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Advanced Machine Learning on Cognitive Computing for Human Behavior Analysis

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第5期8卷 1194-1202页

作者： Lv, Zhihan Qiao, Liang Singh, Amit Kumar Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Natl Inst Technol Patna Comp Sci & Engn Dept Patna 800005 Bihar India

With the increasing size of data, massive amounts of data are being generated continuously. It is hoped to find a cognitive computing technology that can effectively learn and process large-scale data. The decision tree algorithm is optimized from the perspective of machine learning. A cognitive computing model based on context-aware data flow is constructed. Classification and regression tree (CART) algorithm is used in the data computing layer of the cognitive model. In addition, the clustering effectiveness index based on frequent patterns optimizes the K-means clustering method. The performance of the algorithm is analyzed through simulation experiments. The results show that the CART algorithm requires fewer training data sets while guaranteeing classification accuracy. Also, the algorithm has obvious advantages under large-scale data. In the application of actual data set, on Over, Over+Noise, and Bridge, only the clustering validity index based on frequent pattern (FPCVI) index proposed finds the correct number of clusters. The application of DPCK-K-means clustering algorithm can ensure the accuracy and stability of behavior classification, which is greatly significant for operators to analyze user behavior and develop personalized services.

关键词： clustering algorithms Machine learning Cognitive systems Machine learning algorithms Data mining Computational modeling Data models Behavior analysis clustering algorithm cognitive computing classification and regression tree~(CART) classification tree algorithm machine learning

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Optimal allocation of higher education resources based on fuzzy particle swarm optimization (Publication with Expression of Concern)

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INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION 2021年第2_SUPPL期60卷 312 - 324页

作者： Cui, Jiangning China Univ Min & Technol Xuzhou 221116 Jiangsu Peoples R China Shandong Womens Univ Jinan Peoples R China

The development concept of higher education will have a direct impact on the development direction of higher education, because the concept usually reflects people's understanding of the law of higher education, and the current education concept restricts the scale, quality, structure and efficiency of higher education to a certain extent. Throughout the evolution of the world's higher education development concept, it has generally experienced the overall coordination of the economic growth, the internal and external laws, and the people-centered sustainable development. This paper hopes to provide effective help by joint intelligent algorithms. In this paper, the particle swarm optimization algorithm in intelligent optimization algorithm and the FCM algorithm in cluster analysis are studied in depth, and the two are merged together to propose a fuzzy clustering algorithm based on improved particle swarm optimization algorithm. The main research contents of this thesis include: Firstly, the PSO algorithm is improved, and the random search step levy flights and PSO algorithm are repeatedly called by the iterative mode by introducing the eagle strategy. After improvement, the ESPSO algorithm can achieve a balance between global search and local development, and make the particles have more satisfactory search ability and convergence effect. Then the improved PSO algorithm is integrated into the FCM algorithm to solve the above problems of the FCM clustering algorithm and improve the clustering effect. The experimental results show that the improved espso algorithm and FCM algorithm are suitable for actual data. The simulation results show that the algorithm has better clustering effect than the traditional clustering algorithm.

关键词： Fuzzy particle swarm optimization higher education resource optimization artificial intelligence clustering algorithm

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Wheel hub customization with an interactive artificial immune algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2021年第5期32卷 1305-1322页

作者： Liu, Jing Zhi, Qiqi Ji, Haipeng Li, Bolong Lei, Siyuan Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China Hebei Univ Technol Sch Elect Engn Tianjin Peoples R China Tianjin JingNuo Data Technol Co Ltd Tianjin Peoples R China Beijing Baidu Netcom Sci Technol Co Ltd Beijing Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China

With the transformation from traditional manufacturing to intelligent manufacturing, customer-oriented personalized customization has gradually become the main mode of production. Interactive algorithms determine the pros and cons of the solution via customers which can make customers better participants in the customization process. However, if the population size is expanded and the number of evolutionary iterations is too high, frequent interactions are likely to cause customer fatigue. This paper proposes an adaptive interactive artificial immune algorithm based on improved hierarchical clustering. This algorithm uses the improved hierarchical clustering algorithm to optimize generation of the initial antibodies and applies the affinity calculation method based on customer intention, adaptive crossover and mutation operators, and a multisolution reservation method based on hybrid selection strategy to the artificial immune algorithm. Via empirical research on the customized operational data of wheel hubs, the proposed method effectively solves the problem of customer fatigue, significantly improves the convergence speed of the algorithm and reduces the time cost.

关键词： Personalized customization Interactive artificial immune algorithm Wheel hub clustering algorithm Customer fatigue

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Bi-level economic dispatch strategy for virtual power plants based on electric vehicles aggregation

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 225卷

作者： Guo, Jinrui Dou, Chunxia Zhang, Zhijun Yue, Dong Nanjing Univ Posts & Telecommun Inst Adv Technol Carbon Neutral Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing 210023 Jiangsu Peoples R China

The rapid growth of electric vehicles (EVs) imposes great challenges to the flexible management and economic dispatch of virtual power plants. To overcome these obstacles, this paper proposes a bi-level economic dispatch strategy based on EVs aggregation. First, an aggregation method based on improved artificial bee colony Kmeans clustering algorithm is presented to aggregate the EVs with different dynamic characteristics in the upper level. Aiming at the issue that multi-parameter weights and aggregation effects are difficult to determine in the clustering process, information entropy and Silhouette metric are proposed to enhance the accuracy of clustering. Furthermore, pre-dispatch is conducted based on aggregation information and electricity market prices. Next, in the lower level, the charging demands information is utilized to formulate specific dispatch strategies for EVs users, and the pre-dispatch deviation in the upper level is modified. Finally, the simulation results show that the proposed aggregation method improves the clustering effect by 22.4% and guarantees the quality of aggregation and the flexibility of system management. Besides, the proposed strategy can reduce the total system operation cost and the computation time by 5.83% and 55.95% respectively and ensure the economy of the system operation and the computational efficiency of the dispatch.

关键词： Virtual power plants Electric vehicles aggregation Bi-level dispatch clustering algorithm Information entropy Silhouette metric

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A novel clustering-based method for reliability assessment of cyber-physical microgrids considering cyber interdependencies and information transmission errors

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APPLIED ENERGY 2022年 315卷

作者： Aslani, Mehrdad Faraji, Jamal Hashemi-Dezaki, Hamed Ketabi, Abbas Univ Kashan Dept Elect & Comp Engn 6 Km Ghotbravandi Blvd Kashan *** Iran Univ West Bohemia UWB Res & Innovat Ctr Elect Engn RICE Fac Elect Engn Plzen 30100 Czech Republic

In recent years, the evolution of microgrids (MGs) toward a complex interacted cyber-physical system is significant, which has received much attention. Cyber-physical MGs (CPMGs) are vulnerable to various cyberphysical interdependencies mainly. Therefore, the reliability evaluation of such systems is a crucial issue. Several research works have focused on the reliability assessment of CPMGs in the literature. However, there is a research gap in developing fast and accurate models to assess the reliability of CPMGs, simultaneously considering cyber and physical failures, cyber-power interdependencies, and information transmission errors. This article aims to fill such a knowledge gap by developing a new clustering-based method to evaluate the reliability of CPMGs. The scenarios for information transmission errors are generated by the Monte Carlo simulation (MCS), besides uncertain physical parameters, e.g., the output power of renewable distributed generations (DGs). Afterward, the clustering algorithms are used to reduce the number of scenarios. In this paper, the k-means clustering algorithm has been selected to cluster the scenarios. The introduced clustering-based method is applied to a CPMG, and test results are compared to available MCS-based studies. The inaccuracy of the proposed clustering-based method is less than 3%, while its execution time is about 22 times faster than MCS-based ones. The comparative test results under various scenarios like available methods, neglecting the cyber failures and information errors illustrate the advantages of this research. The comparative test results illustrate the accuracy of the reliability calculations, while the execution time is desired.

关键词： Cyber-physical systems Microgrids Reliability assessment clustering algorithm Monte Carlo simulation Information transmission errors

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A Decomposition Method of Electricity Quantity Based on Typical Load Curve Simulation

A Decomposition Method of Electricity Quantity Based on Typi...

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58th IEEE/IAS Industrial and Commercial Power Systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Hong, HuaWei Zhu, LingLing Hong, Liang Wang, Zimou Fujian Power Supply Co State Grid Elect Power Co Ltd Fuan Fujian Peoples R China

ISBN: (数字)9781665450669

ISBN: (纸本)9781665450669

In the settlement process of electricity spot market, it is crucial to obtain the real-time power of users. This paper designs a method to obtain the typical load curve of the users without smart meters in the spot market and then proposes a power decomposition strategy to determine the Time-of-Use consuming quantity of these users. Firstly, users are classified according to their industry characteristics, this paper assumes that the same type users will have similar daily load curve. Then, the history load data of the users with smart meters is collected, and the typical load curve is determined by an improved clustering algorithm based on these history data. Lastly, the Time-of-Use consuming quantity of the users without smart meters can be determined according to the typical load curve. In addition, an improved load curve clustering algorithm based on FCM is proposed, the improved XB clustering validity index is introduced to judge the effect of the clustering results. The actual power load data of Fujian province are used for case study, results verify the correctness and feasibility of the proposed method.

关键词： electricity spot market typical load curve clustering algorithm clustering validity index

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Research on PCB Small Target Defect Detection Based on Improved YOLOv5 3

Research on PCB Small Target Defect Detection Based on Impro...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Liang, Mou Wu, Jigang Cao, Hong Hunan University of Science and Technology Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment Xiangtan China

ISBN: (纸本)9781665492812

As global automation accelerates, the importance of the PCB as a core component of electronic products grows with each passing day. The smallest hazards in PCBs can cause huge losses, so testing the quality of PCBs is an important step in the production process. To address the high level of integration, miniaturization, and multilayering of PCB production technology, we are using a new and improved model based on YOLOv5 to detect PCB defects. This new model solves the problems of difficult feature extraction, the similarity between features, and poor detection performance of PCB defects. In this paper, we use 10,668 images of PCB data containing six different defects. Experimental results show that the improved model in this paper has a detection accuracy of 99.0% and a detection speed of 0.016s compared to other defect detection algorithms of the same type. © 2022 IEEE.

关键词： Attention mechanism clustering algorithm Decoupled-head PCB defect detection YOLOv5

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A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

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IEEE ACCESS 2021年 9卷 16114-16132页

作者： Aslam, Saad Alam, Fakhrul Hasan, Syed Faraz Rashid, Mohammad A. Massey Univ Sch Food & Adv Technol Dept Mech & Elect Engn Auckland 0632 New Zealand Manukau Inst Technol Sch Profess Engn Auckland 2104 New Zealand

clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.

关键词： clustering algorithm content multicasting D2D enabled networks deep neural networks eNB loading machine learning random forest support vector machine user segregation

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Effective User Preference clustering in Web Service Applications

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COMPUTER JOURNAL 2020年第11期63卷 1633-1643页

作者： Wang, Yan Zhou, Jian-Tao Li, Xinyuan Song, Xiaoyu Inner Mongolia Univ Inner Mongolia Engn Lab Cloud Comp 235 Daxue West St Saihan Dist 010021 Hohhot Peoples R China Inner Mongolia Univ Coll Comp Sci 235 Daxue West St Saihan Dist 010021 Hohhot Peoples R China Portland State Univ Dept Elect & Comp Engn 1900 SW Fourth AveSuite 160 Portland OR 97207 USA

The research on personalized recommendation of Web services plays an important role in the field of Web services technology applications. Fortunately, not all users have completely different service preferences. Due to the same application scenarios and personal interests, some users have the same preferences for certain types of Web services. This paper explores the problem of user clustering in the service environment, grouping users according to their service preferences. It helps service providers to identify and characterize the preferences of similar users and provide them with customized services. We propose two combination-based clustering algorithms which make full use of the advantages of the K-means algorithm and the affinity propagation algorithm. In addition, a three-stage clustering process is elaborated to improve the accuracy of user clustering. To reduce the time complexity of the algorithms, we create a parallel execution model of the algorithms implemented by a higher-order MapReduce sequence linking technology. Extensive experiments on simulated datasets and real datasets are performed on the comparisons between the proposed algorithms and the other combination-based clustering algorithms. The experimental results substantiate that the proposed algorithms can effectively distinguish user group with different preferences.

关键词： clustering algorithm combination user preference Affinity propagation K-means

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Performance analysis of iterative linear regression-based clustering in wireless sensor networks

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第4期14卷 423-429页

作者： Meenalochani, Manickam Hemavathi, Natarajan Sudha, Selvaraj Natl Inst Technol Dept EEE Trichy Tamil Nadu India SASTRA Dept EIE Thirumalaisamudram Tamil Nadu India

clustering, an energy efficient approach is preferred in wireless sensor network due to the limited energy consumption of sensor nodes. clustering saves energy as the communication is restricted to a few nodes, thereby increasing the network lifetime. Recently, an iterative linear regression-based clustering is proposed to improve the cluster quality in a wireless sensor network. The quality of clusters so obtained is evaluated through Matlab using the cluster validity analysis platform. However, the performance of the clusters so obtained using iterative linear regression in terms of energy efficiency is not dealt. Hence, an attempt is made to investigate the performance of the network through real time experiments. The network performances are studied in terms of intra-cluster and inter-cluster energy consumption, first node death, half node death and last node death. To prove the effectiveness of the proposed technique, it is compared with the standard k-means clustering algorithm. The results reveal that the clusters obtained using iterative linear regression is efficient in enhancing the lifetime of the wireless sensor network.

关键词： energy consumption iterative methods pattern clustering wireless sensor networks regression analysis energy conservation telecommunication network reliability telecommunication power management iterative linear regression-based clustering wireless sensor network energy efficient approach sensor nodes network lifetime cluster quality cluster validity analysis platform energy efficiency network performances inter-cluster energy consumption clustering algorithm performance analysis Matlab intra-cluster energy consumption first node death half node death last node death standard k-means clustering algorithm

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