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Structured Joint Sparse Orthogonal Nonnegative Matrix Factorization for Fault Detection

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhang, Xi Xiu, Xianchao Zhang, Chao Beijing Jiaotong Univ Sch Math & Stat Beijing Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

As modern industrial processes become complicated, and some faults are difficult to be detected due to noises and nonlinearity of data, data-driven fault detection (FD) has been extensively used to detect abnormal events in functional units. To obtain better FD performance of nonnegative matrix factorization (NMF), this article first proposes an FD method using the structured joint sparse orthogonal NMF (SJSONMF). The core idea is to incorporate the graph regularization, sparsity, and orthogonality constraints into the classical NMF, which enjoys stronger discriminative ability, removes redundancy of different basis vectors, and improves fault interpretability. More importantly, an optimization algorithm based on the proximal alternating nonnegative least squares (PANLS) is developed, which can guarantee and speed up the convergence. Finally, the effectiveness of the proposed method is demonstrated by the experiments on the benchmark Tennessee Eastman Process (TEP) and two practical bearing datasets. Particularly, compared with the classical NMF, the T-2 statistic has a gain of 33.13% for the fault IDV(16) on the TEP. The results show that the proposed model and algorithms are promising for FD.

关键词： Sparse matrices Convergence optimization Data models Principal component analysis Laplace equations Fault detection Fault detection (FD) non-Gaussian process nonnegative matrix factorization (NMF) optimization algorithm orthogonality constraint

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Reviewer assignment algorithms for peer review automation: A survey

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INFORMATION PROCESSING & MANAGEMENT 2022年第5期59卷

作者： Zhao, Xiquan Zhang, Yangsen Beijing Informat Sci & Technol Univ 12 Xiaoying East Rd Beijing 100192 Peoples R China

Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers.

关键词： Matching degree Information retrieval Reviewer assignment problem optimization algorithm Natural language processing Peer review

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Image Reconstruction for Few-view Computed Tomography Based on ℓ 0 Sparse Regularization

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Procedia Computer Science 2017年 107卷 808-813页

作者： Wei Yu Chengxiang Wang Xiaoying Nie Min Huang Limin Wu School of Biomedical Engineering Hubei University of Science and Technology Xianning 437100 China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu 611731 China School of Biomedical Engineering South-Central University for Nationalities Wuhan 430074 China Hubei Key Laboratory of Medical Information Analysis & Tumor Diagnosis and Treatment Wuhan 430074 China

Accurate CT images are expected to be obtained from low-dose/limited projection data. In this work, an ℓ 0 sparse regularization based optimization model was investigated for few-view CT. With the aim to effectively solve the optimization model, original optimization problem was transformed following the framework of iterative reconstruction based on alternating direction (ADM) method. Experiments are demonstrated to validate the efficiency of the proposed algorithm.

关键词： computed tomography iterative reconstruction few-view ℓ 0 sparse regularization optimization algorithm

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A Vertical-axis Wind-solar Complementary Power Generation System Optimized based on Adaptive Particle Swarm algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第5期7卷 492-500页

作者： Gaoyuan Liu Guangyuan Zhong Huayi Sun Jiarui Liang

Wind energy and solar energy are inexhaustible green, clean and renewable energy sources on the earth. Comprehensive utilization of wind and solar resources and the development of wind-solar complementary power generation technology has become a research and development trend in the field of new energy. The wind and solar hybrid power generation system is a power generation system that combines wind power and solar photovoltaic power generation, which is mainly composed of wind turbines, solar photovoltaic battery packs, controllers, batteries, inverters, AC and DC loads and other parts. This paper systematically expounds the composition of the wind-solar hybrid power generation system and the characteristics of each part, proposes a new type of vertical axis wind turbine, and uses a newly proposed improved particle swarm algorithm (YAPSO) to optimize the multi-objective battery in wind-solar hybrid power generation. The simulation experiment proves the feasibility and practicability of the proposed algorithm.

关键词： Vertical-axis Wind and Solar Hybrid Power Generation Microgrid optimization algorithm Self-adaptation Multi-objective optimization

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Effects of the load forecasts mismatch on the optimized schedule of a real small-size smart prosumer

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Energy Procedia 2017年 126卷 406-413页

作者： Piero Danti Sandro Magnani Yanmar R&D Europe Firenze 50125 Italy

The increasing need for reducing the costs and the environmental impact of the energy supply renewed the interest on distributed generation, also favoured by the recent EU directives. Anyway, the solely installation of efficient small- and medium-size pieces of equipment is not sufficient to achieve the expected targets, being their proper scheduling and management, of course based on the fluctuations of both the loads pattern and the energy prices, the fundamental issue determining their effectiveness. In recent times, several techniques have been proposed with the purpose of optimizing the operation of the installed generators on the basis of load predictions; even if these latter ones heavily affect the performance of the plant management, especially when considering a single prosumer whose behaviour is scarcely predictable with a good accuracy, often this aspect is neglected. The present study aims to analyse how inaccurate load predictions affect the performance of an energy plant whose generators are scheduled by an optimization tool working considering a time span of one day. Different “structures” of error will be modelled and analysed, taking as benchmark load profiles the acquired data for different periods of the year from an office building plant, equipped with a PV plant, two micro-CHP system combined with an absorption chiller, an electric chiller, a gas boiler and a reversible electric heat pump, with thermal storages. The focus will be on the comparison of both the economic and CO 2 emission impact, by considering on one side the loads prediction as perfect and on the other side with different entity of errors, with the target to stress the importance of the correct loads prediction issue.

关键词： CHP EHP optimization algorithm Load forecast mismatch

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A Cooperative and Hybrid Network Intrusion Detection Framework in Cloud Computing Based on Snort and Optimized Back Propagation Neural Network

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Procedia Computer Science 2016年 83卷 1200-1206页

作者： Z. Chiba N. Abghour K. Moussaid A. El omri M. Rida Team of Modeling and Optimization of mobile services Faculty of Sciences Hassan II University of Casablanca 20100 Casablanca Morocco

Cloud computing provides a framework for supporting end users easily attaching powerful services and applications through Internet. To give secure and reliable services in cloud computing environment is an important issue. Providing security requires more than user authentication with passwords or digital certificates and confidentiality in data transmission, because it is vulnerable and prone to network intrusions that affect confidentiality, availability and integrity of Cloud resources and offered services. To detect DoS attack and other network level malicious activities in Cloud, use of only traditional firewall is not an efficient solution. In this paper, we propose a cooperative and hybrid network intrusion detection system (CH-NIDS) to detect network attacks in the Cloud environment by monitoring network traffic, while maintaining performance and service quality. In our NIDS framework, we use Snort as a signature based detection to detect known attacks, while for detecting network anomaly, we use Back-Propagation Neural network (BPN). By applying snort prior to the BPN classifier, BPN has to detect only unknown attacks. So, detection time is reduced. To solve the problem of slow convergence of BPN and being easy to fall into local optimum, we propose to optimize the parameters of it by using an optimization algorithm in order to ensure high detection rate, high accuracy, low false positives and low false negatives with affordable computational cost. In addition, in this framework, the IDSs operate in cooperative way to oppose the DoS and DDoS attacks by sharing alerts stored in central log. In this way, unknown attacks that were detected by any IDS can easily be detected by others IDSs. This also helps to reduce computational cost for detecting intrusions at others IDS, and improve detection rate in overall the Cloud environment.

关键词： Cloud computing Network intrusion detection Back-propagation neural network Snort optimization algorithm

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A Review of Research on Vehicle Routing Problem for Electric Vehicles

Frontiers in Economics and Management

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Frontiers in Economics and Management 2022年第10期3卷 96-103页

作者： Fei Peng

In recent years, China's logistics industry has developed rapidly, and the logistics network has become increasingly large. How to optimize the vehicle route is one of the key issues to reduce the cost of logistics and distribution. With the continuous progress of new energy technology, scholars at home and abroad have given a lot of attention to the EVRP problem model and its solution method. With continuous in-depth research, many branch problems with more constraints have been developed, and their solution methods have become more and more diverse. In order to further sort out the research status of EVRP problem at home and abroad, the basic model, variant problem and its solving algorithm type of EVRP problem are firstly introduced.

关键词： Electric Vehicle Routing Problem Literature Review optimization algorithm

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Application Designing Additive Structures

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Procedia Engineering 2017年 185卷 131-138页

作者： Valeriy A. Komarov Alexander A. Pavlov Svetlana A. Pavlova Aircraft Design Department Samara University Samara Russian Federation

The objective of this paper is to demonstrate the opportunities of topology optimization applied to additive technology which will permit to design ultra-light structures practically without regard to the technological limits. The article gives a brief historical overview of the mutual influence of structures, materials and manufacturing technologies. The additive technology seems to have the broadest opportunities for producing existing structures using conventional materials without design changes. A hypothetical variable density material provides the means to solve an auxiliary problem of optimal material distribution considering stress or stiffness constraints. The special optimization algorithm allows the optimal topology layout to be found which will have a minimum value of the integral characteristic, called “load-carrying factor” (LCF). The LCF is a powerful tool for estimating the perfection limit of any structural and technological solution. Along with the optimal structure, such solutions are difficult for manufacturing using conventional materials and technologies. A creation of real material with the characteristics of hypothetical material is considered as one of the nearest areas of additive technology's development for finding optimal structures.

关键词： structure additive technology hypothetic material optimization algorithm load-carrying factor optimal structure

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Study of Wireless Sensor Network Route Based on Improved Ant Colony algorithm

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2016年第10期12卷 86-90页

作者： Bo, Jingyi Wang, Yubin Xu, Na Hebei Normal Univ Sci & Technol Coll Math & Informat Sci & Technol Qinhuangdao 066004 Hebei Peoples R China

Combining the characteristics of wireless sensor network, the ant colony algorithm is applied to a wireless sensor network, and a wireless sensor network route algorithm based on energy equilibrium is proposed in this paper. This algorithm takes the energy factor into the consideration of selection of route based on probability and enhanced calculation of information so as to find out the optimal route from the source node to the target node with low cost and balanced energy, and it prolongs the life cycle of the whole network.

关键词： wireless sensor network ant colony algorithm network route optimization algorithm

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Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Ustun, Deniz Toktas, Abdurrahim Erkan, Ugur Akdagli, Ali Engn Fac Dept Comp Engn TR-33400 Tarsus Mersin Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Elect Elect Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Comp Engn TR-70200 Karaman Turkey Mersin Univ Engn Fac Dept Elect Elect Engn TR-33343 Mersin Turkey

Artificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee;in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted "modified ABC by DE" (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergence.

关键词： Artificial bee colony (ABC) Differential evolution (DE) optimization algorithm Modified ABC Modified algorithm Hybrid algorithm

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