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Meta-heuristic optimization inspired by proton-electron swarm

The Journal of China Universities of Posts and Telecommunications 2020年第3期27卷 42-52,72页

作者： Liu Yongli Liu Shen School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo 454000China

While solving unimodal function problems,conventional meta-heuristic algorithms often suffer from low accuracy and slow ***,in this paper,a novel meta-heuristic optimization algorithm,named proton-electron swarm(PES),is proposed based on physical *** algorithm simulates the physical phenomena of like-charges repelling each other while opposite charges attracting in protons and electrons,and establishes a mathematical model to realize the optimization *** balancing the global exploration and local exploitation ability,this algorithm achieves high accuracy and avoids falling into local optimum when solving target *** order to evaluate the effectiveness of this algorithm,23 classical benchmark functions were selected for comparative *** results show that,compared with the contrast algorithms,the proposed algorithm cannot only obtain higher accuracy and convergence speed in solving unimodal function problems,but also maintain strong optimization ability in solving multimodal function problems.

关键词： meta-heuristic proton electron swarm

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Improved material descriptors for bulk modulus in intermetallic compounds via machine learning

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Rare Metals 2023年第7期42卷 2396-2405页

作者： De-Xin Zhu Kun-Ming Pan Yuan Wu Xiao-Ye Zhou Xiang-Yue Li Yong-Peng Ren Sai-Ru Shi Hua Yu Shi-Zhong Wei Hong-Hui Wu Xu-Sheng Yang National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials Henan Key Laboratory of High-Temperature Structural and Functional MaterialsHenan University of Science and TechnologyLuoyang471003China Longmen Laboratory Luoyang471003China State Key Laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing100083China Guangdong Province Key Laboratory of Durability for Marine Civil Engineering School of Civil EngineeringShenzhen UniversityShenzhen518060China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong999077China

Bulk modulus is an important mechanical property in the optimal design and selection of intermetallic *** this study,bulk modulus datasets of intermetallic compounds were collected,and the features affecting the bulk modulus of intermetallics were screened via feature *** features B_(cal),dB_(avg),and TIE(corresponding to calculated bulk modulus,mean bulk modulus,and third ionization energy,respectively)were found to be the dominant factors influencing bulk modulus and can be extended to other multi-component ***,we predicted the bulk modulus with an accuracy of 95%using surrogate machine learning models with the selected features,and these features were also demonstrated to be effective for high-entropy ***,symbolic regression provided an expression for the relationship between bulk modulus and the screened *** machine learning models provide a new approach for optimizing and predicting the bulk moduli of intermetallic compounds.

关键词： Bulk modulus Intermetallic compounds Machine learning Symbolic regression

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Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

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2nd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Online Retail Customer Segmentation using RFM Quantiles and Clustering Technique 4

Online Retail Customer Segmentation using RFM Quantiles and ...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Majilya, Mouli Maji, Giridhar Ghosh, Partha Sen, Soumya A.K.Choudhury School of Information Technology University of Calcutta Kolkata India Asansol Polytechnic Department of Electrical Engineering Asansol India Academy of Technology Department of Computer Science and Engineering Adisaptagram India

ISBN: (纸本)9798350389128

In the fiercely competitive landscape of modern business, making effective decisions is imperative for generating substantial revenue. With organizations employing diverse strategies to stay ahead, leveraging data for informed decision-making is crucial. Understanding individual preferences and purchasing behaviors can be challenging, but machine learning techniques offer a solution by organizing and analyzing vast datasets. Customer segmentation, particularly through K-means clustering, is instrumental in identifying target groups with similar attributes and interests. The present study focuses on customer clustering method-ologies to give businesses a competitive edge. By collecting, analyzing, and visualizing customer data, we aim to develop a data science model that utilizes K-means clustering and the RFM model (Recency, Frequency, Monetary) for existing customers. Utilizing the UK's e-commerce dataset, which delves into customer purchasing behaviors, we aim to group customers into clusters such as high-value customers, mid-range, and those at risk of churn based on RFM scores. © 2024 IEEE.

关键词： Sales

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Deep Learning Approaches for Road Damage Detection Using YOLOv8 and Custom Convolutional Neural Network 6

Deep Learning Approaches for Road Damage Detection Using YOL...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Khan, Muhammad Waseem Mahmood, Khalid Hussain, Israr Talha Qureshi, Muhammad Muhammad Sanaullah Badar, Hafiz Gao, Wu School of Computer Science Northwestern Poly technical University Center for Bio-inspired System-on-Chip Xi'an China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Taiwan Institute of Computing Mns University of Agriculture Multan Pakistan Henan University Kaifeng450046 China

ISBN: (纸本)9798331541750

The identification of road damage is deemed very important to the preservation of infrastructure. Since recent development in deep learning provide great potential approaches, in this paper, three deep learning models, namely EfficientNet, CNN and YOLOv8 are considered and their performance is evaluated on a dataset with 8,586 images with labelled road damages consisting of seven subclasses including longitudinal cracks and potholes. Besides, other issues like class imbalance and variabilities in the environment were studied and considered in its assessment based on the Models accuracy, precision, recall, and F1-score. With an accuracy of 64%, YOLOv8 is the most accurate model for identifying various types of damages, particularly in identifying multiple damages present in one or more types of damage, as was seen in the high accuracy rate of a split sample of 44 images of multiple damages. Our findings showcase YOLOv8 as a strong contender for real-time road damage detection with potential of enhancement with better augmentation and fine-tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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Based on FFT Transforms Appliance Identification Method 10

Based on FFT Transforms Appliance Identification Method

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10th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2022

作者： Xu, Lihui Qi, Jianguang Pan, Zhenxiang Wang, Yuru Shen, Mingfei School of Information Science and Engineering Dalian Polytechnic University China School of Design Jiangnan University China School of Computer Science and Technology Nanjing University of Technology China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

At present, with the increasing daily power con-sumption of resisdents, more and more electric energy is wasted. Userful often forget that they have turn on the appliance, cause safety hazards and the waste of the electric energy. In order to solve the problem, allow users to see their electrical appliances in use anywhere, reduce the waste of electric energy. This paper come up with a method what based on FFT transforms appliance identification system. First, the current signal is converted into a harmonic signal in the frequency domain by the FFT transform. Then start electrical identification by use KNN algorithm and Apriori algorithm, through simulation and experiment to verify the feadibility of the project. The result of research shows that, the project for signal appliances recognition accuracy can arrived 100%, for multiple appliances recognition accuracy can arrived 97%. Effectively realize the function of the electrical recognition. The results of the study has certain referonce value for the power grid company to plan the dispatch of the power system and power grid construction. © 2022 IEEE.

关键词： Fast Fourier transforms

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Single-Phase Fault Detection Based on GCN-TCN Sparse-Attention Model 12

Single-Phase Fault Detection Based on GCN-TCN Sparse-Attenti...

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12th International Conference on Advanced computer Information Technologies, ACIT 2022

作者： Yong, Ouyang Dou, Wan Rong, Gao Zhiwei, Ye Kochan, Orest Hubei University of Technology School of Computer Science China Hubei University of Technology Nanjing University School of Computer Science State Key Laboratory for Novel Software Technology China University Hubei University of Technology China School of Computer Science Lviv Polytechnic National Lviv Ukraine

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

Effective extraction of fault current features is the problem facing single phase fault detection in distribution networks. We propose a hybrid neural network model based on the GCN-TCN sparse attention mechanism (GTHNN-SA) to solve this problem. Specifically, this model uses the convolutional graph network (GCN) to perform graph convolution operation on the graph data established by the fault line transient zero-sequence current, extract the spatial characteristics of the zero-sequence current data with the fault location relationship, use the temporal convolution network (TCN) to extract the temporal characteristics both of threephase currents and new data generated by GAN. Then fuse and input the three feature matrices to the sparse attention mechanism to highlight essential features of current data. Finally, the output of parse-attention is input to the entire connected layer for classification. This model enables us to learn the current data relationship between different waveform faults more comprehensively, and the proposed method has a good effect on single-phase fault detection in distribution networks. © 2022 IEEE.

关键词： Fault detection

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QLAW: An Improved Quantization-Based Local Audio Watermarking Scheme Using Inter-Frame Correlation

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IEEE Access 2025年 13卷 93359-93371页

作者： Li, Qiutong Xing, Zheng Wang, Ju Huang, Guoheng Yuan, Xiaochen Macao Polytechnic University Faculty of Applied Sciences Macau China Guangdong University of Technology School of Computer Science and Technology Guangzhou China

With the rapid development of the Internet, audio distribution has become more convenient with increasing copyright infringement. To address this problem, this paper proposes a quantization-based local audio watermarking scheme using inter-frame correlation, integrating machine learning techniques and traditional methods. To obtain the time-frequency spectrogram of the audio signal, a Short-Time Fourier Transform (STFT) is first applied to the audio signal. Then, Main Energy Region Extractor (MERE) is proposed to extract the main energy region of the spectrogram. Based on the main energy region, the Stable Frequency and Energy Region Extractor is conducted to find the local feature region for embedding. After segmenting the local feature embedding region into several frames, Adjacent Frame Extraction Process (AFEP) is conducted to select the adjacent frame. Then, Discrete Cosine Transform (DCT) is applied to each embedding frame and its adjacent frame to extract their corresponding frequency domain coefficients. To improve robustness, mid-frequency DCT coefficients are alternately selected to embed the watermark. By adjusting the difference between the embedding frame and its corresponding adjacent frame in a predefined range, the local watermark is embedded. Experimental results show that the proposed scheme outperforms existing schemes in inaudibility and robustness, achieving an average Signal-to-Noise Ratio (SNR) above 25 dB and a lower Bit Error Rate (BER) under various attacks. © 2013 IEEE.

关键词： Audio watermarking

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Remote Monitoring System of Aquaculture Parameters Based on STM32 10

Remote Monitoring System of Aquaculture Parameters Based on ...

引用

10th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2022

作者： Lei, Shanrui Li, Baoying Pan, Zhenxiang Wang, Yuru Shen, Mingfei School of Information Science and Engineering Dalian Polytechnic University China School of Design Jiannan University China School of Computer Science and Technology Nanjing University of Technology China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

In order to detect aquaculture environmental conditions faster and more accurately, this paper designs an aquaculture environmental parameter detection system composed of multiple sensors with STM32 microcontroller as the core controller, One NET cloud platform as the remote monitoring platform. The main function is to use the sensor node to collect water quality parameter information in the breeding area, and use the least square method to establish temperature compensation model for the PH value. Then, the water quality information is uploaded to the One NET cloud platform through the Raspberry Pi 4B+, so as to meet the needs of users for remote real-time monitoring. Experiment shows that the system can meet the basic requirements of farmers to check the water quality parameters of the aquaculture area anytime and anywhere, and meet the expected assumptions. © 2022 IEEE.

关键词： Aquaculture

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Carbon nanotube fibers with excellent mechanical and electrical properties by structural realigning and densification

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Nano Research 2023年第11期16卷 12762-12771页

作者： Kunjie Wu Bin Wang Yutao Niu Wenjing Wang Cao Wu Tao Zhou Li Chen Xianghe Zhan Ziyao Wan Shan Wang Zhengpeng Yang Yichi Zhang Liwen Zhang Yongyi Zhang Zhenzhong Yong Muqiang Jian Qingwen Li Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Division of Nanomaterials and Jiangxi Key Lab of Carbonene Materials Jiangxi Institute of NanotechnologyNanchang 330200China School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026China Henan Key Laboratory of Materials on Deep-Earth Engineering School of Materials Science and EngineeringHenan Polytechnic UniversityJiaozuo 454003China Beijing Graphene Institute(BGI) Beijing 100095China

Floating catalysis chemical vapor deposition(FCCVD)direct spinning process is an attractive method for fabrication of carbon nanotube fibers(CNTFs).However,the intrinsic structural defects,such as entanglement of the constituent carbon nanotubes(CNTs)and inter-tube gaps within the FCCVD CNTFs,hinder the enhancement of mechanical/electrical properties and the realization of practical applications of ***,achieving a comprehensive reassembly of CNTFs with both high alignment and dense packing is particularly ***,an efficient reinforcing strategy for FCCVD CNTFs was developed,involving chlorosulfonic acid-assisted wet stretching for CNT realigning and mechanical rolling for *** reveal the intrinsic relationship between the microstructure and the mechanical/electrical properties of CNTFs,the microstructure evolution of the CNTFs was characterized by cross-sectional scanning electron microscopy(SEM),wide angle X-ray scattering(WAXS),polarized Raman spectroscopy and Brunauer–Emmett–Teller(BET)*** results demonstrate that this strategy can improve the CNT alignment and eliminate the inter-tube voids in the CNTFs,which will lead to the decrease of mean distance between CNTs and increase of inter-tube contact area,resulting in the enhanced inter-tube van der Waals *** microstructural evolutions are beneficial to the load transfer and electron transport between CNTs,and are the main cause of the significant enhancement of mechanical and electrical properties of the ***,the tensile strength,elastic modulus and electrical conductivity of the high-performance CNTFs are 7.67 GPa,230 GPa and 4.36×10^(6)S/m,*** paves the way for further applications of CNTFs in high-end functional composites.

关键词： carbon nanotube fibers mechanical property electrical property alignment packing density

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