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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Yu, Zhenjian Liu, Wei-Li Zhong, Jinghui Huang, Ting Lu, Xu Lin, Zhiyong School of Computer Science Guangdong Polytechnic Normal University Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Guangzhou Institute of Technology Xidian University Guangzhou China

ISBN: (纸本)9798350314588

Evolutionary multitasking (EMT), which aims to exploit effective knowledge among similar tasks to improve search efficiency, is a hot research topic that has recently attracted a lot of attention. Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ant species, is a popular and powerful search algorithm for NP-hard combinatorial optimization problems. However, EMT has seldom been integrated with the ACO. Inspired by the remarkable success of multitasking evolutionary algorithms in numerous research fields, this paper proposes a multitasking ant colony optimization framework (MTACO). The proposed framework enables ants to exploit the pheromones of ant colonies with similar tasks through certain conditions to improve the efficiency and the quality of results of ACO when processing multiple similar tasks dynamically. Furthermore, a multitasking ACS (MTACS) is implemented based on the proposed MTACO framework to solve dynamic vehicle path planning problems (DVPP). The experimental results on DVPP have verified that MTACO can improve the performance of ACO in terms of both algorithm efficiency and quality of results, when ACO is handling multiple tasks simultaneously. © 2023 IEEE.

关键词： Ant colony optimization

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Privacy-preserving human activity sensing:A survey

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High-Confidence Computing 2024年第1期4卷 108-117页

作者： Yanni Yang Pengfei Hu Jiaxing Shen Haiming Cheng Zhenlin An Xiulong Liu School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computing and Decision Sciences Lingnan UniversityHong KongChina Department of Computing The Hong Kong Polytechnic UniversityHong KongChina Department of Computer Science Princeton UniversityNJ 08544USA College of Intelligence and Computing Tianjin UniversityTianjin 300350China

With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being *** using such information provides critical and convenient services,we are gradually exposing every piece of our behavior and *** are aware of the privacy risks and have been working on preserving privacy while sensing human *** survey reviews existing studies on privacy-preserving human activity *** first introduce the sensors and captured private information related to human *** then propose a taxonomy to structure the methods for preserving private information from two aspects:individual and collaborative activity *** each of the two aspects,the methods are classified into three levels:signal,algorithm,and ***,we discuss the open challenges and provide future directions.

关键词： Human activity sensing Privacy-preserving sensing Activity sensing algorithms Human sensors Privacy protection

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Overview of Energy Consumption Optimization in Mobile Edge Computing

Overview of Energy Consumption Optimization in Mobile Edge C...

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2021 International Conference on Electronic Communication, computer science and technology, ECCST 2021

作者： Hu, Bingyi Gao, Jixun Hu, Yanxin Wang, Huaichen Liu, Jialei School of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China School of Computer Henan University of Engineering Zhengzhou451191 China School of Software Engineering Anyang Normal University Anyang455000 China

The energy consumption of traditional mobile network is mainly reflected by task calculation and data transmission. The edge server in mobile edge computing is placed at the edge of the network closer to the mobile terminal device. When the computing resources of the mobile terminal device are insufficient, the task can be offloaded to the edge server for processing. This paper discusses the energy consumption optimization in the Mobile Edge Computing (MEC) system, which is composed of the edge server and mobile devices at the edge of the network. Firstly, the system model of MEC is elaborated. Secondly, the energy consumption optimization for mobile devices and base stations is studied. Which oriented to the mobile device energy consumption optimization is divided into multiple users - single MEC server scenarios of energy consumption optimization and multiple users - the energy optimization of MEC server scenario, and enumerate the oriented to the base station and a new method for the optimization of energy consumption for mobile equipment energy consumption optimization method, in addition to the insufficiencies of these methods and the prospect of the future are also explained. © Published under licence by IOP Publishing Ltd.

关键词： Mobile edge computing

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Automatic Generation of Energy-Efficient Dispatching Rules for Dynamic Flexible Job Shop Scheduling

Automatic Generation of Energy-Efficient Dispatching Rules f...

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2023 China Automation Congress, CAC 2023

作者： Fei, Baolin Xu, Binzi Huang, Dengchao Zhang, Yao Wang, Chun Yang, Long School of Electrical Engineering Anhui Polytechnic University Wuhu241000 China School of Computer Science and Technology Huaibei Normal University Huaibei235000 China School of Computer Jiangsu University of Science and Technology Zhenjiang2121000 China

ISBN: (纸本)9798350303759

Dynamic flexible job shop scheduling (DFJSS) is an important and complex combinatorial optimization problem. Heuristic methods have been extensively studied and proven to be effective in solving the job shop scheduling problems, but they still suffer from difficulties in real-time scheduling when dealing with dynamic environments. In comparison to heuristic methods, genetic programming hyper-heuristic (GPHH) is more suitable for tackling dynamic events since it can make real-time decisions by dispatching rules (DRs) automatically generated based on the current job shop state. However, most existing DR-based studies focus on time-related optimization objectives (e.g., makespan, tardiness, etc.), ignoring energy consumption, which is crucial for meeting the urgent needs of green manufacturing in current society. Therefore, this paper systematically designs the energy-efficient terminals for GPHH, following an in-depth analysis of energy flow in the job shop. Besides, the paper proposes the energy-efficient manually designed DRs based on the DR construction method. Experimental results demonstrate that the DRs containing the proposed energy-efficient terminals can effectively optimize energy-related objectives. © 2023 IEEE.

关键词： Energy utilization

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Geomagnetic Matching Aided Unmanned Surface Vehicle Cooperative Positioning Method 14

Geomagnetic Matching Aided Unmanned Surface Vehicle Cooperat...

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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Shi, Hanzhang Tang, Chengkai Zhang, Lingling Yue, Zhe Liu, Yangyang Dan, Zesheng School of Electronics and Information Northwestern Polytechnical University Xi'an China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9798350366556

Due to the complex maritime environment, satellite navigation is easily interfered by weather, radio, etc., and the error of inertial navigation will accumulate rapidly with the increase of sailing time, the existing cooperative positioning methods cannot meet the accuracy and stability of Unmanned Surface Vehicle (USV) clusters to realize rescue at sea. Aiming at the above problems, this paper proposes a Geomagnetic Matching Cooperative Positioning Method for Unmanned Surface Vehicle Based on Geometric Information (GMCP-GI) for USV clusters based on geometric information. Simulation results show that the proposed g GMCP-GI for USVs can realize the cooperative positioning of USV clusters and can effectively improve the positioning accuracy and stability. © 2024 IEEE.

关键词： Unmanned surface vehicles

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A Novel Comprehensive Similarity Measure for Networked Operation Sequences

A Novel Comprehensive Similarity Measure for Networked Opera...

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2023 China Automation Congress, CAC 2023

作者： Wang, Mengjiao Xu, Binzi Huang, Dengchao Zhang, Yao Wang, Chun Yang, Long School of Electrical Engineering Anhui Polytechnic University Wuhu241000 China School of Computer Science and Technology Huaibei Normal University Huaibei235000 China School of Computer Jiangsu University of Science and Technology Zhenjiang2121000 China

ISBN: (纸本)9798350303759

With the increasing development of knowledge-based technologies in manufacturing, it has been shown as a promising way to solve problems by reusing related knowledge discovered from existing data. When it comes to computer aided process planning (CAPP), however, most efforts have been done on serial operation sequences, ignoring the urgent and practical requirement of networked operation sequences, which can provide more flexible process options. Therefore, we propose a novel comprehensive similarity measure for networked operation sequences in this paper. First, the information embedded in the networked operation sequences is deeply analyzed and classified into four types: operation similarity, precedence relationship, number of common operations, and structure similarity. For each kind of information, an intermediate similarity measure is present considering the specific information need of the networked operation sequence. Then, principal component analysis (PCA) is used to obtain a comprehensive metric without repeatedly calculating the common part of the aforementioned four types of information. Finally, the performance of this proposed measure is evaluated by a numerical illustration, in which a data set of different similarity cases is also built. © 2023 IEEE.

关键词： Knowledge based systems

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A clustered oversampling coupled random forest classification method 2

A clustered oversampling coupled random forest classificatio...

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2022 2nd International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2022

作者： An, Wei-Peng Gao, Feng Shang, Xin-Xin School of Computer Science and Technology Henan Polytechnic University Jiaozuo454002 China School of Accounting Zhengzhou Business University ZhengZhou451400 China

To address the limitations of current classification prediction models, an algorithm DPC-ASMOTE-IRF for Clustering by fast search and find of density peaks (DPC) with adaptive SMOTE (ASMOTE) coupled with improved random forest (IRF) is proposed. The algorithm first introduces in the data processing stage DPC clustering algorithm to cluster the samples, and proposes ASMOTE oversampling technique to process a minority class of samples to get relatively balanced samples. In the algorithm stage, the model's overall classification performance for unbalanced data is improved by assigning weights to each decision tree in the random forest through the classification accuracy. The experimental results show that the classification performance indexes of the proposed method are all improved compared with similar classification experimental models. © Published under licence by IOP Publishing Ltd.

关键词： Clustering algorithms

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Research on apple surface defect detection based on improved YOLOv8 7

Research on apple surface defect detection based on improved...

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7th IEEE Advanced Information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Yang, Hua Zhang, Haifeng Xiao, Jie Wang, Qi Sheng, Shenyang Peng, Chengwu Zhang, Tiancheng Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China Bi Shengyun Information Technology Co. Ltd China

ISBN: (纸本)9798350339161

With the continuous development of artificial intelligence technology, deep learning methods have been widely used in smart agriculture. With the continuous progress of object detection algorithms, it is a future trend to introduce computer vision methods into smart agriculture. This paper proposes an improved YOLOv8 network model for detecting whether apple is still in a healthy state in smart agriculture systems. By introducing a better backbone network EfficientNet, features can be extracted from the data efficiently. In addition, by introducing a novel WIOU calculation function, the rectangular box can be computed better. In this experiment, the average accuracy of the improved YOLOv8-Enet is mAP0.5 and mAP 0.5:0.95, which are 7.1% and 6.5% higher than that of YOLOv8-base, respectively. The proposed YOLOv8-Enet model can effectively detect apple surface defect and provide theoretical and technical support for future research on vision of smart agriculture © 2024 IEEE.

关键词： Object detection

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A Teaching Figure Classification Based on the Fusion of Multiple Modals 4

A Teaching Figure Classification Based on the Fusion of Mult...

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4th International Conference on Information science and Education, ICISE-IE 2023

作者： Jin, Guanghao Yang, Jianxing Ma, Lei Cui, Yuwei Zhao, Junhua Song, Qingzeng School of Artificial Intelligence Beijing Polytechnic Beijing China School of Computer Science and Technology Tiangong University Tianjin China

ISBN: (纸本)9798350394610

Deep learning-based classification methods can help the teaching system automatically collect figure samples while the samples from different sources cause the difficulty of the classification. To solve this problem, we use multiple models to increase the accuracy of the classification. In more details, we pretrain the deep learning models on some public datasets. Then, we continuously train these models on teaching figure datasets. On a figure sample, we firstly classify the dataset that may contain this sample. After we predicted the dataset, we apply a weighted fusion method to classify the label of this sample. As the experimental results shows, our methods can achieve higher accuracy than the existing ones. © 2023 IEEE.

关键词： Classification (of information)

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FL4C^2: A Trustworthy Intelligent Cloud Computing for Private Machine Learning 2

FL4C^2: A Trustworthy Intelligent Cloud Computing for Privat...

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2nd International Conference on Applied Statistics, Computational Mathematics, and Software Engineering, ASCMSE 2023

作者： Yang, He An, Jian He, Xin Shen, Yuhao School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China School of Software Henan University Kaifeng475001 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

The cloud operating system plays a crucial role in supporting machine learning tasks by offering powerful computing capabilities and abundant resources. This enables the implementation of various complex applications, including health monitoring and noise pollution assessment. However, this paradigm involves centralized data storage and processing on a central server, which raises significant data security concerns. To tackle these security issues, we propose a state-of-the-art framework called FL4C^2, which amalgamates federated learning (FL) and cloud computing (CC) eliminating their inherent drawbacks. Specifically, FL4C^2 allows users to collaboratively learn a shared model by solely submitting model parameters to the server, which ensures that user data remains localized and private, thereby preventing any potential data leakage. Moreover, FL4C^2 incorporates an encryption parameter transmission mechanism, which safeguards against indirect data leakage resulting from sharing the model parameters. Simultaneously, we employ an anonymity strategy to generate verifiable anonymous identities, which enhances identity privacy protection. Extensive experimental results based on the KunPeng operating system demonstrate that FL4C^2 achieves superior performance while effectively preserving user privacy. © 2023 SPIE.

关键词： Cloud computing

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