A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Security constrained economic dispatch considering wind energy conversion systems

The available wind power is stochastic and requires appropriate tools in the OPF model for economic and reliable power system operation. This paper exhibit the OPF formulation with factors involved in the intermittency of wind power. Weibull distribution is adopted to find the stochastic wind speed and power distribution. The reserve requirement is evaluated based on the wind distribution and risk of under/over estimation of the wind power. In addition, the Wind Energy Conversion System (WECS) is represented by Doubly Fed Induction Generator (DFIG) based wind farms. The reactive power capability for DFIG based wind farm is also analyzed. The study is performed on IEEE-30 bus system with wind farm located at different buses and with different wind profiles. Also the reactive power capacity to be installed in the wind farm to maintain a satisfactory voltage profile under the various wind flow scenario is demonstrated.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Bacterial algorithm applied for fuzzy rule extraction

This paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.

Coordinated tuning of DFIG-based wind turbines and batteries using bacteria foraging technique for maintaining constant grid power output

This paper proposes the use of battery energy storage (BES) system for the grid-connected doubly fed induction generator (DFIG). The BES would help in storing/releasing additional power in case of higher/lower wind speed to maintain constant grid power. The DC link capacitor is replaced with the BES system in a DFIG-based wind turbine to achieve the above-mentioned goal. The control scheme is modified and the co-ordinated tuning of the associated controllers to enhance the damping of the oscillatory modes is presented using bacterial foraging technique. The results from eigenvalue analysis and the time domain simulation studies are presented to elucidate the effectiveness of the BES systems in maintaining the grid stability under normal operation.

Fundamentos de otimização por inteligência de enxames: uma visão geral

Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos bioinspirados baseados no comportamento de populações para o desenvolvimento de técnicas de solução de problemas. As metaheurísticas tratadas aqui correspondem às estratégias de otimização por colônia de formigas, otimização por enxame de partículas, algoritmo shuffled frog-leaping, coleta de alimentos por bactérias e colônia de abelhas. Os princípios biológicos que motivaram o desenvolvimento de cada uma dessas estratégias, assim como seus respectivos algoritmos computacionais, são introduzidos. Duas aplicações diferentes foram conduzidas para exemplificar o desempenho de tais algoritmos. A finalidade é enfatizar perspectivas de aplicação destas abordagens em diferentes problemas da área de engenharia.

Improving stability of a DFIG-based wind power system with tuned damping controller

This paper focuses on the implementation of a damping controller for the doubly fed induction generator (DFIG) system. Coordinated tuning of the damping controller to enhance the damping of the oscillatory modes is presented using bacterial foraging technique. The effect of the tuned damping controller on converter ratings of the DFIG system is also investigated. The results of both eigenvalue analysis and the time-domain simulation studies are presented to elucidate the effectiveness of the tuned damping controller in the DFIG system. The improvement of the fault ride-through capability of the system is also demonstrated.

RFID系统优化模型与智能算法研究

射频识别技术（Radio Frequency Identification, RFID）作为采集与处理信息的高新技术和信息化标准的基础，被列为本世纪十大重要技术之一。但是，RFID技术的大规模实际应用仍处于探索阶段，RFID系统的应用基础技术还存在着大量尚未解决的关键问题，其中RFID系统优化是RFID技术研究和应用的重要课题。由于RFID系统本身的动态性和不确定性， RFID系统优化面对的一般是非线性、多目标、大规模的复杂优化问题，传统的数学优化算法在处理这些问题时，存在困难。为此，研究新的优化算法成为RFID技术实际应用和理论研究中必须解决的课题。 智能计算方法是求解复杂RFID系统优化问题的一种可供选择的算法。智能计算作为一个新兴领域，其发展已引起了多个学科领域研究人员的关注，目前已经成为人工智能、经济、社会、生物等交叉学科的研究热点和前沿领域。智能计算的各类算法已在传统NP问题求解及诸多实际应用领域中展现出其优异的性能和巨大的发展潜力。 本文旨在对RFID系统的各种优化问题进行深入研究和探讨，面向RFID技术的实际应用需求构建其优化模型，并基于智能计算思想设计能够有效求解这些复杂模型的新型智能优化算法。具体研究内容包括： 首先，进行了RFID读写器网络的调度问题研究。在深入分析RFID网络中读写器冲突类型和成因的基础上，考虑RFID网络中的读写器冲突约束，以最小化系统中的频道数量、时隙分配以及总处理时间建立了RFID读写器网络调度的数学优化模型。从生物学的角度出发提出基于生态捕食模型的改进PSO算法（Particle Swarm Optimizer based on Predator-prey Coevolution, PSOPC），在一定程度上解决了PSO算法在迭代后期随着多样性丧失而陷入局部最优的缺点。应用PSOPC设计了求解RFID读写器网络调度模型的智能求解算法，分别给出算法的求解框架、关键步骤的实现机制。通过在不同规模的RFID读写器网络上进行实例仿真，验证了算法的有效性和模型的正确性。 其次，进行了基于菌群自适应觅食算法RFID网络规划问题的研究。考虑RFID系统在不同应用环境下的系统需求，建立了RFID网络规化的数学模型，其目标函数分别为：RFID网络标签覆盖率的最大化目标函数、RFID读写器冲突的最小化目标函数、RFID网络运行的经济效益最大化目标函数、RFID网络运行的负载平衡目标函数以及同时考虑全局目标的混合目标函数。将自然界生物觅食所采用的自适应搜索策略与细菌的趋化行为和群体感应机制相集成，提出了适合求解复杂RFID网络规划问题的菌群自适应觅食算法（Adaptive Bacterial Foraging Optimization, ABFO）。通过仿真实验基于ABFO算法分别对RFID网络规划模型中的五个目标函数进行了实例求解和分析，测试结果与标准PSO算法和遗传算法进行了比较分析。 再次，进行了基于系统智能方法的RFID网络规划分布式决策模型研究。采用分布式决策的思想建立了RFID网络规划的层次模型，在一定程度上缓解、分散了RFID网络规划问题的复杂性，以解决具有混合变量（包括离散变量和连续变量）的多目标RFID网络规划问题。针对层次模型求解的复杂性，以复杂适应系统理论为指导思想设计了一种新型系统智能优化算法对RFID网络规划的层次模型进行求解。系统智能算法将群体智能中的单层群体系统概念扩展为多层涌现系统，仿真实验表明新提出的算法显著提高了智能计算方法的寻优能力，以及算法的适应性、鲁棒性和平衡性等性能。 最后，进行了RFID网络目标跟踪系统中的数据融合研究。以基于RFID技术的目标定位与跟踪系统为应用背景，提出了基于模糊聚类方法的多RFID读写器数据融合模型框架。通过深入分析蜜蜂采蜜的基本生物学规律，对蜜蜂的个体行为及群体行为进行模拟，提出了一类新型群体智能优化算法-蜂群优化算法（Bee Swarm Optimization, BSO），并将BSO算法嵌入RFID目标定位跟踪系统，作为其模糊聚类的基本算法。仿真研究表明，提出的融合模型能够有效的过滤读写器对跟踪目标的错误监测数据，显著提高目标定位与跟踪的精度。

Marine Microbial Enzymes

3.4. Lipase (EC-3.1. 1.3) 3.5. Other Known Enzymes 3.6. Extremozymes (Enzymes from extremophiles) 3.7. Recognition of Valuable Extremozymes 4. Enzymes as Tools in Biotechnology 4.1. Restriction Enzymes from Marine Bacteria 4.2. Other Nucleases from Marine Bacteria 4.3. Bacteriolytic Enzyme by Bacteriophage from Seawater 5. Innovations in Enzyme Technology 5.1. Enzyme Engineering 5.2. Immobilization Technology 5.3. Gene Cloning for Marine Enzymes 6. Future Prospects

Métodos de otimização aplicados no ajuste de ESPS e controladores de amortecimento inseridos no FACTS TCSC em sistemas elétricos de potência

Pós-graduação em Engenharia Elétrica - FEIS

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

Background:Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement. Methodology/Principal Findings: Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5'-ends of these six Northern-supported sRNA candidates were successfully mapped using 5'-RACE analysis. Conclusions/Significance: We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that similar to 40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.

High-quality annotation of promoter regions for 913 bacterial genomes

Motivation: The number of bacterial genomes being sequenced is increasing very rapidly and hence, it is crucial to have procedures for rapid and reliable annotation of their functional elements such as promoter regions, which control the expression of each gene or each transcription unit of the genome. The present work addresses this requirement and presents a generic method applicable across organisms. Results: Relative stability of the DNA double helical sequences has been used to discriminate promoter regions from non-promoter regions. Based on the difference in stability between neighboring regions, an algorithm has been implemented to predict promoter regions on a large scale over 913 microbial genome sequences. The average free energy values for the promoter regions as well as their downstream regions are found to differ, depending on their GC content. Threshold values to identify promoter regions have been derived using sequences flanking a subset of translation start sites from all microbial genomes and then used to predict promoters over the complete genome sequences. An average recall value of 72% (which indicates the percentage of protein and RNA coding genes with predicted promoter regions assigned to them) and precision of 56% is achieved over the 913 microbial genome dataset.

Autocalibration of dynamic bacterial growth model for water distribution system using GA

The presence of residual chlorine and organic matter govern the bacterial regrowth within a water distribution system. The bacterial growth model is essential to predict the spatial and temporal variation of all these substances throughout the system. The parameters governing the bacterial growth and biodegradable dissolved organic carbon (BDOC) utilization are difficult to determine by experimentation. In the present study, the estimation of these parameters is addressed by using simulation-optimization procedure. The optimal solution by genetic algorithm (GA) has indicated that the proper combination of parameter values are significant rather than correct individual values. The applicability of the model is illustrated using synthetic data generated by introducing noise in to the error-free measurements. The GA was found to be a potential tool in estimating the parameters controlling the bacterial growth and BDOC utilization. Further, the GA was also used for evaluating the sensitivity issues relating parameter values and objective function. It was observed that mu and k(cl) are more significant and dominating compared to the other parameters. But the magnitude of the parameters is also an important issue in deciding the dominance of a particular parameter. GA is found to be a useful tool in autocalibration of bacterial growth model and a sensitivity study of parameters.