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Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference

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SENSORS 2019年第4期19卷 837-837页

作者： Liu, Hanbing He, Xin Jiao, Yubo Wang, Xirui Jilin Univ Coll Transportat Changchun 130025 Peoples R China Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China

Structural health monitoring (SHM) has been widely used in all kinds of bridges. It is significant to accurately assess the serviceability and reliability of bridge subjected to severe conditions by SHM technique. Bridge deflection as an essential evaluation index can reflect structural condition perfectly. In this study, an approach for deflection calculation and reliability assessment of simply supported bridge is presented. Firstly, a bridge deflection calculation method is proposed based on modal flexibility and Kriging method improved by artificial bee colony algorithm. Secondly, a dynamic Bayesian network is employed to evaluate the deflection reliability combined with monitoring results which include modal frequency, mode shape, environmental temperature, and humidity. A linear regression model is established to analyze the relationship between modal parameters and environmental factors. Thirdly, a simply supported bridge is constructed and monitored to verify the effectiveness of the proposed method. The results reveal that the proposed method can precisely calculate the bridge deflection. Finally, the time-dependent reliabilities of two cases are computed and the effects of monitoring factors on bridge deflection reliability are analyzed by sensitivity parameter. It indicates that the reliability is negatively correlated with temperature and more sensitive to mode shape than other three factors.

关键词： structural reliability bridge deflection structural health monitoring modal flexibility dynamic Bayesian network Kriging model artificial bee colony algorithm

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A new method for skull stripping in brain MRI using multistable cellular neural networks

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NEURAL COMPUTING & APPLICATIONS 2018年第8期29卷 79-95页

作者： Yilmaz, Burak Durdu, Akif Emlik, Ganime Dilek Selcuk Univ Dept Elect & Elect Engn Konya Turkey Necmettin Erbakan Univ Meram Med Fac Dept Radiol Konya Turkey

This study proposes a new method on "detecting brain region in MRI data". This task is generally named as "skull stripping" in the literature. The algorithm is developed by using the cellular neural networks (CNNs) and multistable CNN structures. It also includes a contrast enhancement and noise reduction algorithm. The algorithm is named as multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). Three different case studies are performed for measuring the success of the algorithm. Also a fourth case study is performed to evaluate the supporting algorithm, the CEULICA. First two evaluations are performed by using well-known MIDAS-NAMIC and Brainweb databases, which are properly organized Talairach-compatible databases. The third database was obtained from the research and application hospital of Necmettin Erbakan University Meram Faculty of Medicine. These MRI data were not Talairach-compatible and less sampled. The algorithm achieved 0.595 Jaccard, 0.744 Dice, 0.0344 TPF and 0.383 TNF mean values with the Brainweb T1-weighted images and 0.837 Jaccard, 0.898 Dice, 0.0124 TPF and 0.1511 TNF mean values with the MIDAS-NAMIC T2-weighted images. The algorithm achieved 0.8297 Jaccard, 0.9012 Dice, 0.0951 TPF and 0.1225 TNF mean values and achieved with the obtained data the best values among the other algorithms. As a result, it can be claimed that algorithm performs best with the non-Talairach-compatible MRI data due to its nature of performing at cellular level.

关键词： Skull stripping Brain MRI CNN Multistable CNN Contrast enhancement artificial bee colony algorithm MRI Biomedical image processing

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Hybrid Optimization algorithm for Bayesian Network Structure Learning

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INFORMATION 2019年第10期10卷

作者： Sun, Xingping Chen, Chang Wang, Lu Kang, Hongwei Shen, Yong Chen, Qingyi Yunnan Univ Software Sch Kunming 650091 Yunnan Peoples R China

Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.

关键词： Bayesian network structure learning artificial bee colony algorithm

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Linearization of High Power Amplifier Using Modified artificial bee colony and Particle Swarm Optimization algorithm

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Procedia Technology 2016年 25卷 28-35页

作者： P.R. Bipin P.V. Rao Research scholar VTU belgaum India RajaRajesweri college of Engineering Bengaluru India

A linearization method for power amplifiers is proposed to cancel out distortion induced during amplification. Wiener model is chosen for power amplifier modelling. The linearization is achieved by the Modified artificial bee colony (ABC) and Particle Swarm Optimization (PSO) algorithm. The Modified ABC and PSO algorithm reduces the complexity in linearization. Experimental results obtained for the proposed method shows that a better linearization of the amplifier characteristics can be obtained using the proposed method.

关键词： Optimization artificial bee colony algorithm Particle Swarm Optimization Wiener model

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Swarm intelligence-based energy-efficient data delivery in WSAN to virtualise IoT in smart cities

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IET WIRELESS SENSOR SYSTEMS 2018年第6期8卷 256-259页

作者： Manshahia, Mukhdeep Singh Punjabi Univ Dept Math Patiala Punjab India

With the rapid implementation and expansion of the Internet of things (IoT) technologies in smart cities, network congestion control and energy-efficient routing in wireless sensor and actuator networks (WSANs) for virtualising IoT have emerged as a crucial area of research in recent years. This study implements the particle swarm optimisation (PSO) algorithm to realise network congestion control and energy-efficient routing in the transport layer of WSANs. This algorithm is based on the flocking behaviour of birds. The simulation results show that the proposed PSO-based approach provides better performance in terms of network lifetime and packet drop ratio compared with the ant colony optimisation and the artificial bee colony algorithm.

关键词： particle swarm optimisation Communication network design, planning and routing WSAN telecommunication power management telecommunication network routing Optimisation techniques artificial bee colony algorithm telecommunication congestion control Control applications in data transmission Computer networks and techniques energy conservation Wireless sensor networks particle swarm optimisation algorithm IoT virtualisation Computer communications energy-efficient routing packet drop ratio network congestion control swarm intelligence-based energy-efficient data delivery Internet of Things technologies wireless sensor and actuator networks smart cities Control applications in radio and radar PSO-based approach wireless sensor networks actuators Internet of Things ant colony optimisation Expert systems and other AI software and techniques network lifetime swarm intelligence

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Multi-section-Oriented Robust algorithm for Unconstrained Global Optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 7315-7342页

作者： Jabeen, Syeda Darakhshan Indian Inst Technol Kanpur Kanpur 208016 Uttar Pradesh India Birla Inst Technol Dept Math Ranchi 835215 Bihar India

We develop a new hybrid method called multi-section-oriented artificial bee colony MSO-ABC algorithm, to solve complex unconstrained optimization problems. The method initially decomposes the search region into finite collection of subregions in which the best interval value of the objective function is found using ABC algorithm. ABC algorithm uses chaotic maps to express parameter adaption to improve the search performance and escape from local optima. Then, interval ranking rule defined with respect to the optimistic decision makers' point of view is applied to find most promising subregion containing best objective value for further division and non-promising region(s) are discarded. The technique narrows the search region by recursive decomposition and enhances computational effort toward better solution. The process is repeated reducing the search space consecutively. The global/ close to global values are obtained in the form of an interval with negligible width. Validity of MSO-ABC is tested on benchmark functions followed by the non-parametric tests viz., Friedman and Wilcoxon rank test for assessing statistical significance of the proposed method against other well-established optimization methods.

关键词： Interval analysis Heuristic Global optimization artificial bee colony algorithm Unconstrained optimization

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ADAPTIVE IMAGE CONTRAST ENHANCEMENT USING artificial bee colony OPTIMIZATION 24

ADAPTIVE IMAGE CONTRAST ENHANCEMENT USING ARTIFICIAL BEE COL...

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24th IEEE International Conference on Image Processing (ICIP)

作者： Chen, Jia Yu, Weiyu Tian, Jing Chen, Li South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430081 Hubei Peoples R China

ISBN: (纸本)9781509021758

The objective of image contrast enhancement is to improve the contrast level of images, which are degraded during image acquisition. Image contrast enhancement is considered as an optimization problem in this paper and the artificial bee colony (ABC) algorithm is utilized to find the optimal solution for this optimization problem. The contribution of the proposed approach is two-fold. First, in view of that the fitness function is indispensable to evaluate the quality of the enhanced image, a new multiple objective fitness function is proposed in this paper. Second, the image transformation function is critical to generate new pixel intensities for the enhanced image from the original input image;more importantly, it guides the searching movements of the artificial bees. For that, a parametric image transformation function is utilized in this paper so that only the optimal parameters used in the transformation function need to be searched by the ABC algorithm. This is in contrast to that the whole space of image intensity levels is used in the conventional ABC-based image enhancement approaches. Extensive experiments are conducted to demonstrate that the proposed approach outperforms conventional image contrast enhancement approaches to achieve both better visual image quality and higher objective performance measures.

关键词： Image contrast enhancement artificial bee colony algorithm

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The Effect of the Increase in the Number of Channels to the Performance in the Cosine Modulated Filter Bank Design 26

The Effect of the Increase in the Number of Channels to the ...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Ozdemir, Gokcen Karaboga, Nurhan Erciyes Univ Elekt Elekt Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781538615010

In this study, M-Channel Uniform Cosine Modulated Filter Bank is designed. The prototype filter of the filter bank is optimized using the artificial bee colony (ABC) algorithm. For different numbers of sub-channels, the filter bank's stopband attenuation, stopband error and mean square error values are calculated. The effect of the increase in the sub-channel number on these parameters is examined. As the number of channels increases, the value of the stop band error increases, and the mean square error value improves.

关键词： Cosine modulated filter bank number of channel stopband attenuation artificial bee colony algorithm

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Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach

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ADVANCES IN MECHANICAL ENGINEERING 2019年第3期11卷

作者： Zhang, Libin Zhang, Lele Shan, Hongying Jilin Univ Transportat Coll Changchun Jilin Peoples R China Jilin Univ Sch Mech Sci & Engn Changchun 130022 Jilin Peoples R China

Maintenance plays a crucial role in the entire life cycle of equipment. With the acceleration of industrialization, the evaluation of equipment maintenance quality has undoubtedly become more challenging due to the complex mechanical structure, various maintenance modes, and so on. In order to make decisions scientifically, a hybrid multi-criteria decision-making approach integrating triangle fuzzy number, lambda-fuzzy measure, TOPSIS, and Choquet fuzzy integral is proposed in this article. First, the interaction among criteria can be handled reasonably by fuzzy integral based on lambda-fuzzy-measure. Second, fuzzy numbers which are given by experts are applied to deal with fuzzy linguistic value. In addition, artificial bee colony algorithm is first introduced to identify lambda-fuzzy-measure. The comparison results of three optimization algorithms which include artificial bee colony algorithm, genetic algorithm, and particle swarm optimization prove artificial bee colony algorithm is more effective than genetic algorithm and particle swarm optimization. A case study which contains six maintenance alternatives is practiced to prove the effectiveness of the proposed hybrid multi-criteria decision-making approach. Finally, the comparison is made between the proposed method and two classical multi-criteria decision-making approaches which refer to TOPSIS and gray correlation, and the results demonstrate the proposed method is suitable to solve maintenance quality evaluation problem.

关键词： Maintenance quality fuzzy number lambda-fuzzy measure Choquet fuzzy integral artificial bee colony algorithm TOPSIS multi-criteria decision-making approach optimization

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Classification and Clustering of Human Sperm Swimming Patterns 3

Classification and Clustering of Human Sperm Swimming Patter...

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IEEE 3rd International Conference on Signal and Image Processing (ICSIP)

作者： Choi, Ji-won Wang, Chizhong Urbano, Leonardo F. Masson, Puneet VerMilyea, Matthew Kam, Moshe New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA 70 Beharrell St Concord MA 01742 USA Hosp Univ Penn Penn Fertil Care Philadelphia PA 19104 USA Ovat Fertil Austin TX 78731 USA

ISBN: (纸本)9781538663967

The principal observed progressive swim types of sperm cells are linear mean and circular swim. Using motility characteristic parameters produced by CASA systems, we perform a parameter subset search to produce distinct clusters of the different swim types. For this task, the artificial bee colony algorithm (an iterative search algorithm modeled after the collective behavior of bees) and the well-studied k-means clustering algorithm were used on simulated and human sperm swim data. The result is distinct clusters with features of each types of swim. The clustering approach displays potential as a tool for automated sperm swim subpopulation analysis.

关键词： Clustering artificial bee colony algorithm sperm imaging particle tracking sperm motility

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