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Application of Smartphone and Model Updating Technique in Structural Health Monitoring

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019年第5期44卷 4819-4828页

作者： Dey, Palash Akhil, V Laskar, A., I Natl Inst Technol Silchar Dept Civil Engn Silchar 788010 Assam India

The present study outlines the application of smartphone and model updating in structural health monitoring by using vibration response data. The application of the approach has been shown for simply supported beams with uncracked and different crack configurations. Crack has been artificially generated at the time of casting using thin steel plate of 1.5 mm thickness. Finite element analyses have been carried out for uncracked and cracked beams. Laboratory tests were conducted using smartphone to obtain the natural frequencies of both damaged and undamaged beams with the help of accelerometer application software. Acceleration in the vertical direction of the cracked and uncracked beams was measured using this mobile application and thereafter, frequency response function curves have been obtained from this. At last, the model has been updated using bees algorithm to get more accurate results.

关键词： Model updating Structural health monitoring bees algorithm Noise contamination and smartphone

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Reducing the Overfitting in Convolutional Neural Network using Nature-Inspired algorithm: A Novel Hybrid Approach

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 13099-13114页

作者： Alamri, Nawaf Mohammad King Abdulaziz Univ Dept Ind Engn Jeddah Saudi Arabia

Convolutional neural network (CNN) is one of the well-known deep learning algorithms that uses convolutional filters to extract the features in the images. The most important issue when training CNN is the overfitting which prevents the model from generalization to unseen data. This paper addressed this issue by proposing a novel hybrid approach that uses bees algorithm (BA) to optimize the regularization parameter and weight regularization factor to adjust the regularization value in each convolutional layer and fully connected layer resulting in a hybrid algorithm called bees algorithm regularized convolutional neural network (BA-RCNN). It was applied to three different datasets for classification or predictions purposes and showed an improvement in the validation and testing accuracy leading to a lower difference with the training accuracy which means the overfitting is reduced comparing to the original CNN. Applying the BA-RCNN algorithm to 'Cifar10DataDir' improved the validation accuracy from 80.34% for the original CNN to 82.80% for the hybrid BA-RCNN algorithm, in the electrocardiogram the improvement was from 87.80 to 90.47% and both datasets were used for classification. Furthermore, the hybrid BA-RCNN algorithm was applied to predict the porosity percentage based on artificial porosity images and the results showed that the validation accuracy was improved from 81.67% for the original CNN to 87.33% for the hybrid BA-RCNN algorithm.

关键词： Deep learning Convolutional neural network bees algorithm Overfitting bees algorithm regularized convolutional neural network (BA-RCNN)

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Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

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SN APPLIED SCIENCES 2020年第2期2卷 1-57页

作者： Ezugwu, Absalom E. Univ KwaZulu Natal Sch Comp Sci King Edward RdPietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be

关键词： Automatic clustering Clustering analysis Differential evolution Particle swarm optimization Firefly algorithm Invasive weed optimization Artificial bee colony bees algorithm Biogeography-based optimization Harmony search Symbiotic organisms search Teaching-learning-based optimization DB validity index CS validity index

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Meta-Heuristic Tuning of the LQR Weighting Matrices Using Various Objective Functions on an Experimental Flexible Arm Under the Effects of Disturbance

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第8期46卷 7323-7336页

作者： Bilgic, Hasan Huseyin Sen, Muhammed Arif Yapici, Ahmet Yavuz, Hakan Kalyoncu, Mete Iskenderun Tech Univ Fac Engn & Nat Sci Dept Mech Engn TR-31200 Iskenderun Hatay Turkey Konya Tech Univ Dept Mech Engn Konya Turkey Cukurova Univ Dept Mech Engn Adana Turkey

In this paper, meta-heuristic tuning of LQR weighting matrices using various objective functions on the experimental flexible arm under the effects of disturbance is investigated. The use of flexible and lightweight systems provides certain advantages such as high operating speeds, low electricity consumption, and low initial investment costs. However, such systems are more prone to vibration-related problems than their rigid and heavyweight counterparts. One of the closed-loop control methods used to suppress these vibrations is LQR control, but the success of the controller depends on the choice of the gain and regulator matrices. In this study, the bees algorithm, a meta-heuristic search algorithm, is used to determine LQR matrices. The system performance is compared with similar studies in the literature. The proposed objective function reveals an improvement of 3.1% in rotor maximum overshoot compared to studies in the literature. Flexible link maximum overshoot has been reduced from 17.8 to 7.1%. In order to validate the applicability and repeatability of the proposed method under the noise and disturbance in real systems, experimental verification tests were conducted using the Quanser Flexible Link system. As a result, the proposed method has been experimentally validated and it is estimated that it may well be a very useful controller design approach in various engineering systems and related control system developments.

关键词： bees algorithm LQR Optimization Noise rejection Disturbance rejection Flexible link

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Achieving CO2 Emission Targets for Energy Consumption at Canadian Manufacturing and beyond;using Hybrid Optimization Model

Achieving CO2 Emission Targets for Energy Consumption at Can...

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International Joint Conference on Neural Networks (IJCNN)

作者： Marzi, Arash Marzi, Elham Marzi, Hosein Dr John Hugh Gillis Reg High Sch Antigonish NS B2G 2W5 Canada St Francis Xavier Univ Dept Informat Syst Antigonish NS B2G 2W5 Canada

ISBN: (纸本)9781467361293;9781467361286

Due to sporadic climate change and global warming, world have signed international protocols promising to reduce their nation's emissions. This study focuses on the application of the bees algorithm, embedded with an artificial neural network, to determine practical yearly reductions for minimizing oil, natural gas, and coal emissions as by-products of energy consumption in Canada's manufacturing sector based on the Copenhagen Targets for Canada for 2020.

关键词： bees algorithm Artificial Neural Networks Optimization Emission reduction Sensitivity analysis

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Heuristic Optimization Methods Applied to Improve the Performance of Controlled DC-DC High Boost Converter with Coupled Inductors 19

Heuristic Optimization Methods Applied to Improve the Perfor...

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19th International Conference on Electrical Machines and Systems (ICEMS)

作者： Marsala, Giuseppe Ragusa, Antonella UOS Palermo CNR ISSIA Palermo Italy

ISBN: (纸本)9784886860989

This paper presents the optimization of the performances of a controlled zero ripple input current high boost dc-dc converter with coupled inductors. In particular, an optimization of PID and LQR controllers design is presented, using three heuristic methods: Genetic, Particle Swarm and bees algorithms. A comparison between the PID and LQR controllers, with and without optimization, is presented together with the analysis of effectiveness of the three heuristic algorithms to design the control strategies.

关键词： DC-DC converter coupled inductors control strategies genetic algorithm particle swarm optimization bees algorithm

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Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications

Exploring the High Performance Computing-Enablement of a Sui...

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10th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

作者： Li, Zhenya Sinnott, Richard O. Choon, Yee Wen Sjaugi, Muhammad Farhan Mohammad, Mohd Saberi Deris, Safaai Napis, Suhaimi Omatu, Sigeru Manuel Corchado, Juan Ibrahim, Zuwairie Yusof, Zulkifli Md Univ Melbourne Dept Comp & Informat Syst Melbourne Vic Australia Univ Teknol Malaysia Fac Comp Johor Baharu Malaysia Perdana Univ Sch Data Sci Serdang Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Kota Baharu Malaysia Univ Putra Malaysia Fac Biotechnol & Biomol Sci Serdang Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka Japan Univ Salamanca BISITE Res Grp Biomed Res Inst Salamanca Salamanca Spain Univ Malaysia Pahang Fac Mfg Engn Fac Elect & Elect Gambang Malaysia

ISBN: (纸本)9783319401263;9783319401256

Genetic engineering provides methods to modify the genes of microorganisms to achieve desired effects. This can be done for improved organism growth rate or increasing production yield of a desired gene product. Gene knockout is a technique that can improve the specific characteristics of microorganisms by disabling selected sets of genes. However, microorganisms are complex and predicting the effects of gene modification is difficult. Several algorithms have been proposed to support a range of gene knockout strategies, including BAFBA, BHFBA and DBFBA. In this paper, scaling these algorithms and methods to utilise High Performance Computing (HPC) resources have been explored. The applications have been parallelized on HPC and the scalability and performance of these approaches were explored and documented.

关键词： High performance computing HPC Gene knockout Genetic engineering bees algorithm Flux balance

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Data Missing Solution Using Rough Set Theory and Swarm Intelligence

Data Missing Solution Using Rough Set Theory and Swarm Intel...

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International Conference on Advanced Computer Science Applications and Technologies (ACSAT)

作者： Sadiq, Ahmed Tariq Duaimi, Mehdi Gzar Shaker, Samir Adil Univ Technol Baghdad Dept Comp Sci Baghdad Iraq Univ Baghdad Dept Comp Sci Baghdad Iraq

ISBN: (纸本)9780769549590;9781467358323

This paper presents a hybrid approach for solving null values problem;it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estimation through working with rough set theory. The results obtained from most code sets show that bees algorithm better than ID3 in decreasing the number of extracted rules without affecting the accuracy and increasing the accuracy ratio of null values estimation, especially when the number of null values is increasing.

关键词： COMPONENT NULL VALUES PROBLEM ROUGH SET bees algorithm ID3 INCOMPLETE DATABASES

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Review on Feature Selection algorithms for Anomaly-Based Intrusion Detection System 3rd

Review on Feature Selection Algorithms for Anomaly-Based Int...

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3rd International Conference of Reliable Information and Communication Technology (IRICT)

作者： Alamiedy, Taief Alaa Anbar, Mohammed Al-Ani, Ahmed K. Al-Tamimi, Bassam Naji Faleh, Nameer Univ Sains Malaysia Natl Adv IPv6 Ctr Usm 11800 Penang Malaysia Taibah Univ Coll Comp Sci & Engn Medina Saudi Arabia USM Sch Elect & Elect Engn Nibong Tebal 14300 Penang Malaysia

ISBN: (纸本)9783319990071;9783319990064

As Internet networks expand, the amount of network threats and intrusions increased, the demand for an efficient and reliable defense system is required to detect network security vulnerabilities. Intrusion Detection Systems (IDS) are a vital constituent of security of a network to avert data illegal usage and misappropriation. IDS deal with massive amount of data movement that comprises repetitive and inappropriate features. The detection rate implementation is frequently affected by these inappropriate features which also munch up intrusion detection system resources. A significant portion in the removal of dissimilar and not used features in IDS is done by the feature selection method. Methods included are data mining techniques, machine learning, statistical analysis, support vector machine models and neural networks. In this paper, we provide review of several algorithms used for anomaly-based intrusion detection systems to improve performance of machine learning classifiers. This paper first summarizes the theoretical basis of IDS, and then discusses the feature selection techniques and their types.

关键词： Intrusion detection system Feature selection Genetic algorithm bees algorithm Support vector machine Principle components analysis Artificial bee colony

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Improving fusion with optimal weight selection in Face Recognition

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INTEGRATED COMPUTER-AIDED ENGINEERING 2012年第3期19卷 229-237页

作者： Quang Duc Tran Kantartzis, Panagiotis Liatsis, Panos City Univ London Informat Engn & Med Imaging Grp Sch Engn & Math Sci London EC1V 0HB England

Face recognition has a large number of applications, including security/counterterrorism, person identification, Internet communications, E-commerce, and computer entertainment. Although research in automatic face recognition has been conducted since the 1960s, there exist research challenges in its practical application in the terms of performance accuracy, which deteriorates significantly with changes in illumination, pose, expression and occlusions. However, these inherent limitations can be potentially alleviated by fusing biometric information based on multiple facial features. Following this vision, the work presented here offers three contributions. Firstly, we present a Face Recognition System, where diverse biometrics features such as total face, eyes, nose, mouth, etc are extracted from the face image. Secondly, we analyse a number of approaches for combining the aforementioned information at matching score level. Thirdly, we proposed a new approach, based on a recently proposed optimisation technique, the bees algorithm, to determine the optimal weight parameters to enhance the performance of the fusion system. Experiments on the CASIA and ORL face databases indicate that the proposed method achieves consistently high recognition rates, compared to traditional FR approaches, such as the Eigenfaces, Fisherfaces, and D-LDA methods.

关键词： Face recognition multibiometric system Linear Discriminant Analysis density based score fusion bees algorithm

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