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Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

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Frontiers of Structural and Civil Engineering 2019年第4期13卷 863-889页

作者： Tugrul TALASLIOGLU Department of Civil Engineering Osmaniye Korkut Ata University Osmaniye 80000 Turkey

This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enhanced genetic algorithm methodology named EGAwMP is utilized as an optimization tool. The evolutionary search mechanism of EGAwMP is constituted on the basis of generational genetic algorithm (GGA). The exploration capacity of EGAwMP is improved in a way of dividing an entire population into sub-populations and using of a radial basis neural network for dynamically adjustment of EGAwMP’s genetic operator parameters. In order to improve the exploitation capability of EGAwMP, the proposed neural network implementation is also utilized for prediction of more accurate design variables associating with a new design strategy, design codes of which are based on the provisions of LRFD_AISC V3 specification. EGAwMP is applied to determine the real-life ready steel profiles for the optimal design of skeletal structures with 105, 200, 444, and 942 members. EGAwMP accomplishes to increase the quality degrees of optimum designations Furthermore, the importance of using the real-life steel profiles and design codes is also demonstrated. Consequently, EGAwMP is suggested as a design optimization tool for the real-life steel skeletal structures.

关键词： design optimization genetic algorithm multiple populations neural network

A non-group parallel frequent pattern mining algorithm based on conditional patterns

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Frontiers of Information Technology & Electronic Engineering 2019年第9期20卷 1234-1245页

作者： Zhe-jun KUANG Hang ZHOU Dong-dai ZHOU Jin-peng ZHOU Kun YANG College of Computer Science and Technology Changchun UniversityChangchun 130022China School of Economics Changchun UniversityChangchun 130022China School of Information Science and Technology Northeast Normal UniversityChangchun 130117China Division of Engineering Science University of TorontoOntario M5S2E8Canada School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUK

Frequent itemset mining serves as the main method of association rule mining.With the limitations in computing space and performance,the association of frequent items in large data mining requires both extensive time and effort,particularly when the datasets become increasingly larger.In the process of associated data mining in a big data environment,the MapReduce programming model is typically used to perform task partitioning and parallel processing,which could improve the execution effciency of the algorithm.However,to ensure that the associated rule is not destroyed during task partitioning and parallel processing,the inner-relationship data must be stored in the computer space.Because inner-relationship data are redundant,storage of these data will significantly increase the space usage in comparison with the original dataset.In this study,we find that the formation of the frequent pattern(FP)mining algorithm depends mainly on the conditional pattern bases.Based on the parallel frequent pattern(PFP)algorithm theory,the grouping model divides frequent items into several groups according to their frequencies.We propose a non-group PFP(NG-PFP)mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks.Moreover,we present the NG-PFP algorithm for task partition and parallel processing,and its performance in the Hadoop cluster environment is analyzed and discussed.Experimental results indicate that the non-group model shows obvious improvement in terms of computational effciency and the space utilization rate.

关键词： Frequent pattern mining Parallel algorithm Conditional pattern bases MapReduce Big data

A Highly Efficient algorithm for Phased-Array mmWave Massive MIMO Beamforming

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Computers, Materials & Continua 2021年第10期69卷 679-694页

作者： Ayman Abdulhadi Althuwayb Fazirulhisyam Hashim Jiun Terng Liew Imran Khan Jeong Woo Lee Emmanuel Ampoma Affum Abdeldjalil Ouahabi Sébastien Jacques Department of Electrical Engineering Jouf UniversitySakakaAljouf72388Kingdom of Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)Serdang43400Malaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan School of Electrical and Electronics Engineering Chung-Ang UniversitySeoul06974Korea Electrical and Electronic Department Kwame Nkrumah University of Science and TechnologyKumasiGhana UMR 1253 iBrainUniversitéde ToursINSERMToursFrance LIMPAF Laboratory Department of Computer ScienceUniversity of BouiraBouira10000Algeria University of Tours(France) GREMAN UMR 7347CNRSINSA Centre Val-de-LoireTours37100France

With the rapid development of the mobile internet and the internet of things(IoT),the fifth generation(5G)mobile communication system is seeing explosive growth in data traffic.In addition,low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands.Millimeter wave(mmWave)technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks.Importantly,it has an abundant resource spectrum,which can significantly increase the communication rate of a mobile communication system.As such,it is now considered a key technology for future mobile communications.MmWave communication technology also has a more open network architecture;it can deliver varied services and be applied in many scenarios.By contrast,traditional,all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption.This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption.The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition(GMD).In this process,the objective function of the spectral efficiency is derived,then the basic tracking principle and least square(LS)techniques are deployed to design the proposed hybrid precoding.Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45%compared to traditional algorithms.

关键词： 5G mmWave phased array algorithm antenna beamforming

Optimal economic dispatching of multi-microgrids by an improved genetic algorithm

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IET Cyber-Systems and Robotics 2021年第1期3卷 68-76页

作者： Haoyu Jiang Shiyuan Ning Quanbo Ge Wang Yun JinQiang Xu Yu Bin School of Electronics and Information Engineering Guangdong Ocean UniversityGuangdongChina School of Automation Hangzhou Dianzi UniversityHangzhouChina School of Logistics Engineering Shanghai Maritime UniversityShanghaiChina

A multi-microgrid economic dispatching strategy based on adaptive mutation genetic algorithm is proposed for multi-microgrid systems with different load types and power demands.Based on the analysis of industrial,residential and commercial loads,considering the synergy and complementarity between multi-microgrids,an optimal dispatching model of multi-microgrids based on the minimum operation cost and environmental protection cost of multi-microgrids is established from the point of view of environmental protection and economy.At the same time,an adaptive mutation genetic algorithm is proposed to optimize the system model and find the optimal economic dispatching scheme.

关键词： algorithm optimal cost

Fault diagnosis of the bushing infrared images based on mask R‐CNN and improved PCNN joint algorithm

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High Voltage 2021年第1期6卷 116-124页

作者： Jun Jiang Yifan Bie Jiansheng Li Xiaoping Yang Guoming Ma Yuncai Lu Chaohai Zhang Jiangsu Key Laboratory of New Energy Generation and Power Conversion Nanjing University of Aeronautics and AstronauticsNanjingChina Department of Electrical&Electronic Engineering School of EngineeringThe University of ManchesterManchesterUK State Grid Jiangsu Electric Power Co.Ltd.Research Institute NanjingChina State Grid Jiangsu Electric Power Co.Ltd. NanjingChina State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijingChina

Bushings are served as an important component of the power transformers;it's of great significance to keep the bushings in good insulation condition.The infrared images of the bushing are proposed to diagnose the fault with the combination of image segmentation and deep learning,including object detection,fault region extraction,and fault diagnosis.By building an object detection system with the frame of Mask Region convolutional neural network(CNN),the bushing frame can be exactly extracted.To distinguish the fault region of bushings and the background,a simple linear iterative clustering‐based pulse coupled neural network is proposed to improve the fault region segmentation performance.Then,two infrared image feature parameters,the relative position and area,are explored to classify fault type effectively based on the K‐means cluster technique.With the proposed joint algorithm on bushing infrared images,the accuracy reaches 98%,compared with 44%by the conventional CNN classification method.The integrated algorithm provides a feasible and advantageous solution for the field application of bushing image‐based diagnosis.

关键词： neural fault algorithm

Reservoir inflow predicting model based on machine learning algorithm via multi-model fusion:A case study of Jinshuitan river basin

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IET Cyber-Systems and Robotics 2021年第3期3卷 265-277页

作者： Wei Zhang Hanyong Wang Yemin Lin Jianle Jin Wenjuan Liu Xiaolan An Jinshuitan Hydropower Plant of State Grid Zhejiang Electric Power Co.Ltd LishuiChina Institute of Cyber-Systems and Control College of Control Science and EngineeringZhejiang UniversityHangzhouChina

Flood prevention and disaster mitigation have a great impact on people's lives and properties,and so it is urgent to realise high-accuracy inflow predictions for flood early warning.To this end,a prediction model based on a machine learning algorithm via a multimodel combination method is proposed to predict the inflow of Jinshuitan reservoir.Firstly,a data formatting scheme called the‘hydrological regime profile’is designed for input data.The whole data set is partitioned into a low-flow subset and a high-flow subset.Considering the high dimensions of the complex input data,convolutional neural networks(CNN),EXtreme gradient Boosting model(XGBoost)and a partial least squares model(PLS)are used.In the CNN and XGBoost models,a special loss function weighted on inflow is designed to improve the performance on high-inflow predictions.Finally,a multi-model combination method is proposed to improve the prediction performance.Compared with XGBoost,CNN and PLS,the root mean square error of the combined model is reduced by 41.64%,72.29%and 3.41%,respectively.As a consequence,the combined model is able to predict the inflows with higher accuracy compared to the single models.

关键词： prediction algorithm disaster

An improved YOLOv3-tiny algorithm for vehicle detection in natural scenes

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IET Cyber-Systems and Robotics 2021年第3期3卷 256-264页

作者： Bingqiang Huang Haiping Lin Zejun Hu Xinjian Xiang Jiana Yao School of Automation and Electrical Engineering Zhejiang University of Science and TechnologyHangzhouChina College of Information Engineering Hangzhou Vocational&Technical CollegeHangzhouChina

YOLO(You Only Look Once),as a target detection algorithm with good speed and precision,is widely used in the industry.In the process of driving,the vehicle image captured by the driving camera is detected and it extracts the license plate and the front part of the vehicle.Compared with the network structure of YOLOv3-tiny algorithm,the acquisition method of anchor box is improved by combining the Birch algorithm.In order to improve the real-time performance,the original two-scale detection is added to the multi-scale prediction of three-scale detection to ensure its accuracy.Finally,the experimental results show that the improved YOLOv3-tiny network structure proposed in this study can improve the performance of mean-average-precision,intersection over union and speed by 5.99%,17.52%and 48.4%,respectively,and the algorithm has certain robustness.

关键词： algorithm driving network

An aero-engine life-cycle maintenance policy optimization algorithm: Reinforcement learning approach

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Chinese Journal of Aeronautics 2019年第9期32卷 2133-2150页

作者： Zhen LI Shisheng ZHONG Lin LIN School of Mechatronics Engineering Harbin Institute of Technology

An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimization algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iteration algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposed algorithm. Two numerical experiments and algorithm analyses were performed to illustrate the optimization algorithm in detail.

关键词： Aero-engine Hybrid strategy Maintenance policy Optimization algorithm Reinforcement learning

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

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Frontiers in Energy 2019年第2期13卷 386-398页

作者： Chuan Choong YANG Chit Siang SOH Vooi Voon YAP Chuan Choong YANG (El) Chit Siang SOH Vooi Voon YAP Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Jalan Kolej Taman Bandar Barn Kampar 31900 Perak Malaysia

The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.

关键词： load disaggregation voltage-current (V-I) trajectory multi-stage classification algorithm principal component analysis (PCA) k-nearest neighbor (k-NN)