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2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)最新文献

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Self-Branching Competitive Learning for image segmentation 基于自分支竞争学习的图像分割
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645201
T. Guan, Ling-Ling Li
{"title":"Self-Branching Competitive Learning for image segmentation","authors":"T. Guan, Ling-Ling Li","doi":"10.1109/BICTA.2010.5645201","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645201","url":null,"abstract":"This paper proposes an online competitive learning paradigm, Self-Branching Competitive Learning(SBCL), which uses K-Nearest Neighborhood(KNN) and iterative variance estimation for clustering analysis. SBCL adopts the incremental learning strategy, starts clustering data from one initial prototype and then branches if the bias between vectors is larger than the pre-specified scale. SBCL is unrelated to initial cluster number or data distribution, avoids the dead node problem and suits to analyze the online input data. We apply SBCL to two classical problems: clustering data with mixed Gaussian distributions and segmenting MRI images. The experimental results shew that SBCL has good performance in these problems.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"367 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114528966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Unsupervised feature selection based on clustering 基于聚类的无监督特征选择
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645319
Sheng-yi Jiang, Lian-xi Wang
{"title":"Unsupervised feature selection based on clustering","authors":"Sheng-yi Jiang, Lian-xi Wang","doi":"10.1109/BICTA.2010.5645319","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645319","url":null,"abstract":"Feature selection plays an important part in improving the classification accuracy and the quality of clustering in many applications. Feature selection has been widely studied in supervised learning, but in unsupervised learning it is still relatively rare. In this paper, a novel definition of feature differentiation for identifying (determining) the relatively important features is presented, and a one-pass clustering-based feature selection approach is introduced. The new method with nearly linear time complexity selects the optimal subset according to the variation of the feature differentiation. Experimental results on UCI datasets show that our method, by removing the irrelevant or redundant features, can achieve promising classification and clustering results for most datasets. Compared with other traditional feature selection approaches the proposed algorithm has obtained similar or even better performance in terms of dimensionality reduction and classification accuracy.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117198868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Feedback control of chemotherapy drug scheduling for phase specific cancer treatment 肿瘤分期化疗药物调度的反馈控制
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645283
S. Algoul, M. S. Alam, M. A. Hossain, M. Majumder
{"title":"Feedback control of chemotherapy drug scheduling for phase specific cancer treatment","authors":"S. Algoul, M. S. Alam, M. A. Hossain, M. Majumder","doi":"10.1109/BICTA.2010.5645283","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645283","url":null,"abstract":"This paper presents a novel method of chemotherapy drug scheduling for cancer treatment using feedback control and genetic algorithm (GA). The main aim of chemotherapy treatment is to eradicate the tumour, if possible, or to reduce the tumour size to a minimum level with minimum toxic side effects. A feedback control method is developed in order to maintain a predefined level of drug concentration at tumour sites. The reference to the controller is chosen in such a way as to limit the drug concentration in the plasma which in turn limits the toxic side effects. A variant of Proportional-Integral-Derivative (PID) control, namely I-PD is used to control the drug to be infused to the patient's body. A phase specific cancer tumour model is developed and used for this work. The model, initially proposed by Martin [1], describes the effects of drug on different cell populations, plasma drug concentration and toxic side effects. The output of the I-PD control, which is chemotherapy drug dose, is applied to the model to observe its effects. Moreover, GA is used to optimise the parameters of the controller that in turn improves the drug scheduling as well as the effectiveness of the proposed approach. Results show that our method can reduce the tumour size significantly at the end of the treatment. Furthermore, the toxic side effects are always remained very low throughout the whole period. A comparative assessment is also provided to highlight the novelty of the proposed technique. It is noted that the proposed model offers best performance as compared to any reported models.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117313084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
A metric algorithm based on three elements of texture visual feature 一种基于纹理视觉特征三要素的度量算法
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645151
Zhao Ying, X. Mei, Sun Yu
{"title":"A metric algorithm based on three elements of texture visual feature","authors":"Zhao Ying, X. Mei, Sun Yu","doi":"10.1109/BICTA.2010.5645151","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645151","url":null,"abstract":"In order to construct a reference model to recognize image texture, a combining method based on three elements of texture visual features is proposed. Firstly, a fractal model is used to calculate the fractal dimension which is a measure of image textural coarseness. Secondly, a global texture direction is proposed. Gabor filter and local marginal probability histogram is used to calculate a quantitative value of texture direction. Thirdly, the texture contrast base on Tamura model is applied to describe image texture feature. Finally, the combined method based on the coarseness, the direction and the contrast is applied to extract texture visual features in Brodatz texture database. The experimental result is consistent with human visual perception. The algorithm can be better reference model to satisfy machine identification image texture.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123429073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Cooperative coevolutionary genetic algorithms to find optimal elimination orderings for Bayesian networks 寻找贝叶斯网络最优消除顺序的协同进化遗传算法
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645605
Xuchu Dong, Haihong Yu, D. Ouyang, Dianbo Cai, Yuxin Ye, Yonggang Zhang
{"title":"Cooperative coevolutionary genetic algorithms to find optimal elimination orderings for Bayesian networks","authors":"Xuchu Dong, Haihong Yu, D. Ouyang, Dianbo Cai, Yuxin Ye, Yonggang Zhang","doi":"10.1109/BICTA.2010.5645605","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645605","url":null,"abstract":"According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these algorithms are more robust than other existing swarm intelligence methods when solving the elimination ordering problem.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125064559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs 用fpga并行实现加速洗牌青蛙跳跃算法
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645270
Daniel M. Muñoz Arboleda, C. Llanos, L. Coelho, M. Ayala-Rincón
{"title":"Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs","authors":"Daniel M. Muñoz Arboleda, C. Llanos, L. Coelho, M. Ayala-Rincón","doi":"10.1109/BICTA.2010.5645270","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645270","url":null,"abstract":"Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide suboptimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field Programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"387 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Constructing and analyzing folksonomy in online social bookmarking 网络社交书签中的民俗分类法构建与分析
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645104
Zhongming Han, Qian Mo, Yanjun Liu, Min Zuo
{"title":"Constructing and analyzing folksonomy in online social bookmarking","authors":"Zhongming Han, Qian Mo, Yanjun Liu, Min Zuo","doi":"10.1109/BICTA.2010.5645104","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645104","url":null,"abstract":"Research of constructing folksonomy in online social bookmarking system has attracted much attention recently. In this paper, we analyzed problems of constructing methods based on tag co-occurrence and proposed an entropy-based method to measure relationship of tag. Furthermore, a adjust threshold algorithm is proposed. Finally, comprehensive experiments are conducted and the result show our method can effectively construct folksonomy.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122107660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ANN based three-value logic SVPWM control in CSR 基于神经网络的CSR三值逻辑SVPWM控制
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645081
Jinbang Xu, A. Shen, Zhizhuo Wu, Jun Yang, Xuan Yang
{"title":"ANN based three-value logic SVPWM control in CSR","authors":"Jinbang Xu, A. Shen, Zhizhuo Wu, Jun Yang, Xuan Yang","doi":"10.1109/BICTA.2010.5645081","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645081","url":null,"abstract":"To achieve better performance with various load and system parameters in controlling a current-source rectifier (CSR) with less computing cost, a neural-network-based implementation of three-logic space-vector modulation (SVM) is proposed in this research, and the random weight change (RWC)algorithm is employed for on-line parameter tuning. The scheme has been simulated in SABER simulation software and the result is compared with the conventional SVM method. The advantage of the method is explicit with a better performance under a non-rated system load.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial bee colony algorithm for solving multiple sequence alignment 求解多序列比对的人工蜂群算法
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645304
Xiu-juan Lei, Jingjing Sun, Xiaojun Xu, Ling Guo
{"title":"Artificial bee colony algorithm for solving multiple sequence alignment","authors":"Xiu-juan Lei, Jingjing Sun, Xiaojun Xu, Ling Guo","doi":"10.1109/BICTA.2010.5645304","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645304","url":null,"abstract":"In this paper, an artificial bee colony (ABC) algorithm for the multiple sequence alignment (MSA) problem has been proposed. The ABC algorithm is a novel optimization approach inspired by a particular intelligent behaviour of honey bee swarms. Taken the discreteness of the MSA problem into consideration, a new method of ABC algorithm for determining a food source in the neighbourhood is introduced. The performance of our ABC approach is compared with other commonly used algorithms for MSA. Computational results demonstrate the superiority of the new ABC algorithm over genetic algorithm (GA) and particle swarm optimization (PSO) for many sequences with different length and identity. The new approach is more robust and obtains better mathematical and biological quality.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123950343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 23
Identification system of the type of vehicle 车辆类型识别系统
2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645260
B. Daya, A. Akoum, P. Chauvet
{"title":"Identification system of the type of vehicle","authors":"B. Daya, A. Akoum, P. Chauvet","doi":"10.1109/BICTA.2010.5645260","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645260","url":null,"abstract":"The identification of objects is a difficult task because the objects of the real-world are highly variable in aspect, size, color, position in space, etc. The system of identification of object must thus have a very great adaptability. In this article we present a system of identification of the type (model) of vehicles per vision. Several geometrical parameters (distance, surface, ratio … ) of decision, on bases of images taken in real conditions, were tested and analyzed. According to these parameters, the rate of identification can reach 95% on a basis of images made up of 9 classes of the type of vehicles. The fusion of the three classifiers using the rate of identification for each parameter allows showing the effectiveness of our process for the identification of the type of vehicle.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124120804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
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