, the notion of DB-(Distance-based) outliers is not restricted computationally to small values of the number of dimensions k and goes beyond the data space. Here, we study algorithms for mining DB-outliers. We focus on developing algorithms unlimited by k. First, we present a Partition-based algorithm (the PBA). The key idea is to gain efficiency by divide-and-conquer. Second, we present an optimized algorithm called Object-class-based algorithm (the OCBA). The computing of this algorithm has nothing to do with k and the efficiency of this algorithm is as good as the cell-based algorithm. We provide experimental results showing that the two new algorithms have better execution efficiency.
Grover quantum searching algorithm based on weighted targets
Institute of Scientific and Technical Information of China (English)
Li Panchi; Li Shiyong
2008-01-01
The current Grover quantum searching algorithm cannot identify the difference in importance of the search targets when it is applied to an unsorted quantum database, and the probability for each search target is equal. To solve this problem, a Grover searching algorithm based on weighted targets is proposed. First, each target is endowed a weight coefficient according to its importance. Applying these different weight coefficients, the targets are represented as quantum superposition states. Second, the novel Grover searching algorithm based on the quantum superposition of the weighted targets is constructed. Using this algorithm, the probability of getting each target can be approximated to the corresponding weight coefficient, which shows the flexibility of this algorithm.Finally, the validity of the algorithm is proved by a simple searching example.
Human resource recommendation algorithm based on ensemble learning and Spark
Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie
2017-08-01
Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.
Lazy learner text categorization algorithm based on embedded feature selection
Institute of Scientific and Technical Information of China (English)
Yan Peng; Zheng Xuefeng; Zhu Jianyong; Xiao Yunhong
2009-01-01
To avoid the curse of dimensionality, text categorization (TC) algorithms based on machine learning (ML) have to use an feature selection (FS) method to reduce the dimensionality of feature space. Although having been widely used, FS process will generally cause information losing and then have much side-effect on the whole performance of TC algorithms. On the basis of the sparsity characteristic of text vectors, a new TC algorithm based on lazy feature selection (LFS) is presented. As a new type of embedded feature selection approach, the LFS method can greatly reduce the dimension of features without any information losing, which can improve both efficiency and performance of algorithms greatly. The experiments show the new algorithm can simultaneously achieve much higher both performance and efficiency than some of other classical TC algorithms.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
A real time vehicles detection algorithm for vision based sensors
Płaczek, Bartłomiej
2011-01-01
A vehicle detection plays an important role in the traffic control at signalised intersections. This paper introduces a vision-based algorithm for vehicles presence recognition in detection zones. The algorithm uses linguistic variables to evaluate local attributes of an input image. The image attributes are categorised as vehicle, background or unknown features. Experimental results on complex traffic scenes show that the proposed algorithm is effective for a real-time vehicles detection.
Algorithm Research of Individualized Travelling Route Recommendation Based on Similarity
Xue Shan; Liu Song
2015-01-01
Although commercial recommendation system has made certain achievement in travelling route development, the recommendation system is facing a series of challenges because of people’s increasing interest in travelling. It is obvious that the core content of the recommendation system is recommendation algorithm. The advantages of recommendation algorithm can bring great effect to the recommendation system. Based on this, this paper applies traditional collaborative filtering algorithm for analy...
A New RWA Algorithm Based on Multi-Objective
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
In this article, we studied the associated research problems and challenges on routing and wavelength assignment (RWA) in WDM (wavelength division multiplexing) networks. Various RWA approaches are examined and compared. We proposed a new RWA algorithm based on multi-objective. In this new algorithm, we consider multiple network optimizing objectives to setup a lightpath with maximize profit and shortest path under the limited resources. By comparing and analyzing, the proposed algorithm is much better ...
Variable Neighborhood Search Based Algorithm for University Course Timetabling Problem
Kralev, Velin; Kraleva, Radoslava
2016-01-01
In this paper a variable neighborhood search approach as a method for solving combinatoric optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algorithms that are tested on the same sets of input data. The object and the methodology of study are p...
TOA estimation algorithm based on multi-search
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A new time of arrival (TOA) estimation algorithm is proposed. The algorithm computes the optimal sub-correlation length based on the SNR theory. So the robust of TOA acquirement is guaranteed very well. Then, according to the actual transmission environment and network system, the multi-search method is given. From the simulation result,the algorithm shows a very high application value in the realization of wireless location system (WLS).
Variable Neighborhood Search Based Algorithm for University Course Timetabling Problem
Kralev, Velin; Kraleva, Radoslava
2016-01-01
In this paper a variable neighborhood search approach as a method for solving combinatoric optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algorithms that are tested on the same sets of input data. The object and the methodology of study are p...
Hindi Parser-based on CKY algorithm
Nitin Hambir; Ambrish Srivastav
2012-01-01
Hindi parser is a tool which takes Hindi sentence and verifies whether or not given Hindi sentence is correct according to Hindi language grammar. Parsing is important for Natural Language Processing tools. Hindi parser uses the CKY (Coke- Kasami-Younger) parsing algorithm for Parsing of Hindi language. It parses whole sentence and generate a matrix
Persisting viral sequences shape microbial CRISPR-based immunity.
Directory of Open Access Journals (Sweden)
Ariel D Weinberger
Full Text Available Well-studied innate immune systems exist throughout bacteria and archaea, but a more recently discovered genomic locus may offer prokaryotes surprising immunological adaptability. Mediated by a cassette-like genomic locus termed Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR, the microbial adaptive immune system differs from its eukaryotic immune analogues by incorporating new immunities unidirectionally. CRISPR thus stores genomically recoverable timelines of virus-host coevolution in natural organisms refractory to laboratory cultivation. Here we combined a population genetic mathematical model of CRISPR-virus coevolution with six years of metagenomic sequencing to link the recoverable genomic dynamics of CRISPR loci to the unknown population dynamics of virus and host in natural communities. Metagenomic reconstructions in an acid-mine drainage system document CRISPR loci conserving ancestral immune elements to the base-pair across thousands of microbial generations. This 'trailer-end conservation' occurs despite rapid viral mutation and despite rapid prokaryotic genomic deletion. The trailer-ends of many reconstructed CRISPR loci are also largely identical across a population. 'Trailer-end clonality' occurs despite predictions of host immunological diversity due to negative frequency dependent selection (kill the winner dynamics. Statistical clustering and model simulations explain this lack of diversity by capturing rapid selective sweeps by highly immune CRISPR lineages. Potentially explaining 'trailer-end conservation,' we record the first example of a viral bloom overwhelming a CRISPR system. The polyclonal viruses bloom even though they share sequences previously targeted by host CRISPR loci. Simulations show how increasing random genomic deletions in CRISPR loci purges immunological controls on long-lived viral sequences, allowing polyclonal viruses to bloom and depressing host fitness. Our results thus link
A highlight on lipid based nanocarriers for transcutaneous immunization.
Nasr, Maha; Abdel-Hamid, Sameh; Alyoussef, Abdullah A
2015-01-01
Transcutaneous vaccination has become a widely used technique for providing immunity against several types of pathogens, taking advantage of the immune components found in the skin. The success in the field of vaccination has not only relied on the type of antigen and adjuvant delivered, but also on how they are delivered. In this regard, particulate carriers, especially nanoparticles have evoked considerable interest, owing to the desirable properties that they impart to the substance being delivered. The presentation of antigens by the nanoparticles mimics the presentation of the immunogen by the pathogen; hence, it creates a similar immune response. Furthermore, nanoparticles protect the antigen from degradation and allow its prolonged release, which maximizes its exposure to the immune cells. The most commonly used materials for the formulation of nanoparticles are either polymer-based or lipid based. This review will focus on the lipid based nanocarriers, either vesicular such as liposomes, transfersomes, and ethosomes, or non-vesicular such as cubosomes, solid lipid nanoparticles, nano-structured lipid carriers, solid in oil nanodispersions, lipoplexes, and hybrid polymeric-lipidic systems. The applications of these carriers in the field of transcutaneous immunization will be discussed in this review as well.
Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis
Directory of Open Access Journals (Sweden)
S. Muthurajkumar
2014-05-01
Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.
A Vehicle Detection Algorithm Based on Deep Belief Network
Directory of Open Access Journals (Sweden)
Hai Wang
2014-01-01
Full Text Available Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.
A PRESSURE-BASED ALGORITHM FOR CAVITATING FLOW COMPUTATIONS
Institute of Scientific and Technical Information of China (English)
ZHANG Ling-xin; ZHAO Wei-guo; SHAO Xue-ming
2011-01-01
A pressure-based algorithm for the prediction of cavitating flows is presented. The algorithm employs a set of equations including the Navier-Stokes equations and a cavitation model explaining the phase change between liquid and vapor. A pressure-based method is used to construct the algorithm and the coupling between pressure and velocity is considered. The pressure correction equation is derived from a new continuity equation which employs a source term related to phase change rate instead of the material derivative of density Dp/Dt.Thispressure-based algorithm allows for the computation of steady or unsteady,2-Dor 3-D cavitating flows. Two 2-D cases, flows around a flat-nose cylinder and around a NACA0015 hydrofoil, are simulated respectively, and the periodic cavitation behaviors associated with the re-entrant jets are captured. This algorithm shows good capability of computating time-dependent cavitating flows.
New Iterated Decoding Algorithm Based on Differential Frequency Hopping System
Institute of Scientific and Technical Information of China (English)
LIANG Fu-lin; LUO Wei-xiong
2005-01-01
A new iterated decoding algorithm is proposed for differential frequency hopping (DFH) encoder concatenated with multi-frequency shift-key (MFSK) modulator. According to the character of the frequency hopping (FH) pattern trellis produced by DFH function, maximum a posteriori (MAP) probability theory is applied to realize the iterate decoding of it. Further, the initial conditions for the new iterate algorithm based on MAP algorithm are modified for better performance. Finally, the simulation result compared with that from traditional algorithms shows good anti-interference performance.
Topology control based on quantum genetic algorithm in sensor networks
Institute of Scientific and Technical Information of China (English)
SUN Lijuan; GUO Jian; LU Kai; WANG Ruchuan
2007-01-01
Nowadays,two trends appear in the application of sensor networks in which both multi-service and quality of service (QoS)are supported.In terms of the goal of low energy consumption and high connectivity,the control on topology is crucial.The algorithm of topology control based on quantum genetic algorithm in sensor networks is proposed.An advantage of the quantum genetic algorithm over the conventional genetic algorithm is demonstrated in simulation experiments.The goals of high connectivity and low consumption of energy are reached.
Agent-based Algorithm for Spatial Distribution of Objects
Collier, Nathan
2012-06-02
In this paper we present an agent-based algorithm for the spatial distribution of objects. The algorithm is a generalization of the bubble mesh algorithm, initially created for the point insertion stage of the meshing process of the finite element method. The bubble mesh algorithm treats objects in space as bubbles, which repel and attract each other. The dynamics of each bubble are approximated by solving a series of ordinary differential equations. We present numerical results for a meshing application as well as a graph visualization application.
Support vector classification algorithm based on variable parameter linear programming
Institute of Scientific and Technical Information of China (English)
Xiao Jianhua; Lin Jian
2007-01-01
To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed.In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model.The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given.An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.
A multicast dynamic wavelength assignment algorithm based on matching degree
Institute of Scientific and Technical Information of China (English)
WU Qi-wu; ZHOU Xian-wei; WANG Jian-ping; YIN Zhi-hong; ZHANG Long
2009-01-01
The wavelength assignment with multiple multicast requests in fixed routing WDM network is studied. A new multicast dynamic wavelength assignment algorithm is presented based on matching degree. First, the wavelength matching degree between available wavelengths and multicast routing trees is introduced into the algorithm. Then, the wavelength assign-ment is translated into the maximum weight matching in bipartite graph, and this matching problem is solved by using an extended Kuhn-Munkres algorithm. The simulation results prove that the overall optimal wavelength assignment scheme is obtained in polynomial time. At the same time, the proposed algorithm can reduce the connecting blocking probability and improve the system resource utilization.
A new parallel algorithm for image matching based on entropy
Institute of Scientific and Technical Information of China (English)
董开坤; 胡铭曾
2001-01-01
Presents a new parallel image matching algorithm based on the concept of entropy feature vector and suitable to SIMD computer, which, in comparison with other algorithms, has the following advantages: ( 1 ) The spatial information of an image is appropriately introduced into the definition of image entropy. (2) A large number of multiplication operations are eliminated, thus the algorithm is sped up. (3) The shortcoming of having to do global calculation in the first instance is overcome, and concludes the algorithm has very good locality and is suitable for parallel processing.
The RSA Cryptoprocessor Hardware Implementation Based on Modified Montgomery Algorithm
Institute of Scientific and Technical Information of China (English)
CHEN Bo; WANG Xu; RONG Meng-tian
2005-01-01
RSA (Rivest-Shamir-Adleman)public-key cryptosystem is widely used in the information security area such as encryption and digital signature. Based on the modified Montgomery modular multiplication algorithm, a new architecture using CSA(carry save adder)was presented to implement modular multiplication. Compared with the popular modular multiplication algorithms using two CSA, the presented algorithm uses only one CSA, so it can improve the time efficiency of RSA cryptoprocessor and save about half of hardware resources for modular multiplication. With the increase of encryption data size n, the clock cycles for the encryption procedure reduce in T(n2) , compared with the modular multiplication algorithms using two CSA.
An Incremental Rule Acquisition Algorithm Based on Rough Set
Institute of Scientific and Technical Information of China (English)
YU Hong; YANG Da-chun
2005-01-01
Rough Set is a valid mathematical theory developed in recent years,which has the ability to deal with imprecise,uncertain,and vague information.This paper presents a new incremental rule acquisition algorithm based on rough set theory.First,the relation of the new instances with the original rule set is discussed.Then the change law of attribute reduction and value reduction are studied when a new instance is added.Follow,a new incremental learning algorithm for decision tables is presented within the framework of rough set.Finally,the new algorithm and the classical algorithm are analyzed and compared by theory and experiments.
Application of adaptive immune genetic algorithm in cognitive engine%自适应免疫遗传算法在认知决策引擎中的应用
Institute of Scientific and Technical Information of China (English)
夏龄
2013-01-01
An Adaptive Immune Genetic Algorithm(AIGA) is proposed, which introduces the adaptive immune genetic operators based on the immune genetic algorithm. Crossover probability and mutation probability are employed in the proposed algorithm to not only prevent the individual backwards during crossover and mutation processes, but also ensure the diversity of population, and this algorithm can rapidly converge to the global optimal solution. Simulation results show that AIGA exhibits a good performance in convergence speed, average fitness value and stability in comparison to Genetic Algorithm(GA) and other algorithms, which greatly satisfies the demand of parameters optimization of cognitive engine.% 提出一种自适应免疫遗传算法，设计自适应免疫遗传算子。该算法利用交叉率和变异率自适应调整策略，既防止交叉变异中的个体退化，又保证种群的多样性，并能快速收敛到全局最优解。仿真分析表明，与遗传算法等其他算法相比，该算法具有收敛速度快、平均适应度高、稳定性好等优点，能满足认知引擎参数优化的需要。
CUDT: a CUDA based decision tree algorithm.
Lo, Win-Tsung; Chang, Yue-Shan; Sheu, Ruey-Kai; Chiu, Chun-Chieh; Yuan, Shyan-Ming
2014-01-01
Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5 ∼ 55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.
Novel Adaptive Beamforming Algorithm Based on Wavelet Packet Transform
Institute of Scientific and Technical Information of China (English)
Zhang Xiaofei; Xu Dazhuan
2005-01-01
An analysis of the received signal of array antennas shows that the received signal has multi-resolution characteristics, and hence the wavelet packet theory can be used to detect the signal. By emplying wavelet packet theory to adaptive beamforming, a wavelet packet transform-based adaptive beamforming algorithm (WP-ABF) is proposed . This WP-ABF algorithm uses wavelet packet transform as the preprocessing, and the wavelet packet transformed signal uses least mean square algorithm to implement the adaptive beamforming. White noise can be wiped off under wavelet packet transform according to the different characteristics of signal and white under the wavelet packet transform. Theoretical analysis and simulations demonstrate that the proposed WP-ABF algorithm converges faster than the conventional adaptive beamforming algorithm and the wavelet transform-based beamforming algorithm. Simulation results also reveal that the convergence of the algorithm relates closely to the wavelet base and series; that is, the algorithm convergence gets better with the increasing of series, and for the same series of wavelet base the convergence gets better with the increasing of regularity.
New MPPT algorithm based on hybrid dynamical theory
Elmetennani, Shahrazed
2014-11-01
This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.
Analog Circuit Design Optimization Based on Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Mansour Barari
2014-01-01
Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
A Practical Localization Algorithm Based on Wireless Sensor Networks
Huang, Tao; Xia, Feng; Jin, Cheng; Li, Liang
2010-01-01
Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization algorithms, which increase the cost and greatly limit the range of location-based applications. In this paper we present a method which can effectively meet different localization accuracy requirements of most indoor and outdoor location services in realistic applications. Our algorithm is composed of two phases: partition phase, in which the target region is split into small grids and localization refinement phase in which a higher accuracy location can be generated by applying a trick algorithm. A realistic demo system using our algorithm has been developed to illustrate its feasibility and availability. The results show that our algorithm can improve the localization accuracy.
Teaching learning based optimization algorithm and its engineering applications
Rao, R Venkata
2016-01-01
Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.
Acceleration of Directional Medain Filter Based Deinterlacing Algorithm (DMFD
Directory of Open Access Journals (Sweden)
Addanki Purna Ramesh
2011-12-01
Full Text Available This paper presents a novel directional median filter based deinterlacing algorithm (DMFD. DMFD is a content adaptive spatial deinterlacing algorithm that finds the direction of the edge and applies the median filtering along the edge to interpolate the odd pixels from the 5 pixels from the upper and 5 pixels from the lower even lines of the field. The proposed algorithm gives a significance improvement of 3db for baboon standard test image that has high textured content compared to CADEM, DOI, and MELA and also gives improved average PSNR compared previous algorithms. The algorithm written and tested in C and ported onto Altera’s NIOS II embedded soft processor and configured in CYCLONE-II FPGA. The ISA of Nios-II processor has extended with two additional instructions for calculation of absolute difference and minimum of four numbers to accelerate the FPGA implementation of the algorithms by 3.2 times
Local Community Detection Algorithm Based on Minimal Cluster
Directory of Open Access Journals (Sweden)
Yong Zhou
2016-01-01
Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.
Compressive sensing based algorithms for electronic defence
Mishra, Amit Kumar
2017-01-01
This book details some of the major developments in the implementation of compressive sensing in radio applications for electronic defense and warfare communication use. It provides a comprehensive background to the subject and at the same time describes some novel algorithms. It also investigates application value and performance-related parameters of compressive sensing in scenarios such as direction finding, spectrum monitoring, detection, and classification.
Image completion algorithm based on texture synthesis
Institute of Scientific and Technical Information of China (English)
Zhang Hongying; Peng Qicong; Wu Yadong
2007-01-01
A new algorithm is proposed for completing the missing parts caused by the removal of foreground or background elements from an image of natural scenery in a visually plausible way.The major contributions of the proposed algorithm are: (1) for most natural images, there is a strong orientation of texture or color distribution.So a method is introduced to compute the main direction of the texture and complete the image by limiting the search to one direction to carry out image completion quite fast; (2) there exists a synthesis ordering for image completion.The searching order of the patches is denned to ensure the regions with more known information and the structures should be completed before filling in other regions; (3) to improve the visual effect of texture synthesis, an adaptive scheme is presented to determine the size of the template window for capturing the features of various scales.A number of examples are given to demonstrate the effectiveness of the proposed algorithm.
A Novel Heuristic Algorithm Based on Clark and Wright Algorithm for Green Vehicle Routing Problem
Mehdi Alinaghian; Zahra Kaviani; Siyavash Khaledan
2015-01-01
A significant portion of Gross Domestic Production (GDP) in any country belongs to the transportation system. Transportation equipment, in the other hand, is supposed to be great consumer of oil products. Many attempts have been assigned to the vehicles to cut down Greenhouse Gas (GHG). In this paper a novel heuristic algorithm based on Clark and Wright Algorithm called Green Clark and Wright (GCW) for Vehicle Routing Problem regarding to fuel consumption is presented. The objective function ...
旅行商问题的人工免疫算法%An Artificial Immune Algorithm for Travelling Salesman Problem
Institute of Scientific and Technical Information of China (English)
李茂军; 舒宜; 童调生
2003-01-01
This paper presents an Artificial Immune Algorithm (AIA)simulating the biological immune systems, andoffers its basic principle and approach. Comparing AIA with Genetic Algorithm (GAs)simulating the biological evolu-tion process, the paper points out that the method producing new antibodies in AIA is more versatile than the oneproducing new individuals in GAs. AIA reflects mechanism of natural selection better than GAs does, as AIA selectseffective antibodies from all antibodies by the appetency between an antibody and an antigen and by the repulsion be-tween an antibody and another, while GAs selects new individuals of next colony by the proportion of individual fit-ness. For Travel Salesman Problem (TSP), this paper brings forward how to describe antibodies artificially, how toproduce original antibodies, how to compute the appetency between an antibody and an antigen and the repulsion be-tween an antibody and another, and works out several artificial immune operators producing new antibod-ies. Simulating examples show that AIA is a very effective method for TSP.
Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems
Institute of Scientific and Technical Information of China (English)
YAO Yu; ZHU Shanfeng; CHEN Xinmeng
2006-01-01
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users' ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.
Video segmentation using multiple features based on EM algorithm
Institute of Scientific and Technical Information of China (English)
张风超; 杨杰; 刘尔琦
2004-01-01
Object-based video segmentation is an important issue for many multimedia applications. A video segmentation method based on EM algorithm is proposed. We consider video segmentation as an unsupervised classification problem and apply EM algorithm to obtain the maximum-likelihood estimation of the Gaussian model parameters for model-based segmentation. We simultaneously combine multiple features (motion, color) within a maximum likelihood framework to obtain accurate segment results. We also use the temporal consistency among video frames to improve the speed of EM algorithm. Experimental results on typical MPEG-4 sequences and real scene sequences show that our method has an attractive accuracy and robustness.
Fast image matching algorithm based on projection characteristics
Zhou, Lijuan; Yue, Xiaobo; Zhou, Lijun
2011-06-01
Based on analyzing the traditional template matching algorithm, this paper identified the key factors restricting the speed of matching and put forward a brand new fast matching algorithm based on projection. Projecting the grayscale image, this algorithm converts the two-dimensional information of the image into one-dimensional one, and then matches and identifies through one-dimensional correlation, meanwhile, because of normalization has been done, when the image brightness or signal amplitude increasing in proportion, it could also perform correct matching. Experimental results show that the projection characteristics based image registration method proposed in this article could greatly improve the matching speed, which ensuring the matching accuracy as well.
Research of Collaborative Filtering Recommendation Algorithm based on Network Structure
Directory of Open Access Journals (Sweden)
Hui PENG
2013-10-01
Full Text Available This paper combines the classic collaborative filtering algorithm with personalized recommendation algorithm based on network structure. For the data sparsity and malicious behavior problems of traditional collaborative filtering algorithm, the paper introduces a new kind of social network-based collaborative filtering algorithm. In order to improve the accuracy of the personalized recommendation technology, we first define empty state in the state space of multi-dimensional semi-Markov processes and obtain extended multi-dimensional semi-Markov processes which are combined with social network analysis theory, and then we get social network information flow model. The model describes the flow of information between the members of the social network. So, we propose collaborative filtering algorithm based on social network information flow model. The algorithm uses social network information and combines user trust with user interest and find nearest neighbors of the target user and then forms a project recommended to improve the accuracy of recommended. Compared with the traditional collaborative filtering algorithm, the algorithm can effectively alleviate the sparsity and malicious behavior problem, and significantly improve the quality of the recommendation system recommended.
Institute of Scientific and Technical Information of China (English)
1992-01-01
920630 Effects of the spleen on immunestate of patients with gastric cancer.QIUDengbo (仇登波), et al. Dept General Surg,Union Hosp, Tongji Med Univ, Wuhan, 430022.Natl Med J China 1992; 72(6): 334-337. For analysing the effects of the spleen on im-mune state of gastric cancer patients.T-lym-
Area Variation Based Color Snake Algorithm for Moving Object Tracking
Institute of Scientific and Technical Information of China (English)
Shoum-ik ROYCHOUDHURY; Young-joon HAN
2010-01-01
A snake algorithm has been known that it has a strong point in extracting the exact contour of an object.But it is apt to be influenced by scattered edges around the control points.Since the shape of a moving object in 2D image changes a lot due ta its rotation and translation in the 3D space,the conventional algorithm that takes into account slowly moving objects cannot provide an appropriate solution.To utilize the advantages of the snake algrithm while minimizing the drawbacks,this paper proposes the area variation based color snake algorithm for moving object tracking.The proposed algorithm includes a new energy term which is used for preserving the shape of an object between two consecutive inages.The proposed one can also segment precisely interesting objects on complex image since it is based on color information.Experiment results show that the proposed algorithm is very effective in various environments.
CUDA Based Speed Optimization of the PCA Algorithm
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Salih Görgünoğlu
2016-05-01
Full Text Available Principal Component Analysis (PCA is an algorithm involving heavy mathematical operations with matrices. The data extracted from the face images are usually very large and to process this data is time consuming. To reduce the execution time of these operations, parallel programming techniques are used. CUDA is a multipurpose parallel programming architecture supported by graphics cards. In this study we have implemented the PCA algorithm using both the classical programming approach and CUDA based implementation using different configurations. The algorithm is subdivided into its constituent calculation steps and evaluated for the positive effects of parallelization on each step. Therefore, the parts of the algorithm that cannot be improved by parallelization are identified. On the other hand, it is also shown that, with CUDA based approach dramatic improvements in the overall performance of the algorithm arepossible.
Novel algorithm for distributed replicas management based on dynamic programming
Institute of Scientific and Technical Information of China (English)
Wang Tao; Lu Xianliang; Hou Mengshu
2006-01-01
Replicas can improve the data reliability in distributed system. However, the traditional algorithms for replica management are based on the assumption that all replicas have the uniform reliability, which is inaccurate in some actual systems. To address such problem, a novel algorithm is proposed based on dynamic programming to manage the number and distribution of replicas in different nodes. By using Markov model, replicas management is organized as a multi-phase process, and the recursion equations are provided. In this algorithm, the heterogeneity of nodes, the expense for maintaining replicas and the engaged space have been considered. Under these restricted conditions, this algorithm realizes high data reliability in a distributed system. The results of case analysis prove the feasibility of the algorithm.
Heuristic based data scheduling algorithm for OFDMA wireless network
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks.A distributed scheduling algorithm (DSA) for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links.For the global fairness improvement of algorithms,a global power control scheduling algorithm (GPCSA) based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio.Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.
Drilling Path Optimization Based on Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Guangyu; ZHANG Weibo; DU Yuexiang
2006-01-01
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
Image Recovery Algorithm Based on Learned Dictionary
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Xinghui Zhu
2014-01-01
Full Text Available We proposed a recovery scheme for image deblurring. The scheme is under the framework of sparse representation and it has three main contributions. Firstly, considering the sparse property of natural image, the nonlocal overcompleted dictionaries are learned for image patches in our scheme. And, then, we coded the patches in each nonlocal clustering with the corresponding learned dictionary to recover the whole latent image. In addition, for some practical applications, we also proposed a method to evaluate the blur kernel to make the algorithm usable in blind image recovery. The experimental results demonstrated that the proposed scheme is competitive with some current state-of-the-art methods.
A Novel Heuristic Algorithm Based on Clark and Wright Algorithm for Green Vehicle Routing Problem
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Mehdi Alinaghian
2015-08-01
Full Text Available A significant portion of Gross Domestic Production (GDP in any country belongs to the transportation system. Transportation equipment, in the other hand, is supposed to be great consumer of oil products. Many attempts have been assigned to the vehicles to cut down Greenhouse Gas (GHG. In this paper a novel heuristic algorithm based on Clark and Wright Algorithm called Green Clark and Wright (GCW for Vehicle Routing Problem regarding to fuel consumption is presented. The objective function is fuel consumption, drivers, and the usage of vehicles. Being compared to exact methods solutions for small-sized problems and to Differential Evolution (DE algorithm solutions for large-scaled problems, the results show efficient performance of the proposed GCW algorithm.
Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm
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Jianyong Liu
2015-01-01
Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.
CUDT: A CUDA Based Decision Tree Algorithm
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Win-Tsung Lo
2014-01-01
Full Text Available Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture, which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.
A NOVEL THRESHOLD BASED EDGE DETECTION ALGORITHM
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Y. RAMADEVI,
2011-06-01
Full Text Available Image segmentation is the process of partitioning/subdividing a digital image into multiple meaningful regions or sets of pixels regions with respect to a particular application. Edge detection is one of the frequently used techniques in digital image processing. The level to which the subdivision is carried depends on theproblem being viewed. Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. There are many ways to perform edge detection. In this paper different Edge detection methods such as Sobel, Prewitt, Robert, Canny, Laplacian of Gaussian (LOG are used for segmenting the image. Expectation-Maximization (EM algorithm, OSTU and Genetic algorithms are also used. A new edge detection technique is proposed which detects the sharp and accurate edges that are not possible with the existing techniques. The proposed method with different threshold values for given input image is shown that ranges between 0 and 1 and it are observed that when the threshold value is 0.68 the sharp edges are recognised properly.
Efficient Satellite Scheduling Based on Improved Vector Evaluated Genetic Algorithm
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Tengyue Mao
2012-03-01
Full Text Available Satellite scheduling is a typical multi-peak, many-valley, nonlinear multi-objective optimization problem. How to effectively implement the satellite scheduling is a crucial research in space areas.This paper mainly discusses the performance of VEGA (Vector Evaluated Genetic Algorithm based on the study of basic principles of VEGA algorithm, algorithm realization and test function, and then improves VEGA algorithm through introducing vector coding, new crossover and mutation operators, new methods to assign fitness and hold good individuals. As a result, the diversity and convergence of improved VEGA algorithm of improved VEGA algorithm have been significantly enhanced and will be applied to Earth-Mars orbit optimization. At the same time, this paper analyzes the results of the improved VEGA, whose results of performance analysis and evaluation show that although VEGA has a profound impact upon multi-objective evolutionary research, multi-objective evolutionary algorithm on the basis of Pareto seems to be a more effective method to get the non-dominated solutions from the perspective of diversity and convergence of experimental result. Finally, based on Visual C + + integrated development environment, we have implemented improved vector evaluation algorithm in the satellite scheduling.
ROUTING AND WAVELENGTH ASSIGNMENT ALGORITHMS BASED ON EQUIVALENT NETWORKS
Institute of Scientific and Technical Information of China (English)
Qi Xiaogang; Liu Lifang; Lin Sanyang
2006-01-01
In this paper, a Wavelength Division Multiplexing (WDM) network model based on the equivalent networks is described, and wavelength-dependent equivalent arc, equivalent networks, equivalent multicast tree and some other terms are presented. Based on this model and relevant Routing and Wavelength Assignment (RWA) strategy, a unicast RWA algorithm and a multicast RWA algorithm are presented. The wavelength-dependent equivalent arc expresses the schedule of local RWA and the equivalent network expresses the whole topology of WDM optical networks, so the two algorithms are of the flexibility in RWA and the optimization of the whole problem. The theoretic analysis and simulation results show the two algorithms are of the stronger capability and the lower complexity than the other existing algorithms for RWA problem, and the complexity of the two algorithms are only related to the scale of the equivalent networks. Finally, we prove the two algorithms' feasibility and the one-by-one corresponding relation between the equivalent multicast tree and original multicast tree, and point out the superiorities and drawbacks of the two algorithms respectively.
A novel bit-quad-based Euler number computing algorithm.
Yao, Bin; He, Lifeng; Kang, Shiying; Chao, Yuyan; Zhao, Xiao
2015-01-01
The Euler number of a binary image is an important topological property in computer vision and pattern recognition. This paper proposes a novel bit-quad-based Euler number computing algorithm. Based on graph theory and analysis on bit-quad patterns, our algorithm only needs to count two bit-quad patterns. Moreover, by use of the information obtained during processing the previous bit-quad, the average number of pixels to be checked for processing a bit-quad is only 1.75. Experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms.
[Heart rate measurement algorithm based on artificial intelligence].
Chengxian, Cai; Wei, Wang
2010-01-01
Based on the heart rate measurement method using time-lapse image of human cheek, this paper proposes a novel measurement algorithm based on Artificial Intelligence. The algorithm combining with fuzzy logic theory acquires the heart beat point by using the defined fuzzy membership function of each sampled point. As a result, it calculates the heart rate by counting the heart beat points in a certain time period. Experiment shows said algorithm satisfies in operability, accuracy and robustness, which leads to constant practical value.
THE PARALLEL RECURSIVE AP ADAPTIVE ALGORITHM BASED ON VOLTERRA SERIES
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Aiming at the nonlinear system identification problem, a parallel recursive affine projection (AP) adaptive algorithm for the nonlinear system based on Volterra series is presented in this paper. The algorithm identifies in parallel the Volterra kernel of each order, recursively estimate the inverse of the autocorrelation matrix for the Volterra input of each order, and remarkably improve the convergence speed of the identification process compared with the NLMS and conventional AP adaptive algorithm based on Volterra series. Simulation results indicate that the proposed method in this paper is efficient.
Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm
Institute of Scientific and Technical Information of China (English)
李侃; 刘玉树
2004-01-01
A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.
A Novel Approach to Fast Image Filtering Algorithm of Infrared Images based on Intro Sort Algorithm
Gupta, Kapil Kumar; Niranjan, Jitendra Kumar
2012-01-01
In this study we investigate the fast image filtering algorithm based on Intro sort algorithm and fast noise reduction of infrared images. Main feature of the proposed approach is that no prior knowledge of noise required. It is developed based on Stefan- Boltzmann law and the Fourier law. We also investigate the fast noise reduction approach that has advantage of less computation load. In addition, it can retain edges, details, text information even if the size of the window increases. Intro sort algorithm begins with Quick sort and switches to heap sort when the recursion depth exceeds a level based on the number of elements being sorted. This approach has the advantage of fast noise reduction by reducing the comparison time. It also significantly speed up the noise reduction process and can apply to real-time image processing. This approach will extend the Infrared images applications for medicine and video conferencing.
Analysis of a wavelet-based robust hash algorithm
Meixner, Albert; Uhl, Andreas
2004-06-01
This paper paper is a quantitative evaluation of a wavelet-based, robust authentication hashing algorithm. Based on the results of a series of robustness and tampering sensitivity tests, we describepossible shortcomings and propose variousmodifications to the algorithm to improve its performance. The second part of the paper describes and attack against the scheme. It allows an attacker to modify a tampered image, such that it's hash value closely matches the hash value of the original.
An Event Grouping Based Algorithm for University Course Timetabling Problem
Kralev, Velin; Kraleva, Radoslava; Yurukov, Borislav
2016-01-01
This paper presents the study of an event grouping based algorithm for a university course timetabling problem. Several publications which discuss the problem and some approaches for its solution are analyzed. The grouping of events in groups with an equal number of events in each group is not applicable to all input data sets. For this reason, a universal approach to all possible groupings of events in commensurate in size groups is proposed here. Also, an implementation of an algorithm base...
An Event Grouping Based Algorithm for University Course Timetabling Problem
Kralev, Velin; Kraleva, Radoslava; Yurukov, Borislav
2016-01-01
This paper presents the study of an event grouping based algorithm for a university course timetabling problem. Several publications which discuss the problem and some approaches for its solution are analyzed. The grouping of events in groups with an equal number of events in each group is not applicable to all input data sets. For this reason, a universal approach to all possible groupings of events in commensurate in size groups is proposed here. Also, an implementation of an algorithm base...
Institute of Scientific and Technical Information of China (English)
张利; 孟祥凯; 刘文生
2015-01-01
将免疫遗传算法运用到柔性生产中能够极大地改进遗传算法的缺点，提高收敛效果。在此基础上提出了粒子群算法，并与免疫遗传算法进行对比，分析最优结果；将其应用到车间调度中能极大地提高调度路径的优化。运用仿真将克隆免疫算法的调度结果与粒子群算法的调度结果对比，以获得较优的结果。%The immune genetic algorithm applied to flexible manufacruring system can greatly overcome the genetic algorithm’s shortcomings and improve the convergence effect .In this paper ,the PSO was applied to the job shop scheduling ,which greatly improved the scheduling path optimization effect .The simulation study on job shop scheduling which was based on immune algorithm and particle swarm optimization was carried out ,the simualted results were compared ,to achieve the best result .
无线传感器网络免疫代理数据融合算法%Immune agent data fusion algorithm in Wireless Sensor Network
Institute of Scientific and Technical Information of China (English)
孙子文; 刘加杰; 梁广玮
2013-01-01
In view of the time delay and energy consumption, an immune agent-based data fusion algorithm is proposed. The energy consumption of network is reduced by the free migration of agent. In order to further reduce the energy consumption of network, the number of node participated in data fusion is reduced by the immune of sensor data. The time delay of network in emergency situation is reduced by the establishment of emergency access. And the sensor data of nodes are compressed by hexadecimal encoding. The simulation results show that the proposed algorithm is effective in reducing the energy consumption and the time delay of network.%针对网络能耗和延迟问题,提出了一种基于免疫代理的数据融合算法.通过代理的自由迁移降低节点传输能耗；通过免疫降低参与融合的节点数以降低网络能耗；设立应急通道以降低紧急情况下的网络延迟；采用十六进制编码方法对融合数据进行压缩处理.试验结果表明,该算法能有效降低网络能耗和延迟.
Genetic Algorithm Based Microscale Vehicle Emissions Modelling
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Sicong Zhu
2015-01-01
Full Text Available There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.
Warehouse Optimization Model Based on Genetic Algorithm
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Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
RELAY ALGORITHM BASED ON NETWORK CODING IN WIRELESS LOCAL NETWORK
Institute of Scientific and Technical Information of China (English)
Wang Qi; Wang Qingshan; Wang Dongxue
2013-01-01
The network coding is a new technology in the field of information in 21st century.It could enhance the network throughput and save the energy consumption,and is mainly based on the single transmission rate.However,with the development of wireless network and equipment,wireless local network MAC protocols have already supported the multi-rate transmission.This paper investigates the optimal relay selection problem based on network coding.Firstly,the problem is formulated as an optimization problem.Moreover,a relay algorithm based on network coding is proposed and the transmission time gain of our algorithm over the traditional relay algorithm is analyzed.Lastly,we compare total transmission time and the energy consumption of our proposed algorithm,Network Coding with Relay Assistance (NCRA),Transmission Request (TR),and the Direct Transmission (DT) without relay algorithm by adopting IEEE 802.11b.The simulation results demonstrate that our algorithm that improves the coding opportunity by the cooperation of the relay nodes leads to the transmission time decrease of up to 17％ over the traditional relay algorithms.
Haplotyping a single triploid individual based on genetic algorithm.
Wu, Jingli; Chen, Xixi; Li, Xianchen
2014-01-01
The minimum error correction model is an important combinatorial model for haplotyping a single individual. In this article, triploid individual haplotype reconstruction problem is studied by using the model. A genetic algorithm based method GTIHR is presented for reconstructing the triploid individual haplotype. A novel coding method and an effectual hill-climbing operator are introduced for the GTIHR algorithm. This relatively short chromosome code can lead to a smaller solution space, which plays a positive role in speeding up the convergence process. The hill-climbing operator ensures algorithm GTIHR converge at a good solution quickly, and prevents premature convergence simultaneously. The experimental results prove that algorithm GTIHR can be implemented efficiently, and can get higher reconstruction rate than previous algorithms.
Distribution network planning algorithm based on Hopfield neural network
Institute of Scientific and Technical Information of China (English)
GAO Wei-xin; LUO Xian-jue
2005-01-01
This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn't need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.
Switching Equalization Algorithm Based on a New SNR Estimation Method
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
It is well-known that turbo equalization with the max-log-map (MLM) rather than the log-map (LM) algorithm is insensitive to signal to noise ratio (SNR) mismatch. As our first contribution, an improved MLM algorithm called scaled max-log-map (SMLM) algorithm is presented. Simulation results show that the SMLM scheme can dramatically outperform the MLM without sacrificing the robustness against SNR mismatch. Unfortunately, its performance is still inferior to that of the LM algorithm with exact SNR knowledge over the class of high-loss channels. As our second contribution, a switching turbo equalization scheme, which switches between the SMLM and LM schemes, is proposed to practically close the performance gap. It is based on a novel way to estimate the SNR from the reliability values of the extrinsic information of the SMLM algorithm.
A SAR Back Projection Autofocusing Algorithm Based on Legendre Approximation
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Gao Yang
2014-06-01
Full Text Available The Back Projection (BP algorithm is a very important time-domain methodology for Synthetic Aperture Radar (SAR imaging. However, conventional autofocus techniques are based on frequency-domain imaging algorithms, and can not be directly applied to BP imagery for error phase estimation. In this paper, an autofocus algorithm for BP imagery is proposed. The algorithm takes image sharpness as an objective function, and employs the coordinate descent optimization scheme to obtain the optimum phase-corrected variables by iterations. In the implementation, with a Legendre approximation of the objective function, the optimal phase estimation can be found analytically for each parameter within an iteration, avoiding computationally expensive line-search procedures. The experimental results with both simulated and measured data confirm the accuracy and effectiveness of the proposed algorithm.
SIMULATED ANNEALING BASED POLYNOMIAL TIME QOS ROUTING ALGORITHM FOR MANETS
Institute of Scientific and Technical Information of China (English)
Liu Lianggui; Feng Guangzeng
2006-01-01
Multi-constrained Quality-of-Service (QoS) routing is a big challenge for Mobile Ad hoc Networks (MANETs) where the topology may change constantly. In this paper a novel QoS Routing Algorithm based on Simulated Annealing (SA_RA) is proposed. This algorithm first uses an energy function to translate multiple QoS weights into a single mixed metric and then seeks to find a feasible path by simulated annealing. The paper outlines simulated annealing algorithm and analyzes the problems met when we apply it to Qos Routing (QoSR) in MANETs. Theoretical analysis and experiment results demonstrate that the proposed method is an effective approximation algorithms showing better performance than the other pertinent algorithm in seeking the (approximate) optimal configuration within a period of polynomial time.
Distortion Parameters Analysis Method Based on Improved Filtering Algorithm
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ZHANG Shutuan
2013-10-01
Full Text Available In order to realize the accurate distortion parameters test of aircraft power supply system, and satisfy the requirement of corresponding equipment in the aircraft, the novel power parameters test system based on improved filtering algorithm is introduced in this paper. The hardware of the test system has the characters of s portable and high-speed data acquisition and processing, and the software parts utilize the software Labwindows/CVI as exploitation software, and adopt the pre-processing technique and adding filtering algorithm. Compare with the traditional filtering algorithm, the test system adopted improved filtering algorithm can help to increase the test accuracy. The application shows that the test system with improved filtering algorithm can realize the accurate test results, and reach to the design requirements.
Earth Observation Satellites Scheduling Based on Decomposition Optimization Algorithm
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Feng Yao
2010-11-01
Full Text Available A decomposition-based optimization algorithm was proposed for solving Earth Observation Satellites scheduling problem. The problem was decomposed into task assignment main problem and single satellite scheduling sub-problem. In task assignment phase, the tasks were allocated to the satellites, and each satellite would schedule the task respectively in single satellite scheduling phase. We adopted an adaptive ant colony optimization algorithm to search the optimal task assignment scheme. Adaptive parameter adjusting strategy and pheromone trail smoothing strategy were introduced to balance the exploration and the exploitation of search process. A heuristic algorithm and a very fast simulated annealing algorithm were proposed to solve the single satellite scheduling problem. The task assignment scheme was valued by integrating the observation scheduling result of multiple satellites. The result was responded to the ant colony optimization algorithm, which can guide the search process of ant colony optimization. Computation results showed that the approach was effective to the satellites observation scheduling problem.
Mobile robot dynamic path planning based on improved genetic algorithm
Wang, Yong; Zhou, Heng; Wang, Ying
2017-08-01
In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.
Multiparty Quantum Key Agreement Based on Quantum Search Algorithm.
Cao, Hao; Ma, Wenping
2017-03-23
Quantum key agreement is an important topic that the shared key must be negotiated equally by all participants, and any nontrivial subset of participants cannot fully determine the shared key. To date, the embed modes of subkey in all the previously proposed quantum key agreement protocols are based on either BB84 or entangled states. The research of the quantum key agreement protocol based on quantum search algorithms is still blank. In this paper, on the basis of investigating the properties of quantum search algorithms, we propose the first quantum key agreement protocol whose embed mode of subkey is based on a quantum search algorithm known as Grover's algorithm. A novel example of protocols with 5 - party is presented. The efficiency analysis shows that our protocol is prior to existing MQKA protocols. Furthermore it is secure against both external attack and internal attacks.
A New Generalized Similarity-Based Topic Distillation Algorithm
Institute of Scientific and Technical Information of China (English)
ZHOU Hongfang; DANG Xiaohui
2007-01-01
The procedure of hypertext induced topic search based on a semantic relation model is analyzed, and the reason for the topic drift of HITS algorithm was found to prove that Web pages are projected to a wrong latent semantic basis. A new concept-generalized similarity is introduced and, based on this, a new topic distillation algorithm GSTDA(generalized similarity based topic distillation algorithm) was presented to improve the quality of topic distillation. GSTDA was applied not only to avoid the topic drift, but also to explore relative topics to user query. The experimental results on 10 queries show that GSTDA reduces topic drift rate by 10% to 58% compared to that of HITS(hypertext induced topic search) algorithm, and discovers several relative topics to queries that have multiple meanings.
Fingerprint Image Segmentation Algorithm Based on Contourlet Transform Technology
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Guanghua Zhang
2016-09-01
Full Text Available This paper briefly introduces two classic algorithms for fingerprint image processing, which include the soft threshold denoise algorithm of wavelet domain based on wavelet domain and the fingerprint image enhancement algorithm based on Gabor function. Contourlet transform has good texture sensitivity and can be used for the segmentation enforcement of the fingerprint image. The method proposed in this paper has attained the final fingerprint segmentation image through utilizing a modified denoising for a high-frequency coefficient after Contourlet decomposition, highlighting the fingerprint ridge line through modulus maxima detection and finally connecting the broken fingerprint line using a value filter in direction. It can attain richer direction information than the method based on wavelet transform and Gabor function and can make the positioning of detailed features more accurate. However, its ridge should be more coherent. Experiments have shown that this algorithm is obviously superior in fingerprint features detection.
A New Augmentation Based Algorithm for Extracting Maximal Chordal Subgraphs.
Bhowmick, Sanjukta; Chen, Tzu-Yi; Halappanavar, Mahantesh
2015-02-01
A graph is chordal if every cycle of length greater than three contains an edge between non-adjacent vertices. Chordal graphs are of interest both theoretically, since they admit polynomial time solutions to a range of NP-hard graph problems, and practically, since they arise in many applications including sparse linear algebra, computer vision, and computational biology. A maximal chordal subgraph is a chordal subgraph that is not a proper subgraph of any other chordal subgraph. Existing algorithms for computing maximal chordal subgraphs depend on dynamically ordering the vertices, which is an inherently sequential process and therefore limits the algorithms' parallelizability. In this paper we explore techniques to develop a scalable parallel algorithm for extracting a maximal chordal subgraph. We demonstrate that an earlier attempt at developing a parallel algorithm may induce a non-optimal vertex ordering and is therefore not guaranteed to terminate with a maximal chordal subgraph. We then give a new algorithm that first computes and then repeatedly augments a spanning chordal subgraph. After proving that the algorithm terminates with a maximal chordal subgraph, we then demonstrate that this algorithm is more amenable to parallelization and that the parallel version also terminates with a maximal chordal subgraph. That said, the complexity of the new algorithm is higher than that of the previous parallel algorithm, although the earlier algorithm computes a chordal subgraph which is not guaranteed to be maximal. We experimented with our augmentation-based algorithm on both synthetic and real-world graphs. We provide scalability results and also explore the effect of different choices for the initial spanning chordal subgraph on both the running time and on the number of edges in the maximal chordal subgraph.
The Algorithm for Rule-base Refinement on Fuzzy Set
Institute of Scientific and Technical Information of China (English)
LI Feng; WU Cui-hong; DING Xiang-wu
2006-01-01
In the course of running an artificial intelligent system many redundant rules are often produced. To refine the knowledge base, viz. to remove the redundant rules, can accelerate the reasoning and shrink the rule base. The purpose of the paper is to present the thinking on the topic and design the algorithm to remove the redundant rules from the rule base.The "abstraction" of "state variable", redundant rules and the least rule base are discussed in the paper. The algorithm on refining knowledge base is also presented.
Main features of DNA-based immunization vectors
Directory of Open Access Journals (Sweden)
V. Azevedo
1999-02-01
Full Text Available DNA-based immunization has initiated a new era of vaccine research. One of the main goals of gene vaccine development is the control of the levels of expression in vivo for efficient immunization. Modifying the vector to modulate expression or immunogenicity is of critical importance for the improvement of DNA vaccines. The most frequently used vectors for genetic immunization are plasmids. In this article, we review some of the main elements relevant to their design such as strong promoter/enhancer region, introns, genes encoding antigens of interest from the pathogen (how to choose and modify them, polyadenylation termination sequence, origin of replication for plasmid production in Escherichia coli, antibiotic resistance gene as selectable marker, convenient cloning sites, and the presence of immunostimulatory sequences (ISS that can be added to the plasmid to enhance adjuvanticity and to activate the immune system. In this review, the specific modifications that can increase overall expression as well as the potential of DNA-based vaccination are also discussed.
A New Method Based on Multi Agent System and Artificial Immune System for Systematic Maintenance
Directory of Open Access Journals (Sweden)
Adel Abdelhadi
2014-05-01
Full Text Available This study propose a novel method for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling. The proposed approach is inspired by the behavior of the human body. We have implemented a problem-solving approach for optimizing the processing time, methods based on Métaheuristiques. This hybridization is between a Multi agent system and inspirations of the human body, especially artificial immune system. The effectiveness of our approach has been demonstrated repeatedly in this study. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.
Adaptive bad pixel correction algorithm for IRFPA based on PCNN
Leng, Hanbing; Zhou, Zuofeng; Cao, Jianzhong; Yi, Bo; Yan, Aqi; Zhang, Jian
2013-10-01
Bad pixels and response non-uniformity are the primary obstacles when IRFPA is used in different thermal imaging systems. The bad pixels of IRFPA include fixed bad pixels and random bad pixels. The former is caused by material or manufacture defect and their positions are always fixed, the latter is caused by temperature drift and their positions are always changing. Traditional radiometric calibration-based bad pixel detection and compensation algorithm is only valid to the fixed bad pixels. Scene-based bad pixel correction algorithm is the effective way to eliminate these two kinds of bad pixels. Currently, the most used scene-based bad pixel correction algorithm is based on adaptive median filter (AMF). In this algorithm, bad pixels are regarded as image noise and then be replaced by filtered value. However, missed correction and false correction often happens when AMF is used to handle complex infrared scenes. To solve this problem, a new adaptive bad pixel correction algorithm based on pulse coupled neural networks (PCNN) is proposed. Potential bad pixels are detected by PCNN in the first step, then image sequences are used periodically to confirm the real bad pixels and exclude the false one, finally bad pixels are replaced by the filtered result. With the real infrared images obtained from a camera, the experiment results show the effectiveness of the proposed algorithm.
A novel algorithm for computer based assessment
2012-01-01
Student learning outcomes have been evaluated through graded assignments and tests by most paper-based assessment systems. But computer based assessments has the opportunity to improve the efficiency of assessments process. The use of internet is also made possible
Cooperation-based Ant Colony Algorithm in WSN
Directory of Open Access Journals (Sweden)
Jianbin Xue
2013-04-01
Full Text Available This paper proposed a routing algorithm based on ant colony algorithm. The traditional ant colony algorithm updates pheromone according to the path length, to get the shortest path from the initial node to destination node. But MIMO system is different from the SISO system. The distance is farther but the energy is not bigger. Similarly, the closer the distance, the smaller the energy is not necessarily. So need to select the path according to the energy consumption of the path. This paper is based on the energy consumption to update the pheromone which from the cluster head node to the next hop node. Then, can find a path which the communication energy consumption is least. This algorithm can save more energy consumption of the network. The simulation results of MATLAB show that the path chosen by the algorithm is better than the simple ant colony algorithm, and the algorithm can save the network energy consumption better and can prolong the life cycle of the network.
A Flocking Based algorithm for Document Clustering Analysis
Energy Technology Data Exchange (ETDEWEB)
Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL
2006-01-01
Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.
Phase shift extraction algorithm based on Euclidean matrix norm.
Deng, Jian; Wang, Hankun; Zhang, Desi; Zhong, Liyun; Fan, Jinping; Lu, Xiaoxu
2013-05-01
In this Letter, the character of Euclidean matrix norm (EMN) of the intensity difference between phase-shifting interferograms, which changes in sinusoidal form with the phase shifts, is presented. Based on this character, an EMN phase shift extraction algorithm is proposed. Both the simulation calculation and experimental research show that the phase shifts with high precision can be determined with the proposed EMN algorithm easily. Importantly, the proposed EMN algorithm will supply a powerful tool for the rapid calibration of the phase shifts.
Restart-Based Genetic Algorithm for the Quadratic Assignment Problem
Misevicius, Alfonsas
The power of genetic algorithms (GAs) has been demonstrated for various domains of the computer science, including combinatorial optimization. In this paper, we propose a new conceptual modification of the genetic algorithm entitled a "restart-based genetic algorithm" (RGA). An effective implementation of RGA for a well-known combinatorial optimization problem, the quadratic assignment problem (QAP), is discussed. The results obtained from the computational experiments on the QAP instances from the publicly available library QAPLIB show excellent performance of RGA. This is especially true for the real-life like QAPs.
Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The three-layer forward neural networks are used to establish the inverse kinem a tics models of robot manipulators. The fuzzy genetic algorithm based on the line ar scaling of the fitness value is presented to update the weights of neural net works. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the propo sed method improves considerably the precision of the inverse kinematics solutio ns for robot manipulators and guarantees a rapid global convergence and overcome s the drawbacks of SGA and the BP algorithm.
A novel image encryption algorithm based on DNA subsequence operation.
Zhang, Qiang; Xue, Xianglian; Wei, Xiaopeng
2012-01-01
We present a novel image encryption algorithm based on DNA subsequence operation. Different from the traditional DNA encryption methods, our algorithm does not use complex biological operation but just uses the idea of DNA subsequence operations (such as elongation operation, truncation operation, deletion operation, etc.) combining with the logistic chaotic map to scramble the location and the value of pixel points from the image. The experimental results and security analysis show that the proposed algorithm is easy to be implemented, can get good encryption effect, has a wide secret key's space, strong sensitivity to secret key, and has the abilities of resisting exhaustive attack and statistic attack.
A motion retargeting algorithm based on model simplification
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A new motion retargeting algorithm is presented, which adapts the motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimization process is difficult to converge to the optimal value because of high complexity of the physical human model. In order to address this problem, an appropriate simplified model automatically determined by a motion analysis technique is utilized, and then motion retargeting with this simplified model as an intermediate agent is implemented. The entire motion retargeting algorithm involves three steps of nonlinearly constrained optimization: forward retargeting, motion scaling and inverse retargeting. Experimental results show the validity of this algorithm.
Quantum Image Encryption Algorithm Based on Quantum Image XOR Operations
Gong, Li-Hua; He, Xiang-Tao; Cheng, Shan; Hua, Tian-Xiang; Zhou, Nan-Run
2016-07-01
A novel encryption algorithm for quantum images based on quantum image XOR operations is designed. The quantum image XOR operations are designed by using the hyper-chaotic sequences generated with the Chen's hyper-chaotic system to control the control-NOT operation, which is used to encode gray-level information. The initial conditions of the Chen's hyper-chaotic system are the keys, which guarantee the security of the proposed quantum image encryption algorithm. Numerical simulations and theoretical analyses demonstrate that the proposed quantum image encryption algorithm has larger key space, higher key sensitivity, stronger resistance of statistical analysis and lower computational complexity than its classical counterparts.
A Secure Watermarking Algorithm Based on Coupled Map Lattice
Institute of Scientific and Technical Information of China (English)
YI Xiang; WANG Wei-ran
2005-01-01
Based on the nonlinear theory, a secure watermarking algorithm using wavelet transform and coupled map lattice is presented. The chaos is sensitive to initial conditions and has a good non-relevant correlation property, but the finite precision effect limits its application in practical digital watermarking system. To overcome the undesirable short period of chaos mapping and improve the security level of watermarking, the hyper-chaotic sequence is adopted in this algorithm. The watermark is mixed with the hyper-chaotic sequence and embedded in the wavelet domain of the host image. Experimental results and analysis are given to demonstrate that the proposed watermarking algorithm is transparent, robust and secure.
A Novel Image Encryption Algorithm Based on DNA Subsequence Operation
Zhang, Qiang; Xue, Xianglian; Wei, Xiaopeng
2012-01-01
We present a novel image encryption algorithm based on DNA subsequence operation. Different from the traditional DNA encryption methods, our algorithm does not use complex biological operation but just uses the idea of DNA subsequence operations (such as elongation operation, truncation operation, deletion operation, etc.) combining with the logistic chaotic map to scramble the location and the value of pixel points from the image. The experimental results and security analysis show that the proposed algorithm is easy to be implemented, can get good encryption effect, has a wide secret key's space, strong sensitivity to secret key, and has the abilities of resisting exhaustive attack and statistic attack. PMID:23093912
A Novel Image Encryption Algorithm Based on DNA Subsequence Operation
Directory of Open Access Journals (Sweden)
Qiang Zhang
2012-01-01
Full Text Available We present a novel image encryption algorithm based on DNA subsequence operation. Different from the traditional DNA encryption methods, our algorithm does not use complex biological operation but just uses the idea of DNA subsequence operations (such as elongation operation, truncation operation, deletion operation, etc. combining with the logistic chaotic map to scramble the location and the value of pixel points from the image. The experimental results and security analysis show that the proposed algorithm is easy to be implemented, can get good encryption effect, has a wide secret key's space, strong sensitivity to secret key, and has the abilities of resisting exhaustive attack and statistic attack.
Proposal of Tabu Search Algorithm Based on Cuckoo Search
Directory of Open Access Journals (Sweden)
Ahmed T. Sadiq Al-Obaidi
2014-03-01
Full Text Available This paper presents a new version of Tabu Search (TS based on Cuckoo Search (CS called (Tabu-Cuckoo Search TCS to reduce the effect of the TS problems. The proposed algorithm provides a more diversity to candidate solutions of TS. Two case studies have been solved using the proposed algorithm, 4-Color Map and Traveling Salesman Problem. The proposed algorithm gives a good result compare with the original, the iteration numbers are less and the local minimum or non-optimal solutions are less.
Heuristic-based scheduling algorithm for high level synthesis
Mohamed, Gulam; Tan, Han-Ngee; Chng, Chew-Lye
1992-01-01
A new scheduling algorithm is proposed which uses a combination of a resource utilization chart, a heuristic algorithm to estimate the minimum number of hardware units based on operator mobilities, and a list-scheduling technique to achieve fast and near optimal schedules. The schedule time of this algorithm is almost independent of the length of mobilities of operators as can be seen from the benchmark example (fifth order digital elliptical wave filter) presented when the cycle time was increased from 17 to 18 and then to 21 cycles. It is implemented in C on a SUN3/60 workstation.
A Learning Algorithm based on High School Teaching Wisdom
Philip, Ninan Sajeeth
2010-01-01
A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces better learning curves by demanding the student to optimally dedicate their learning time on the failed examples. When used in machine learning, the algorithm is found to train a machine on a data with maximum variance in the feature space so that the generalization ability of the network improves. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery.
Rate control algorithm based on frame complexity estimation for MVC
Yan, Tao; An, Ping; Shen, Liquan; Zhang, Zhaoyang
2010-07-01
Rate control has not been well studied for multi-view video coding (MVC). In this paper, we propose an efficient rate control algorithm for MVC by improving the quadratic rate-distortion (R-D) model, which reasonably allocate bit-rate among views based on correlation analysis. The proposed algorithm consists of four levels for rate bits control more accurately, of which the frame layer allocates bits according to frame complexity and temporal activity. Extensive experiments show that the proposed algorithm can efficiently implement bit allocation and rate control according to coding parameters.
FAST UPDATE ALGORITHM FOR TCAM-BASED ROUTING LOOKUPS
Institute of Scientific and Technical Information of China (English)
王志恒; 叶强; 白英彩
2002-01-01
Routing technology has been forced to evolve towards higher capacity and per-port packet processing speed. The ability to achieve high forwarding speed is due to either software or hardware technology. TCAM (Ternary Content Addressable Memory) provides a performance advantage over other software or hardware search algorithms, often resulting in an order-of-magnitude reduction of search time. But slow updates may affect the performance of TCAM-based routing lookup. So the key is to design a table management algorithm, which supports high-speed updates in TCAMs. This paper presented three table management algorithms, and then compared their performance. Finally, the optimal one after comparing was given.
Performance evaluation of a texture-based segmentation algorithm
Sadjadi, Firooz A.
1991-07-01
Texture segmentations are crucial components of many remote sensing, scene analysis, and object recognition systems. However, very little attention has been paid to the problem of performance evaluation in the numerous algorithms that have been proposed by the image understanding community. In this paper, a particular algorithm is introduced and its performance is evaluated in a systematic manner on a wide range of scene and scenarios. Both the algorithm and the methodology used in its evaluation have significance in numerous applications in the computer-based image understanding field.
Automatic Image Registration Algorithm Based on Wavelet Transform
Institute of Scientific and Technical Information of China (English)
LIU Qiong; NI Guo-qiang
2006-01-01
An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the least-squares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.
A novel iris segmentation algorithm based on small eigenvalue analysis
Harish, B. S.; Aruna Kumar, S. V.; Guru, D. S.; Ngo, Minh Ngoc
2015-12-01
In this paper, a simple and robust algorithm is proposed for iris segmentation. The proposed method consists of two steps. In first step, iris and pupil is segmented using Robust Spatial Kernel FCM (RSKFCM) algorithm. RSKFCM is based on traditional Fuzzy-c-Means (FCM) algorithm, which incorporates spatial information and uses kernel metric as distance measure. In second step, small eigenvalue transformation is applied to localize iris boundary. The transformation is based on statistical and geometrical properties of the small eigenvalue of the covariance matrix of a set of edge pixels. Extensive experimentations are carried out on standard benchmark iris dataset (viz. CASIA-IrisV4 and UBIRIS.v2). We compared our proposed method with existing iris segmentation methods. Our proposed method has the least time complexity of O(n(i+p)) . The result of the experiments emphasizes that the proposed algorithm outperforms the existing iris segmentation methods.
Image Encryption Algorithm Based on Chaotic Economic Model
Directory of Open Access Journals (Sweden)
S. S. Askar
2015-01-01
Full Text Available In literature, chaotic economic systems have got much attention because of their complex dynamic behaviors such as bifurcation and chaos. Recently, a few researches on the usage of these systems in cryptographic algorithms have been conducted. In this paper, a new image encryption algorithm based on a chaotic economic map is proposed. An implementation of the proposed algorithm on a plain image based on the chaotic map is performed. The obtained results show that the proposed algorithm can successfully encrypt and decrypt the images with the same security keys. The security analysis is encouraging and shows that the encrypted images have good information entropy and very low correlation coefficients and the distribution of the gray values of the encrypted image has random-like behavior.
Cryptanalysis of an image encryption algorithm based on DNA encoding
Akhavan, A.; Samsudin, A.; Akhshani, A.
2017-10-01
Recently an image encryption algorithm based on DNA encoding and the Elliptic Curve Cryptography (ECC) is proposed. This paper aims to investigate the security the DNA-based image encryption algorithm and its resistance against chosen plaintext attack. The results of the analysis demonstrate that security of the algorithm mainly relies on one static shuffling step, with a simple confusion operation. In this study, a practical plain image recovery method is proposed, and it is shown that the images encrypted with the same key could easily be recovered using the suggested cryptanalysis method with as low as two chosen plain images. Also, a strategy to improve the security of the algorithm is presented in this paper.
A Modularity Degree Based Heuristic Community Detection Algorithm
Directory of Open Access Journals (Sweden)
Dongming Chen
2014-01-01
Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.
Efficient mining of association rules based on gravitational search algorithm
Directory of Open Access Journals (Sweden)
Fariba Khademolghorani
2011-07-01
Full Text Available Association rules mining are one of the most used tools to discover relationships among attributes in a database. A lot of algorithms have been introduced for discovering these rules. These algorithms have to mine association rules in two stages separately. Most of them mine occurrence rules which are easily predictable by the users. Therefore, this paper discusses the application of gravitational search algorithm for discovering interesting association rules. This evolutionary algorithm is based on the Newtonian gravity and the laws of motion. Furthermore, contrary to the previous methods, the proposed method in this study is able to mine the best association rules without generating frequent itemsets and is independent of the minimum support and confidence values. The results of applying this method in comparison with the method of mining association rules based upon the particle swarm optimization show that our method is successful.
Algorithmic Algebraic Combinatorics and Gröbner Bases
Klin, Mikhail; Jurisic, Aleksandar
2009-01-01
This collection of tutorial and research papers introduces readers to diverse areas of modern pure and applied algebraic combinatorics and finite geometries with a special emphasis on algorithmic aspects and the use of the theory of Grobner bases. Topics covered include coherent configurations, association schemes, permutation groups, Latin squares, the Jacobian conjecture, mathematical chemistry, extremal combinatorics, coding theory, designs, etc. Special attention is paid to the description of innovative practical algorithms and their implementation in software packages such as GAP and MAGM
QRS Detection Based on an Advanced Multilevel Algorithm
Wissam Jenkal; Rachid Latif; Ahmed Toumanari; Azzedine Dliou; Oussama El B’charri; Fadel Mrabih Rabou Maoulainine
2016-01-01
This paper presents an advanced multilevel algorithm used for the QRS complex detection. This method is based on three levels. The first permits the extraction of higher peaks using an adaptive thresholding technique. The second allows the QRS region detection. The last level permits the detection of Q, R and S waves. The proposed algorithm shows interesting results compared to recently published methods. The perspective of this work is the implementation of this method on an embedded system ...
Free Search Algorithm Based Estimation in WSN Location
Institute of Scientific and Technical Information of China (English)
ZHOU Hui; LI Dan-mei; SHAO Shi-huang; XU Chen
2009-01-01
This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search, which converts parameter estimation to on-line optimization of nonlinear function and estimates the coordinates of senor nodes using the Free Search optimization. Compared to the least-squares estimation algorithms, the localization accuracy has been increased significantly, which has been verified by the simulation results.
T-Algorithm-Based Logic Simulation on Distributed Systems
Sundaram, S; Patnaik, LM
1992-01-01
Increase in the complexity of VLSI digital circuit it sign demands faster logic simulation techniques than those currently available. One of the ways of speeding up existing logic simulataon algorithms is by exploiting the inherent parallelism an the sequentaal versaon. In this paper, we explore the possibility of mapping a T-algoriihm based logac samulataon algorithm onto a cluster of workstation interconnected by an ethernet. The set of gates at a particular level as partitioned by the hias...
PEA: Polymorphic Encryption Algorithm based on quantum computation
Komninos, N.; Mantas, G.
2011-01-01
In this paper, a polymorphic encryption algorithm (PEA), based on basic quantum computations, is proposed for the encryption of binary bits. PEA is a symmetric key encryption algorithm that applies different combinations of quantum gates to encrypt binary bits. PEA is also polymorphic since the states of the shared secret key control the different combinations of the ciphertext. It is shown that PEA achieves perfect secrecy and is resilient to eavesdropping and Trojan horse attacks. A securit...
Sampling-based Algorithms for Optimal Motion Planning
Karaman, Sertac
2011-01-01
During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically opti...
Face detection based on multiple kernel learning algorithm
Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun
2016-09-01
Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or not, and also its location, size. It can be seen as a binary classification problem, which can be well solved by support vector machine (SVM). Though SVM has strong model generalization ability, it has some limitations, which will be deeply analyzed in the paper. To access them, we study the principle and characteristics of the Multiple Kernel Learning (MKL) and propose a MKL-based face detection algorithm. In the paper, we describe the proposed algorithm in the interdisciplinary research perspective of machine learning and image processing. After analyzing the limitation of describing a face with a single feature, we apply several ones. To fuse them well, we try different kernel functions on different feature. By MKL method, the weight of each single function is determined. Thus, we obtain the face detection model, which is the kernel of the proposed method. Experiments on the public data set and real life face images are performed. We compare the performance of the proposed algorithm with the single kernel-single feature based algorithm and multiple kernels-single feature based algorithm. The effectiveness of the proposed algorithm is illustrated. Keywords: face detection, feature fusion, SVM, MKL
List-Based Simulated Annealing Algorithm for Traveling Salesman Problem
Directory of Open Access Journals (Sweden)
Shi-hua Zhan
2016-01-01
Full Text Available Simulated annealing (SA algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters’ setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA algorithm to solve traveling salesman problem (TSP. LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.
Majorization-minimization algorithms for wavelet-based image restoration.
Figueiredo, Mário A T; Bioucas-Dias, José M; Nowak, Robert D
2007-12-01
Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using l1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.
Improving Adenovirus Based Gene Transfer: Strategies to Accomplish Immune Evasion
Directory of Open Access Journals (Sweden)
Andrea Amalfitano
2010-09-01
Full Text Available Adenovirus (Ad based gene transfer vectors continue to be the platform of choice for an increasing number of clinical trials worldwide. In fact, within the last five years, the number of clinical trials that utilize Ad based vectors has doubled, indicating growing enthusiasm for the numerous positive characteristics of this gene transfer platform. For example, Ad vectors can be easily and relatively inexpensively produced to high titers in a cGMP compliant manner, can be stably stored and transported, and have a broad applicability for a wide range of clinical conditions, including both gene therapy and vaccine applications. Ad vector based gene transfer will become more useful as strategies to counteract innate and/or pre-existing adaptive immune responses to Ads are developed and confirmed to be efficacious. The approaches attempting to overcome these limitations can be divided into two broad categories: pre-emptive immune modulation of the host, and selective modification of the Ad vector itself. The first category of methods includes the use of immunosuppressive drugs or specific compounds to block important immune pathways, which are known to be induced by Ads. The second category comprises several innovative strategies inclusive of: (1 Ad-capsid-display of specific inhibitors or ligands; (2 covalent modifications of the entire Ad vector capsid moiety; (3 the use of tissue specific promoters and local administration routes; (4 the use of genome modified Ads; and (5 the development of chimeric or alternative serotype Ads. This review article will focus on both the promise and the limitations of each of these immune evasion strategies, and in the process delineate future directions in developing safer and more efficacious Ad-based gene transfer strategies.
Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators.
Bárdossy, András; Blinowska, Aleksandra; Kuzmicz, Wieslaw; Ollitrault, Jacky; Lewandowski, Michał; Przybylski, Andrzej; Jaworski, Zbigniew
2014-02-01
The paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value. The algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it. The total number of events in our training and validation sets was 132. In
A Novel Algorithm Based on 3D-MUSIC Algorithm for Localizing Near-Field Source
Institute of Scientific and Technical Information of China (English)
SHAN Zhi-yong; ZHOU Xi-lang; PEN Gen-jiang
2005-01-01
A novel 3-D MUSIC algorithm based on the classical 3D-MUSIC algorithm for the location of near-field source was presented. Under the far-field assumption of actual near-field, two algebraic relations of the location parameters between the actual near-field sources and the far-field ones were derived. With Fourier transformation and polynomial-root methods, the elevation and the azimuth of the far-field were obtained, the tracking paths can be developed, and the location parameters of the near-field source can be determined, then the more accurate results can be estimated using an optimization method. The computer simulation results p rove that the algorithm for the location of the near-fields is more accurate, effective and suitable for real-time applications.
A Color Image Edge Detection Algorithm Based on Color Difference
Zhuo, Li; Hu, Xiaochen; Jiang, Liying; Zhang, Jing
2016-12-01
Although image edge detection algorithms have been widely applied in image processing, the existing algorithms still face two important problems. On one hand, to restrain the interference of noise, smoothing filters are generally exploited in the existing algorithms, resulting in loss of significant edges. On the other hand, since the existing algorithms are sensitive to noise, many noisy edges are usually detected, which will disturb the subsequent processing. Therefore, a color image edge detection algorithm based on color difference is proposed in this paper. Firstly, a new operation called color separation is defined in this paper, which can reflect the information of color difference. Then, for the neighborhood of each pixel, color separations are calculated in four different directions to detect the edges. Experimental results on natural and synthetic images show that the proposed algorithm can remove a large number of noisy edges and be robust to the smoothing filters. Furthermore, the proposed edge detection algorithm is applied in road foreground segmentation and shadow removal, which achieves good performances.
A face recognition algorithm based on thermal and visible data
Sochenkov, Ilya; Tihonkih, Dmitrii; Vokhmintcev, Aleksandr; Melnikov, Andrey; Makovetskii, Artyom
2016-09-01
In this work we present an algorithm of fusing thermal infrared and visible imagery to identify persons. The proposed face recognition method contains several components. In particular this is rigid body image registration. The rigid registration is achieved by a modified variant of the iterative closest point (ICP) algorithm. We consider an affine transformation in three-dimensional space that preserves the angles between the lines. An algorithm of matching is inspirited by the recent results of neurophysiology of vision. Also we consider the ICP minimizing error metric stage for the case of an arbitrary affine transformation. Our face recognition algorithm also uses the localized-contouring algorithms to segment the subject's face; thermal matching based on partial least squares discriminant analysis. Thermal imagery face recognition methods are advantageous when there is no control over illumination or for detecting disguised faces. The proposed algorithm leads to good matching accuracies for different person recognition scenarios (near infrared, far infrared, thermal infrared, viewed sketch). The performance of the proposed face recognition algorithm in real indoor environments is presented and discussed.
Genetic Algorithm based PID controller for Frequency Regulation Ancillary services
Directory of Open Access Journals (Sweden)
Sandeep Bhongade
2010-12-01
Full Text Available In this paper, the parameters of Proportional, Integral and Derivative (PID controller for Automatic Generation Control (AGC suitable in restructured power system is tuned according to Generic Algorithms (GAs based performance indices. The key idea of the proposed method is to use the fitness function based on Area Control Error (ACE. The functioning of the proposed Genetic Algorithm based PID (GAPID controller has been demonstrated on a 75-bus Indian power system network and the results have been compared with those obtained by using Least Square Minimization method.
Optimization of HAART with genetic algorithms and agent-based models of HIV infection.
Castiglione, F; Pappalardo, F; Bernaschi, M; Motta, S
2007-12-15
Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection. The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups. A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html
Application of genetic algorithm to hexagon-based motion estimation.
Kung, Chih-Ming; Cheng, Wan-Shu; Jeng, Jyh-Horng
2014-01-01
With the improvement of science and technology, the development of the network, and the exploitation of the HDTV, the demands of audio and video become more and more important. Depending on the video coding technology would be the solution for achieving these requirements. Motion estimation, which removes the redundancy in video frames, plays an important role in the video coding. Therefore, many experts devote themselves to the issues. The existing fast algorithms rely on the assumption that the matching error decreases monotonically as the searched point moves closer to the global optimum. However, genetic algorithm is not fundamentally limited to this restriction. The character would help the proposed scheme to search the mean square error closer to the algorithm of full search than those fast algorithms. The aim of this paper is to propose a new technique which focuses on combing the hexagon-based search algorithm, which is faster than diamond search, and genetic algorithm. Experiments are performed to demonstrate the encoding speed and accuracy of hexagon-based search pattern method and proposed method.
Digital Image Encryption Algorithm Design Based on Genetic Hyperchaos
Directory of Open Access Journals (Sweden)
Jian Wang
2016-01-01
Full Text Available In view of the present chaotic image encryption algorithm based on scrambling (diffusion is vulnerable to choosing plaintext (ciphertext attack in the process of pixel position scrambling, we put forward a image encryption algorithm based on genetic super chaotic system. The algorithm, by introducing clear feedback to the process of scrambling, makes the scrambling effect related to the initial chaos sequence and the clear text itself; it has realized the image features and the organic fusion of encryption algorithm. By introduction in the process of diffusion to encrypt plaintext feedback mechanism, it improves sensitivity of plaintext, algorithm selection plaintext, and ciphertext attack resistance. At the same time, it also makes full use of the characteristics of image information. Finally, experimental simulation and theoretical analysis show that our proposed algorithm can not only effectively resist plaintext (ciphertext attack, statistical attack, and information entropy attack but also effectively improve the efficiency of image encryption, which is a relatively secure and effective way of image communication.
Face Recognition Algorithms Based on Transformed Shape Features
Directory of Open Access Journals (Sweden)
Sambhunath Biswas
2012-05-01
Full Text Available Human face recognition is, indeed, a challenging task, especially under illumination and pose variations. We examine in the present paper effectiveness of two simple algorithms using coiflet packet and Radon transforms to recognize human faces from some databases of still gray level images, under the environment of illumination and pose variations. Both the algorithms convert 2-D gray level training face images into their respective depth maps or physical shape which are subsequently transformed by Coiflet packet and Radon transforms to compute energy for feature extraction. Experiments show that such transformed shape features are robust to illumination and pose variations. With the features extracted, training classes are optimally separated through linear discriminant analysis (LDA, while classification for test face images is made through a k-NN classifier, based on L1 norm and Mahalanobis distance measures. Proposed algorithms are then tested on face images that differ in illumination,expression or pose separately, obtained from three databases,namely, ORL, Yale and Essex-Grimace databases. Results, so obtained, are compared with two different existing algorithms.Performance using Daubechies wavelets is also examined. It is seen that the proposed Coiflet packet and Radon transform based algorithms have significant performance, especially under different illumination conditions and pose variation. Comparison shows the proposed algorithms are superior.
Research on Quantum Searching Algorithms Based on Phase Shifts
Institute of Scientific and Technical Information of China (English)
ZHONG Pu-Cha; BAO Wan-Su
2008-01-01
@@ One iterative in Grover's original quantum search algorithm consists of two Hadamard-Walsh transformations, a selective amplitude inversion and a diffusion amplitude inversion. We concentrate on the relation among the probability of success of the algorithm, the phase shifts, the number of target items and the number of iterations via replacing the two amplitude inversions by phase shifts of an arbitrary φ = ψ(0 ≤φ, ψ≤ 2π). Then, according to the relation we find out the optimal phase shifts when the number of iterations is given. We present a new quantum search algorithm based on the optimal phase shifts of 1.018 after 0.5π /√M/N iterations. The new algorithm can obtain either a single target item or multiple target items in the search space with the probability of success at least 93.43%.
Electronic Commerce Logistics Network Optimization Based on Swarm Intelligent Algorithm
Directory of Open Access Journals (Sweden)
Yabing Jiao
2013-09-01
Full Text Available This article establish an efficient electronic commerce logistics operation system to reduce distribution costs and build a logistics network operation model based on around the B2C electronic commerce enterprise logistics network operation system. B2C electronic commerce transactions features in the enterprise network platform. To solve the NP-hard problem this article use hybrid ant colony algorithm, particle swarm algorithm and group swarm intelligence algorithm to get a best solution. According to the intelligent algorithm, design of electronic commerce logistics network optimization system, enter the national 22 electronic commerce logistics network for validation. Through the experiment to verify the optimized logistics cost greatly decreased. This research can help B2C electronic commerce enterprise logistics network to optimize decision-making under the premise of ensuring the interests of consumers and service levels also can be an effective way for enterprises to improve the efficiency of logistics services and reduce operation costs
PCNN document segmentation method based on bacterial foraging optimization algorithm
Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian
2014-04-01
Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.
A layer reduction based community detection algorithm on multiplex networks
Wang, Xiaodong; Liu, Jing
2017-04-01
Detecting hidden communities is important for the analysis of complex networks. However, many algorithms have been designed for single layer networks (SLNs) while just a few approaches have been designed for multiplex networks (MNs). In this paper, we propose an algorithm based on layer reduction for detecting communities on MNs, which is termed as LRCD-MNs. First, we improve a layer reduction algorithm termed as neighaggre to combine similar layers and keep others separated. Then, we use neighaggre to find the community structure hidden in MNs. Experiments on real-life networks show that neighaggre can obtain higher relative entropy than the other algorithm. Moreover, we apply LRCD-MNs on some real-life and synthetic multiplex networks and the results demonstrate that, although LRCD-MNs does not have the advantage in terms of modularity, it can obtain higher values of surprise, which is used to evaluate the quality of partitions of a network.
Knowledge Template Based Multi-perspective Car Recognition Algorithm
Directory of Open Access Journals (Sweden)
Bo Cai
2010-12-01
Full Text Available In order to solve the problem due to the vehicle-oriented society such as traffic jam or traffic accident, intelligent transportation system(ITS is raised and become scientist’s research focus, with the purpose of giving people better and safer driving condition and assistance. The core of intelligent transport system is the vehicle recognition and detection, and it’s the prerequisites for other related problems. Many existing vehicle recognition algorithms are aiming at one specific direction perspective, mostly front/back and side view. To make the algorithm more robust, our paper raised a vehicle recognition algorithm for oblique vehicles while also do research on front/back and side ones. The algorithm is designed based on the common knowledge of the car, such as shape, structure and so on. The experimental results of many car images show that our method has fine accuracy in car recognition.
Meteosat Images Encryption based on AES and RSA Algorithms
Directory of Open Access Journals (Sweden)
Boukhatem Mohammed Belkaid
2015-06-01
Full Text Available Satellite image Security is playing a vital role in the field of communication system and Internet. This work is interested in securing transmission of Meteosat images on the Internet, in public or local networks. To enhance the security of Meteosat transmission in network communication, a hybrid encryption algorithm based on Advanced Encryption Standard (AES and Rivest Shamir Adleman (RSA algorithms is proposed. AES algorithm is used for data transmission because of its higher efficiency in block encryption and RSA algorithm is used for the encryption of the key of the AES because of its management advantages in key cipher. Our encryption system generates a unique password every new session of encryption. Cryptanalysis and various experiments have been carried out and the results were reported in this paper, which demonstrate the feasibility and flexibility of the proposed scheme.
Target Image Matching Algorithm Based on Binocular CCD Ranging
Directory of Open Access Journals (Sweden)
Dongming Li
2014-01-01
Full Text Available This paper proposed target image in a subpixel level matching algorithm for binocular CCD ranging, which is based on the principle of binocular CCD ranging. In the paper, firstly, we introduced the ranging principle of the binocular ranging system and deduced a binocular parallax formula. Secondly, we deduced the algorithm which was named improved cross-correlation matching algorithm and cubic surface fitting algorithm for target images matched, and it could achieve a subpixel level matching for binocular CCD ranging images. Lastly, through experiment we have analyzed and verified the actual CCD ranging images, then analyzed the errors of the experimental results and corrected the formula of calculating system errors. Experimental results showed that the actual measurement accuracy of a target within 3 km was higher than 0.52%, which meet the accuracy requirements of the high precision binocular ranging.
Validation of a Bayesian-based isotope identification algorithm
Energy Technology Data Exchange (ETDEWEB)
Sullivan, C.J.; Stinnett, J., E-mail: stinnettjacob@gmail.com
2015-06-01
Handheld radio-isotope identifiers (RIIDs) are widely used in Homeland Security and other nuclear safety applications. However, most commercially available devices have serious problems in their ability to correctly identify isotopes. It has been reported that this flaw is largely due to the overly simplistic identification algorithms on-board the RIIDs. This paper reports on the experimental validation of a new isotope identification algorithm using a Bayesian statistics approach to identify the source while allowing for calibration drift and unknown shielding. We present here results on further testing of this algorithm and a study on the observed variation in the gamma peak energies and areas from a wavelet-based peak identification algorithm.
Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms
Directory of Open Access Journals (Sweden)
Cheng-Yuan Shih
2010-01-01
Full Text Available This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA and quadratic discriminant analysis (QDA. It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.
A Load Balance Routing Algorithm Based on Uneven Clustering
Directory of Open Access Journals (Sweden)
Liang Yuan
2013-10-01
Full Text Available Aiming at the problem of uneven load in clustering Wireless Sensor Network (WSN, a kind of load balance routing algorithm based on uneven clustering is proposed to do uneven clustering and calculate optimal number of clustering. This algorithm prevents the number of common node under some certain cluster head from being too large which leads load to be overweight to death through even node clustering. It constructs evaluation function which can better reflect residual energy distribution of nodes and at the same time constructs routing evaluation function between cluster heads which uses MATLAB to do simulation on the performance of this algorithm. Simulation result shows that the routing established by this algorithm effectively improves network’s energy balance and lengthens the life cycle of network.
Dynamic Obfuscation Algorithm based on Demand-Driven Symbolic Execution
Directory of Open Access Journals (Sweden)
Yubo Yang
2014-06-01
Full Text Available Dynamic code obfuscation technique increases the difficulty of dynamically reverse by the runtime confusion. Path explosion directly affects the efficiency and accuracy of dynamic symbolic analysis. Because of the defect, this paper presents a novel algorithm DDD (Demand-Driven Dynamic Obfuscation Algorithm by using the demand-driven theory of symbolic analysis. First, create a large number of invalid paths to mislead the result of symbolic analysis. Second, according to the demand-driven theory, create a specific execution path to protect the security of software. The design and implementation of the algorithm is based on the current popular and mature SMT (satisfiability model theory, and the experimental effects are tested by Z3 - the SMT solver and Pex - the symbolic execution test tools. The experimental results prove that the algorithm enhance the security of the program.
Measuring Disorientation Based on the Needleman-Wunsch Algorithm
Directory of Open Access Journals (Sweden)
Tolga Güyer
2015-04-01
Full Text Available This study offers a new method to measure navigation disorientation in web based systems which is powerful learning medium for distance and open education. The Needleman-Wunsch algorithm is used to measure disorientation in a more precise manner. The process combines theoretical and applied knowledge from two previously distinct research areas, disorientation and string-matching. String-matching algorithms provide a more convenient disorientation measurement than other techniques, in that they examine the similarity between an optimal path and learners’ navigation paths. The algorithm particularly takes into account the contextual similarity between partly relevant web-pages in a user’s navigation path and pages in an optimal path. This study focuses on the reasons and the required steps to use this algorithm for disorientation measurement. Examples of actual student activities and learning environment data are provided to illustrate the process.
Hybrid Collision Detection Algorithm based on Image Space
Directory of Open Access Journals (Sweden)
XueLi Shen
2013-07-01
Full Text Available Collision detection is an important application in the field of virtual reality, and efficiently completing collision detection has become the research focus. For the poorly real-time defect of collision detection, this paper has presented an algorithm based on the hybrid collision detection, detecting the potential collision object sets quickly with the mixed bounding volume hierarchy tree, and then using the streaming pattern collision detection algorithm to make an accurate detection. With the above methods, it can achieve the purpose of balancing load of the CPU and GPU and speeding up the detection rate. The experimental results show that compared with the classic Rapid algorithm, this algorithm can effectively improve the efficiency of collision detection.
Medical Images Watermarking Algorithm Based on Improved DCT
Directory of Open Access Journals (Sweden)
Yv-fan SHANG
2013-12-01
Full Text Available Targeting at the incessant securities problems of digital information management system in modern medical system, this paper presents the robust watermarking algorithm for medical images based on Arnold transformation and DCT. The algorithm first deploys the scrambling technology to encrypt the watermark information and then combines it with the visual feature vector of the image to generate a binary logic series through the hash function. The sequence as taken as keys and stored in the third party to obtain ownership of the original image. Having no need for artificial selection of a region of interest, no capacity constraint, no participation of the original medical image, such kind of watermark extracting solves security and speed problems in the watermark embedding and extracting. The simulation results also show that the algorithm is simple in operation and excellent in robustness and invisibility. In a word, it is more practical compared with other algorithms
A robust DCT domain watermarking algorithm based on chaos system
Xiao, Mingsong; Wan, Xiaoxia; Gan, Chaohua; Du, Bo
2009-10-01
Digital watermarking is a kind of technique that can be used for protecting and enforcing the intellectual property (IP) rights of the digital media like the digital images containting in the transaction copyright. There are many kinds of digital watermarking algorithms. However, existing digital watermarking algorithms are not robust enough against geometric attacks and signal processing operations. In this paper, a robust watermarking algorithm based on chaos array in DCT (discrete cosine transform)-domain for gray images is proposed. The algorithm provides an one-to-one method to extract the watermark.Experimental results have proved that this new method has high accuracy and is highly robust against geometric attacks, signal processing operations and geometric transformations. Furthermore, the one who have on idea of the key can't find the position of the watermark embedded in. As a result, the watermark not easy to be modified, so this scheme is secure and robust.
Solution of optimal power flow using evolutionary-based algorithms
African Journals Online (AJOL)
This paper applies two reliable and efficient evolutionary-based methods named Shuffled Frog Leaping Algorithm ... Grey Wolf Optimizer (GWO) to solve Optimal Power Flow (OPF) problem. OPF is ..... The wolves search for the prey based on the alpha, beta, and delta positions. ..... Energy Conversion and Management, Vol.
Knowledge Automatic Indexing Based on Concept Lexicon and Segmentation Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Lan-cheng; JIANG Dan; LE Jia-jin
2005-01-01
This paper is based on two existing theories about automatic indexing of thematic knowledge concept. The prohibit-word table with position information has been designed. The improved Maximum Matching-Minimum Backtracking method has been researched. Moreover it has been studied on improved indexing algorithm and application technology based on rules and thematic concept word table.
Approximation Algorithms for Model-Based Diagnosis
Feldman, A.B.
2010-01-01
Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation a
Approximation Algorithms for Model-Based Diagnosis
Feldman, A.B.
2010-01-01
Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation a
An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems.
Timmis, J; Ismail, A R; Bjerknes, J D; Winfield, A F T
2016-08-01
Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots.
TWO NEW FCT ALGORITHMS BASED ON PRODUCT SYSTEM
Institute of Scientific and Technical Information of China (English)
Guo Zhaoli; Shi Baochang; Wang Nengchao
2001-01-01
In this paper we present a product system and give arepresentation for consine functions with the sys tem. Based on the formula two new algorithms are designed for computing the Discrete Cosine Transform. Both algorithms have regular recursive structure and good numerical stability and are easy to parallize. CLC Number：O17 Document ID：AReferences：[1]Arguello,F. and Zapata,E. L. ,Fast Cosine Transform Based on the Successive Doubling Method,Electronics Lett.,26:19,1990,1616-1618.[2]Chan,S.C. and Ho,K.L. ,Direct Methods for Computing Discrete Sinusoidal Transform,IEE Proceedings,136: 6,1990,433- 442.[3]Chan,S.C. and Ho,K.L. ,A New Two-Dimensional Fast Cosine Transform Algorithm,IEEE Trans. Signal Processing,32:2,1991,481-485.[4]Cvetkovic,Z. and Popovic,M. V.,New Fast Recursive Algorithms for the Computation of Discrete Cosine and Sine Transforms,IEEE Trans. Signal Processing,40: 8,1992,2083-2086.[5]Hou,H.S.,A Fast Recursive Algorithm for Computing the Discrete Cosine Transform,IEEE Trans. ASSP-35:10,1987,1455-1461.[6]Lee,B.G. ,A New Algorithm to Compute the Discrete Cosine Transform,IEEE Trans. ASSP,Vol. ASSP-32:6,1984,1243-1245.[7]Lee,P. and Uang,F. Y.,Restructured Recursive DCT and DST Algorithms,IEEE Trans.Signal Processing,42: 7,1994,1600- 1609.[8]Yun,D. and Lee,S.U. ,On the Fixed-Point Error Analysis of Several Fast IDCT Algorithms,IEEE Trans. Circuits and Systems- I : Analog and Digital Signal Processing,42 : 11,1995,686- 692.Manuscript Received：2000年2月20日Published：2001年9月1日
Relevance Feedback Algorithm Based on Collaborative Filtering in Image Retrieval
Directory of Open Access Journals (Sweden)
Yan Sun
2010-12-01
Full Text Available Content-based image retrieval is a very dynamic study field, and in this field, how to improve retrieval speed and retrieval accuracy is a hot issue. The retrieval performance can be improved when applying relevance feedback to image retrieval and introducing the participation of people to the retrieval process. However, as for many existing image retrieval methods, there are disadvantages of relevance feedback with information not being fully saved and used, and their accuracy and flexibility are relatively poor. Based on this, the collaborative filtering technology was combined with relevance feedback in this study, and an improved relevance feedback algorithm based on collaborative filtering was proposed. In the method, the collaborative filtering technology was used not only to predict the semantic relevance between images in database and the retrieval samples, but to analyze feedback log files in image retrieval, which can make the historical data of relevance feedback be fully used by image retrieval system, and further to improve the efficiency of feedback. The improved algorithm presented has been tested on the content-based image retrieval database, and the performance of the algorithm has been analyzed and compared with the existing algorithms. The experimental results showed that, compared with the traditional feedback algorithms, this method can obviously improve the efficiency of relevance feedback, and effectively promote the recall and precision of image retrieval.
Designers' Cognitive Thinking Based on Evolutionary Algorithms
Zhang Shutao; Jianning Su; Chibing Hu; Peng Wang
2013-01-01
The research on cognitive thinking is important to construct the efficient intelligent design systems. But it is difficult to describe the model of cognitive thinking with reasonable mathematical theory. Based on the analysis of design strategy and innovative thinking, we investigated the design cognitive thinking model that included the external guide thinking of "width priority - depth priority" and the internal dominated thinking of "divergent thinking - convergent thinking", built a reaso...
Multi-Task Collaboration CPS Modeling Based on Immune Feedback
Directory of Open Access Journals (Sweden)
Haiying Li
2013-09-01
Full Text Available In this paper, a dynamic multi-task collaboration CPS control model based on the self-adaptive immune feedback is proposed and implemented in the smart home environment. First, the internal relations between CPS and the biological immune system are explored via their basic theories. Second, CPS control mechanism is elaborated through the analysis of CPS control structure. Finally, a comprehensive strategy for support is introduced into multi-task collaboration to improve the dynamic cognitive ability. At the same time, the performance of parameters is correspondingly increased by the operator of the antibody concentration and the selective pressure. Furthermore, the model has been put into service in the smart home laboratory. The experimental results show that this model can integrate user’s needs into the environment for properly regulating the home environment.
Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm
Dong Yumin; Zhao Li
2014-01-01
Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the a...
van der Lee, J H; Svrcek, W Y; Young, B R
2008-01-01
Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.
Plagiarism Detection Based on SCAM Algorithm
DEFF Research Database (Denmark)
Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio
2011-01-01
Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...... document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall....
Plagiarism Detection Based on SCAM Algorithm
DEFF Research Database (Denmark)
Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio
2011-01-01
Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...
New Artificial Immune System Approach Based on Monoclonal Principle for Job Recommendation
Directory of Open Access Journals (Sweden)
Shaha Al-Otaibi
2016-04-01
Full Text Available Finding the best solution for an optimization problem is a tedious task, specifically in the presence of enormously represented features. When we handle a problem such as job recommendations that have a diversity of their features, we should rely to metaheuristics. For example, the Artificial Immune System which is a novel computational intelligence paradigm achieving diversification and exploration of the search space as well as exploitation of the good solutions were reached in reasonable time. Unfortunately, in problems with diversity nature such job recommendation, it produces a huge number of antibodies that causes a large number of matching processes affect the system efficiency. To leverage this issue, we present a new intelligence algorithm inspired by immunology based on monoclonal antibodies production principle that, up to our knowledge, has never applied in science and engineering problems. The proposed algorithm recommends ranked list of best applicants for a certain job. We discussed the design issues, as well as the immune system processes that should be applied to the problem. Finally, the experiments are conducted that shown an excellence of our approach.
Novel Frequency Hopping Sequences Generator Based on AES Algorithm
Institute of Scientific and Technical Information of China (English)
李振荣; 庄奕琪; 张博; 张超
2010-01-01
A novel frequency hopping(FH) sequences generator based on advanced encryption standard(AES) iterated block cipher is proposed for FH communication systems.The analysis shows that the FH sequences based on AES algorithm have good performance in uniformity, correlation, complexity and security.A high-speed, low-power and low-cost ASIC of FH sequences generator is implemented by optimizing the structure of S-Box and MixColumns of AES algorithm, proposing a hierarchical power management strategy, and applying ...
Optimal design of steel portal frames based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
Yue CHEN; Kai HU
2008-01-01
As for the optimal design of steel portal frames, due to both the complexity of cross selections of beams and columns and the discreteness of design variables, it is difficult to obtain satisfactory results by traditional optimization. Based on a set of constraints of the Technical Specification for Light-weighted Steel Portal Frames of China, a genetic algorithm (GA) optimization program for portal frames, written in MATLAB code, was proposed in this paper. The graph user interface (GUI) is also developed for this optimal program, so that it can be used much more conveniently. Finally, some examples illustrate the effectiveness and efficiency of the genetic-algorithm-based optimal program.
A novel bit-quad-based Euler number computing algorithm
Yao, Bin; He, Lifeng; Kang, Shiying; Chao, Yuyan; Xiao ZHAO
2015-01-01
The Euler number of a binary image is an important topological property in computer vision and pattern recognition. This paper proposes a novel bit-quad-based Euler number computing algorithm. Based on graph theory and analysis on bit-quad patterns, our algorithm only needs to count two bit-quad patterns. Moreover, by use of the information obtained during processing the previous bit-quad, the average number of pixels to be checked for processing a bit-quad is only 1.75. Experimental results ...
Half-global discretization algorithm based on rough set theory
Institute of Scientific and Technical Information of China (English)
Tan Xu; Chen Yingwu
2009-01-01
It is being widely studied how to extract knowledge from a decision table based on rough set theory. The novel problem is how to discretize a decision table having continuous attribute. In order to obtain more reasonable discretization results, a discretization algorithm is proposed, which arranges half-global discretization based on the correlational coefficient of each continuous attribute while considering the uniqueness of rough set theory. When choosing heuristic information, stability is combined with rough entropy. In terms of stability, the possibility of classifying objects belonging to certain sub-interval of a given attribute into neighbor sub-intervals is minimized. By doing this, rational discrete intervals can be determined. Rough entropy is employed to decide the optimal cut-points while guaranteeing the consistency of the decision table after discretization. Thought of this algorithm is elaborated through Iris data and then some experiments by comparing outcomes of four discritized datasets are also given, which are calculated by the proposed algorithm and four other typical algorithms for discritization respectively. After that, classification rules are deduced and summarized through rough set based classifiers. Results show that the proposed discretization algorithm is able to generate optimal classification accuracy while minimizing the number of discrete intervals. It displays superiority especially when dealing with a decision table having a large attribute number.
A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis
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Zhiming Song
2015-01-01
Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.
A novel multiobjective evolutionary algorithm based on regression analysis.
Song, Zhiming; Wang, Maocai; Dai, Guangming; Vasile, Massimiliano
2015-01-01
As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m - 1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m - 1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.
Cosine-Based Clustering Algorithm Approach
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Mohammed A. H. Lubbad
2012-02-01
Full Text Available Due to many applications need the management of spatial data; clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shapes. It must be insensitive to the outliers (noise and the order of input data. In this paper Cosine Cluster is proposed based on cosine transformation, which satisfies all the above requirements. Using multi-resolution property of cosine transforms, arbitrary shape clusters can be effectively identified at different degrees of accuracy. Cosine Cluster is also approved to be highly efficient in terms of time complexity. Experimental results on very large data sets are presented, which show the efficiency and effectiveness of the proposed approach compared to other recent clustering methods.
Improved motion information-based infrared dim target tracking algorithms
Lei, Liu; Zhijian, Huang
2014-11-01
Accurate and fast tracking of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. However, under complex backgrounds, such as clutter, varying illumination, and occlusion, the traditional tracking method often converges to a local maximum and loses the real infrared target. To cope with these problems, three improved tracking algorithm based on motion information are proposed in this paper, namely improved mean shift algorithm, improved Optical flow method and improved Particle Filter method. The basic principles and the implementing procedure of these modified algorithms for target tracking are described. Using these algorithms, the experiments on some real-life IR and color images are performed. The whole algorithm implementing processes and results are analyzed, and those algorithms for tracking targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying tracking effectiveness and robustness. Meanwhile, it has high tracking efficiency and can be used for real-time tracking.
A Metaheuristic Algorithm Based on Chemotherapy Science: CSA
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Mohammad Hassan Salmani
2017-01-01
Full Text Available Among scientific fields of study, mathematical programming has high status and its importance has led researchers to develop accurate models and effective solving approaches to addressing optimization problems. In particular, metaheuristic algorithms are approximate methods for solving optimization problems whereby good (not necessarily optimum solutions can be generated via their implementation. In this study, we propose a population-based metaheuristic algorithm according to chemotherapy method to cure cancers that mainly search the infeasible region. As in chemotherapy, Chemotherapy Science Algorithm (CSA tries to kill inappropriate solutions (cancers and bad cells of the human body; however, this would inevitably risk incidentally destroying some acceptable solutions (healthy cells. In addition, as the cycle of cancer treatment repeats over and over, the algorithm is iterated. To align chemotherapy process with the proposed algorithm, different basic terms and definitions including Infeasibility Function (IF, objective function (OF, Cell Area (CA, and Random Cells (RCs are presented in this study. In the terminology of algorithms and optimization, IF and OF are mainly applicable as criteria to compare every pair of generated solutions. Finally, we test CSA and its structure using the benchmark Traveling Salesman Problem (TSP.
A Global Path Planning Algorithm Based on Bidirectional SVGA
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Taizhi Lv
2017-01-01
Full Text Available For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎. This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.
Texture orientation-based algorithm for detecting infrared maritime targets.
Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai
2015-05-20
Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.
Jie, Yu; Gang, Wang; Teng, Zhu; Xiaojuan, Li; Qin, Yan
2014-01-01
An unsupervised classification method based on the H/α classifier and artificial immune system (AIS) is proposed to overcome the inefficiencies that arise when traditional classification methods deal with polarimetric synthetic aperture radar (PolSAR) data having large numbers of overlapping pixels and excess polarimetric information. The method is composed of two steps. First, Cloude-Pottier decomposition is used to obtain the entropy H and the scattering angle α. The classification result based on the H/α plane is used to initialize the AIS algorithm. Second, to obtain accurate results, the AIS clonal selection algorithm is used to perform an iterative calculation. As a self-organizing, self-recognizing, and self-optimizing algorithm, the AIS is able to obtain a global optimal solution and better classification results by making use of both the scattering mechanism of ground features and polarimetric scattering characteristics. The effectiveness and feasibility of this method are demonstrated by experiments using a NASA-JPL PolSAR image and a high-resolution PolSAR image of Lingshui autonomous county in Hainan Province.
Institute of Scientific and Technical Information of China (English)
盛云雷; 李永忠; 荆春伟
2011-01-01
将人工免疫原理与高速网络分流技术结合,提出了一种基于免疫原理的高速网络入侵防御算法.在对阴性选择算法改进基础上引入疫苗算子和选择算子,降低了系统二次免疫应答时间,增强了系统抗体库自我学习能力,进而改善了系统的实时性和高效性.在分析了高速网络分流技术和改进阴性选择算法的基础上,建立了基于免疫原理的高速网络入侵防御系统.仿真结果证明了该算法的有效性.%A new method is proposed against the limited capacity in making real-time intrusion prevention efficiently for high-speed network, that is the intrusion prevension algorithm based on immune principle, combining of the artificial immune principle and data-distribution technology.In the research on negative selection algorithm, the vaccine operator and positive selection operator are introduced to the algorithm.Owing to the new algorithm, self-learning ability of antibody bank gets enhanced and the time needed for secondary response is reduced, so that the efficiency of system gets greatly improved.After the analysis of data-distribution technology and negative selection algorithm,the intrusion prevension system based on immune principle is established.Simulation results show the effectiveness of the algorithm.
A Scheduling Algorithm Based on Petri Nets and Simulated Annealing
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Rachida H. Ghoul
2007-01-01
Full Text Available This study aims at presenting a hybrid Flexible Manufacturing System "HFMS" short-term scheduling problem. Based on the art state of general scheduling algorithms, we present the meta-heuristic, we have decided to apply for a given example of HFMS. That was the study of Simulated Annealing Algorithm SA. The HFMS model based on hierarchical Petri nets, was used to represent static and dynamic behavior of the HFMS and design scheduling solutions. Hierarchical Petri nets model was regarded as being made up a set of single timed colored Petri nets models. Each single model represents one process which was composed of many operations and tasks. The complex scheduling problem was decomposed in simple sub-problems. Scheduling algorithm was applied on each sub model in order to resolve conflicts on shared production resources.
The PCNN adaptive segmentation algorithm based on visual perception
Zhao, Yanming
To solve network adaptive parameter determination problem of the pulse coupled neural network (PCNN), and improve the image segmentation results in image segmentation. The PCNN adaptive segmentation algorithm based on visual perception of information is proposed. Based on the image information of visual perception and Gabor mathematical model of Optic nerve cells receptive field, the algorithm determines adaptively the receptive field of each pixel of the image. And determines adaptively the network parameters W, M, and β of PCNN by the Gabor mathematical model, which can overcome the problem of traditional PCNN parameter determination in the field of image segmentation. Experimental results show that the proposed algorithm can improve the region connectivity and edge regularity of segmentation image. And also show the PCNN of visual perception information for segmentation image of advantage.
CBFS: high performance feature selection algorithm based on feature clearness.
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Minseok Seo
Full Text Available BACKGROUND: The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes. This is expected to bring reduction of processing time and improvement of classification accuracy. METHODOLOGY: In this study, we devised a new feature selection algorithm (CBFS based on clearness of features. Feature clearness expresses separability among classes in a feature. Highly clear features contribute towards obtaining high classification accuracy. CScore is a measure to score clearness of each feature and is based on clustered samples to centroid of classes in a feature. We also suggest combining CBFS and other algorithms to improve classification accuracy. CONCLUSIONS/SIGNIFICANCE: From the experiment we confirm that CBFS is more excellent than up-to-date feature selection algorithms including FeaLect. CBFS can be applied to microarray gene selection, text categorization, and image classification.
An Improved FCM Medical Image Segmentation Algorithm Based on MMTD
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Ningning Zhou
2014-01-01
Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.
Genetic algorithm-based evaluation of spatial straightness error
Institute of Scientific and Technical Information of China (English)
崔长彩; 车仁生; 黄庆成; 叶东; 陈刚
2003-01-01
A genetic algorithm ( GA ) -based approach is proposed to evaluate the straightness error of spatial lines. According to the mathematical definition of spatial straightness, a verification model is established for straightness error, and the fitness function of GA is then given and the implementation techniques of the proposed algorithm is discussed in detail. The implementation techniques include real number encoding, adaptive variable range choosing, roulette wheel and elitist combination selection strategies, heuristic crossover and single point mutation schemes etc. An application example is quoted to validate the proposed algorithm. The computation result shows that the GA-based approach is a superior nonlinear parallel optimization method. The performance of the evolution population can be improved through genetic operations such as reproduction, crossover and mutation until the optimum goal of the minimum zone solution is obtained. The quality of the solution is better and the efficiency of computation is higher than other methods.
A structural comparison of measurement-based admission control algorithms
Institute of Scientific and Technical Information of China (English)
GU Yi-ran; WANG Suo-ping; WU Hai-ya
2006-01-01
Measurement-based admission control (MBAC)algorithm is designed for the relaxed real-time service. In contrast to traditional connection admission control mechanisms,the most attractive feature of MBAC algorithm is that it does not require a prior traffic model and that is very difficult for the user to come up with a tight traffic model before establishing a flow.Other advantages of MBAC include that it can achieve higher network utilization and offer quality service to users. In this article, the study of the equations in the MBAC shows that they can all be expressed in the same form. Based on the same form,some MBAC algorithms can achieve same performance only if they satisfy some conditions.
A fast image encryption algorithm based on chaotic map
Liu, Wenhao; Sun, Kehui; Zhu, Congxu
2016-09-01
Derived from Sine map and iterative chaotic map with infinite collapse (ICMIC), a new two-dimensional Sine ICMIC modulation map (2D-SIMM) is proposed based on a close-loop modulation coupling (CMC) model, and its chaotic performance is analyzed by means of phase diagram, Lyapunov exponent spectrum and complexity. It shows that this map has good ergodicity, hyperchaotic behavior, large maximum Lyapunov exponent and high complexity. Based on this map, a fast image encryption algorithm is proposed. In this algorithm, the confusion and diffusion processes are combined for one stage. Chaotic shift transform (CST) is proposed to efficiently change the image pixel positions, and the row and column substitutions are applied to scramble the pixel values simultaneously. The simulation and analysis results show that this algorithm has high security, low time complexity, and the abilities of resisting statistical analysis, differential, brute-force, known-plaintext and chosen-plaintext attacks.
An Algorithm on Generating Lattice Based on Layered Concept Lattice
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Zhang Chang-sheng
2013-08-01
Full Text Available Concept lattice is an effective tool for data analysis and rule extraction, a bottleneck factor on impacting the applications of concept lattice is how to generate lattice efficiently. In this paper, an algorithm LCLG on generating lattice in batch processing based on layered concept lattice is developed, this algorithm is based on layered concept lattice, the lattice is generated downward layer by layer through concept nodes and provisional nodes in current layer; the concept nodes are found parent-child relationships upward layer by layer, then the Hasse diagram of inter-layer connection is generated; in the generated process of the lattice nodes in each layer, we do the pruning operations dynamically according to relevant properties, and delete some unnecessary nodes, such that the generating speed is improved greatly; the experimental results demonstrate that the proposed algorithm has good performance.
Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks
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Ruiyun Yu
2014-01-01
Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.
Time-Based Dynamic Trust Model Using Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
TANG Zhuo; LU Zhengding; LI Kai
2006-01-01
The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts'change that is aroused by the time' lapse and the inter-operation through an instance.
LAHS: A novel harmony search algorithm based on learning automata
Enayatifar, Rasul; Yousefi, Moslem; Abdullah, Abdul Hanan; Darus, Amer Nordin
2013-12-01
This study presents a learning automata-based harmony search (LAHS) for unconstrained optimization of continuous problems. The harmony search (HS) algorithm performance strongly depends on the fine tuning of its parameters, including the harmony consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth (bw). Inspired by the spur-in-time responses in the musical improvisation process, learning capabilities are employed in the HS to select these parameters based on spontaneous reactions. An extensive numerical investigation is conducted on several well-known test functions, and the results are compared with the HS algorithm and its prominent variants, including the improved harmony search (IHS), global-best harmony search (GHS) and self-adaptive global-best harmony search (SGHS). The numerical results indicate that the LAHS is more efficient in finding optimum solutions and outperforms the existing HS algorithm variants.
Effective ANT based Routing Algorithm for Data Replication in MANETs
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N.J. Nithya Nandhini
2013-12-01
Full Text Available In mobile ad hoc network, the nodes often move and keep on change its topology. Data packets can be forwarded from one node to another on demand. To increase the data accessibility data are replicated at nodes and made as sharable to other nodes. Assuming that all mobile host cooperative to share their memory and allow forwarding the data packets. But in reality, all nodes do not share the resources for the benefits of others. These nodes may act selfishly to share memory and to forward the data packets. This paper focuses on selfishness of mobile nodes in replica allocation and routing protocol based on Ant colony algorithm to improve the efficiency. The Ant colony algorithm is used to reduce the overhead in the mobile network, so that it is more efficient to access the data than with other routing protocols. This result shows the efficiency of ant based routing algorithm in the replication allocation.
Cai, Xiuhong; Li, Xiang; Qi, Hong; Wei, Fang; Chen, Jianyong; Shuai, Jianwei
2016-10-01
The gating properties of the inositol 1, 4, 5-trisphosphate (IP3) receptor (IP3R) are determined by the binding and unbinding capability of Ca2+ ions and IP3 messengers. With the patch clamp experiments, the stationary properties have been discussed for Xenopus oocyte type-1 IP3R (Oo-IP3R1), type-3 IP3R (Oo-IP3R3) and Spodoptera frugiperda IP3R (Sf-IP3R). In this paper, in order to provide insights about the relation between the observed gating characteristics and the gating parameters in different IP3Rs, we apply the immune algorithm to fit the parameters of a modified DeYoung-Keizer model. By comparing the fitting parameter distributions of three IP3Rs, we suggest that the three types of IP3Rs have the similar open sensitivity in responding to IP3. The Oo-IP3R3 channel is easy to open in responding to low Ca2+ concentration, while Sf-IP3R channel is easily inhibited in responding to high Ca2+ concentration. We also show that the IP3 binding rate is not a sensitive parameter for stationary gating dynamics for three IP3Rs, but the inhibitory Ca2+ binding/unbinding rates are sensitive parameters for gating dynamics for both Oo-IP3R1 and Oo-IP3R3 channels. Such differences may be important in generating the spatially and temporally complex Ca2+ oscillations in cells. Our study also demonstrates that the immune algorithm can be applied for model parameter searching in biological systems.
An Initiative-Learning Algorithm Based on System Uncertainty
Institute of Scientific and Technical Information of China (English)
ZHAO Jun
2005-01-01
Initiative-learning algorithms are characterized by and hence advantageous for their independence of prior domain knowledge.Usually,their induced results could more objectively express the potential characteristics and patterns of information systems.Initiative-learning processes can be effectively conducted by system uncertainty,because uncertainty is an intrinsic common feature of and also an essential link between information systems and their induced results.Obviously,the effectiveness of such initiative-learning framework is heavily dependent on the accuracy of system uncertainty measurements.Herein,a more reasonable method for measuring system uncertainty is developed based on rough set theory and the conception of information entropy;then a new algorithm is developed on the bases of the new system uncertainty measurement and the Skowron's algorithm for mining propositional default decision rules.The proposed algorithm is typically initiative-learning.It is well adaptable to system uncertainty.As shown by simulation experiments,its comprehensive performances are much better than those of congeneric algorithms.
Directory of Open Access Journals (Sweden)
K. Kumaravel
2015-05-01
Full Text Available Wireless Mesh Network (WMN uses the latest technology which helps in providing end users a high quality service referred to as the Internet’s “last mile”. Also considering WMN one of the most important technologies that are employed is multicast communication. Among the several issues routing which is significantly an important issue is addressed by every WMN technologies and this is done during the process of data transmission. The IEEE 802.11s Standard entails and sets procedures which need to be followed to facilitate interconnection and thus be able to devise an appropriate WMN. There has been introduction of several protocols by many authors which are mainly devised on the basis of machine learning and artificial intelligence. Multi-path routing may be considered as one such routing method which facilitates transmission of data over several paths, proving its capabilities as a useful strategy for achieving reliability in WMN. Though, multi-path routing in any manner cannot really guarantee deterministic transmission. As here there are multiple paths available for enabling data transmission from source to destination node. The algorithm that had been employed before in the studies conducted did not take in to consideration routing metrics which include energy aware metrics that are used for path selection during transferring of data. The following study proposes use of the hybrid multipath routing algorithm while taking in to consideration routing metrics which include energy, minimal loss for efficient path selection and transferring of data. Proposed algorithm here has two phases. In the first phase prim’s algorithm has been proposed so that in networks route discovery may be possible. For the second one the Hybrid firefly algorithm which is based on harmony search has been employed for selection of the most suitable and best through proper analysis of metrics which include energy awareness and minimal loss for every path that has
A Lex-BFS-based recognition algorithm for Robinsonian matrices
Laurent, M.; Seminaroti, M.; Paschos, V.; Widmayer, P.
2015-01-01
Robinsonian matrices arise in the classical seriation problem and play an important role in many applications where unsorted similarity (or dissimilarity) information must be re- ordered. We present a new polynomial time algorithm to recognize Robinsonian matrices based on a new characterization of
A Lex-BFS-based recognition algorithm for Robinsonian matrices
M. Laurent (Monique); M. Seminaroti (Matteo); V. Paschos; P. Widmayer
2015-01-01
htmlabstractRobinsonian matrices arise in the classical seriation problem and play an important role in many applications where unsorted similarity (or dissimilarity) information must be re- ordered. We present a new polynomial time algorithm to recognize Robinsonian matrices based on a new characte
Measuring Disorientation Based on the Needleman-Wunsch Algorithm
Güyer, Tolga; Atasoy, Bilal; Somyürek, Sibel
2015-01-01
This study offers a new method to measure navigation disorientation in web based systems which is powerful learning medium for distance and open education. The Needleman-Wunsch algorithm is used to measure disorientation in a more precise manner. The process combines theoretical and applied knowledge from two previously distinct research areas,…
A Lex-BFS-based recognition algorithm for Robinsonian matrices
M. Laurent (Monique); M. Seminaroti (Matteo); V. Paschos; P. Widmayer
2015-01-01
htmlabstractRobinsonian matrices arise in the classical seriation problem and play an important role in many applications where unsorted similarity (or dissimilarity) information must be re- ordered. We present a new polynomial time algorithm to recognize Robinsonian matrices based on a new
Competition assignment problem algorithm based on Hungarian method
Institute of Scientific and Technical Information of China (English)
KONG Chao; REN Yongtai; GE Huiling; DENG Hualing
2007-01-01
Traditional Hungarian method can only solve standard assignment problems, while can not solve competition assignment problems. This article emphatically discussed the difference between standard assignment problems and competition assignment problems. The kinds of competition assignment problem algorithms based on Hungarian method and the solutions of them were studied.
Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm
Institute of Scientific and Technical Information of China (English)
Mingjian Sun; Naizhang Feng; Yi Shen; Jiangang Li; Liyong Ma; Zhenghua Wu
2011-01-01
The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that,for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore,the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.%@@ The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that,for the CS reconstruction, the desired image should have a sparse representation in a known transform domain.However, the sparsity of photoacoustic signals is destroyed because noises always exist.Therefore,the original sparse signal cannot be effectively recovered using the general reconstruction algorithm.In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic inages based on a set of noisy CS measurements.Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.
An Efficient 16-Bit Multiplier based on Booth Algorithm
Khan, M. Zamin Ali; Saleem, Hussain; Afzal, Shiraz; Naseem, Jawed
2012-11-01
Multipliers are key components of many high performance systems such as microprocessors, digital signal processors, etc. Optimizing the speed and area of the multiplier is major design issue which is usually conflicting constraint so that improving speed results mostly in bigger areas. A VHDL designed architecture based on booth multiplication algorithm is proposed which not only optimize speed but also efficient on energy use.
Reducing Ultrasonic Signal Noise by Algorithms based on Wavelet Thresholding
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M. Kreidl
2002-01-01
Full Text Available Traditional techniques for reducing ultrasonic signal noise are based on the optimum frequency of an acoustic wave, ultrasonic probe construction and low-noise electronic circuits. This paper describes signal processing methods for noise suppression using a wavelet transform. Computer simulations of the proposed testing algorithms are presented.
A Table Based Algorithm for Minimum Directed Spanning Trees
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
As far as the weighted digraph is considered, an optimal directed spanning tree algorithm called table basedalgorithm (TBA) ia proposed in the paper based on the table instead of the weighted digraph. The optimality is proved,and a numerical example is demonatrated.
A CT Image Segmentation Algorithm Based on Level Set Method
Institute of Scientific and Technical Information of China (English)
QU Jing-yi; SHI Hao-shan
2006-01-01
Level Set methods are robust and efficient numerical tools for resolving curve evolution in image segmentation. This paper proposes a new image segmentation algorithm based on Mumford-Shah module. The method is used to CT images and the experiment results demonstrate its efficiency and veracity.
A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm
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Youchuan Wan
2016-01-01
Full Text Available Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA and particle swarm optimization (PSO easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA. The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO, and artificial immune algorithm (AIA. Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.
改进免疫算法在无人机航线规划中的应用%Application of Improved Immune Algorithm in UAV Path Planning
Institute of Scientific and Technical Information of China (English)
缪永飞; 钟珞; 陈艳恩; 夏罗生
2015-01-01
Unmanned aerial vehicle ( UAV) path planning method was discussed.It's designed to establish a much more ob-jective and reasonable planned path which could blend with real digital terrain.On account of the slow convergence rate, and that immune algorithm is easily to fall into local optimum, an improved immune algorithm based on tabu criterion was proposed, and it was used to solve the UAV path planning problem.It aimed at determining the individual evaluation criteria through gene enco-ding and a series of genetic manipulation such as crossover and hyper-mutation.Through the optimization of initial track of UAV on a digital elevation map which was proposed on real geographical information.The flight path could meet various constraints. The comparative analysis with ant colony algorithm shows that the algorithm is faster and more effective to get convergent process and good solutions.%针对无人机的航线规划方法展开研究，旨在建立能够融合真实数字地形的，更为客观、合理的航迹规划方法。由于免疫算法易陷入局部最优点及收敛速度过慢等问题，提出了一种基于禁忌准则的改进免疫算法，并应用于无人机航迹规划，其通过基因编码确定个体评价准则、交叉和高频变异等操作，通过在真实的地理环境信息所建立的数字高程地图上进行无人机的初始航迹优化，使航迹能够满足各种约束条件。与蚁群算法对比分析的结果表明，该算法加快了收敛进程，并可求得较优解。
ITO-based evolutionary algorithm to solve traveling salesman problem
Dong, Wenyong; Sheng, Kang; Yang, Chuanhua; Yi, Yunfei
2014-03-01
In this paper, a ITO algorithm inspired by ITO stochastic process is proposed for Traveling Salesmen Problems (TSP), so far, many meta-heuristic methods have been successfully applied to TSP, however, as a member of them, ITO needs further demonstration for TSP. So starting from designing the key operators, which include the move operator, wave operator, etc, the method based on ITO for TSP is presented, and moreover, the ITO algorithm performance under different parameter sets and the maintenance of population diversity information are also studied.
Application layer multicast routing solution based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
Peng CHENG; Qiufeng WU; Qionghai DAI
2009-01-01
Application layer multicast routing is a multi-objective optimization problem.Three routing con-straints,tree's cost,tree's balance and network layer load distribution are analyzed in this paper.The three fitness functions are used to evaluate a multicast tree on the three indexes respectively and one general fitness function is generated.A novel approach based on genetic algorithms is proposed.Numerical simulations show that,compared with geometrical routing rules,the proposed algorithm improve all three indexes,especially on cost and network layer load distribution indexes.
Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network
Institute of Scientific and Technical Information of China (English)
WANG Zhenfei; ZHAI Guangqun; WANG Nengchao
2006-01-01
An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system (HVS) characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.
Image fusion based on expectation maximization algorithm and steerable pyramid
Institute of Scientific and Technical Information of China (English)
Gang Liu(刘刚); Zhongliang Jing(敬忠良); Shaoyuan Sun(孙韶媛); Jianxun Li(李建勋); Zhenhua Li(李振华); Henry Leung
2004-01-01
In this paper, a novel image fusion method based on the expectation maximization (EM) algorithm and steerable pyramid is proposed. The registered images are first decomposed by using steerable pyramid.The EM algorithm is used to fuse the image components in the low frequency band. The selection method involving the informative importance measure is applied to those in the high frequency band. The final fused image is then computed by taking the inverse transform on the composite coefficient representations.Experimental results show that the proposed method outperforms conventional image fusion methods.
Community Structure Detection Algorithm Based on the Node Belonging Degree
Directory of Open Access Journals (Sweden)
Jian Li
2013-07-01
Full Text Available In this paper, we propose a novel algorithm to identify communities in complex networks based on the node belonging degree. First, we give the concept of the node belonging degree, and then determine whether a node belongs to a community or not according to the belonging degree of the node with respect to the community. The experiment results of three real-world networks: a network with three communities with 19 nodes, Zachary Karate Club and network of American college football teams show that the proposed algorithm has satisfactory community structure detection.