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2023 International Conference on Non-Traditional Machining and Intelligent Manufacturing, NTMIM 2023

作者： Li, Jingyu Han, Mingxu Dong, Guangyu Wei, Huimei Li, Yao School of Information Science and Technology Dalian Institute of Science and Technology Liaoning Dalian China School of Computer Engineering City Institute Dalian University of Technology Liaoning China

Artificial intelligence has been gradually integrated into life, and knowledge graph is also particularly important, and its application is gradually maturing. In recent years researchers have used the rich entity and relationship information in the knowledge graph to overcome many difficulties. Through the fusion, reasoning, and deep learning of large-scale knowledge graphs, knowledge graphs link and apply these memories to generate wisdom. It can be said that knowledge graph has become the infrastructure of the era of artificial intelligence. The article combines Neo4j with ECharts visualization to develop a visual question answering robot in the medical field. This uses a more scientific method to understand and present data, provides a humanized visual question answering robot. And finally shows the effect of visual knowledge graph as well as assists users in the analysis of specific diseases. © Published under licence by IOP Publishing Ltd.

关键词： Knowledge graph

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Smart-City-based Data Fusion Algorithm for Internet of Things

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computers, Materials & Continua 2022年第11期73卷 2407-2421页

作者： Jawad Khan Muhammad Amir Khan N.Z.Jhanjhi Mamoona Humayun Abdullah Alourani Department of Computer Science IIC University of TechnologyPhnom PenhCambodia Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan School of Computer Science and Engineering SCETaylor’s UniversitySubang JayaSelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf Universityal-JoufKSA Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Increasingly,Wireless Sensor Networks(WSNs)are contributing enormous amounts of *** the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to *** wireless sensor nodes,a Smart City environment may now be shown for the benefit of *** Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and ***,Quality of Service(QoS)and poor data performance are common problems in WSNs,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion *** our proposed research,a WSN multisensor data fusion technique employing fuzzy logic for event *** the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall *** network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting *** proposed algorithm performs well in fusion,according to the results of the *** a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.

关键词： Internet of things data fusion wireless sensor network fuzzy inference system

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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A Federated Class Incremental Learning Framework for Edge Intelligence 10

A Federated Class Incremental Learning Framework for Edge In...

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10th IEEE Smart World Congress, SWC 2024

作者： Chen, Yuxian Zhang, Ying Ren, Wei Guo, Bin Yu, Zhiwen Northwestern Polytechnical University School of Computer Science Xi'an China Mapping Science and Geospatial Information Technology of Mnr Casm State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying Beijing China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798331520861

With the advancement of deep learning, artificial intelligence has garnered significant attention in the field of edge computing. However, the characteristics of edge devices present two major challenges for the application of deep neural networks: the conflict between high accuracy and strong data privacy requirements, and the conflict between the high consumption of computing resources and the low performance of edge devices. To address these challenges, this paper proposes a federated class-incremental learning framework for edge intelligence. Based on the varying consumption of computing and storage resources, the scheme divides the training process of the complete federated class-incremental model into three stages, which are executed at different locations. The pre-training of the basic model and the aggregation of global parameters are carried out on the central cloud server with strong computing power, while analytic incremental learning is introduced on the edge server to reduce the computation and storage load during model updates. This scheme offers three advantages: continuous updating capabilities of the model, low resource consumption on edge devices, and a robust data privacy protection mechanism for edge devices. Performance analysis results from experiments demonstrate the reliability of this method. © 2024 IEEE.

关键词： Federated learning

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Prediction of environmental factors in seafood cold chain transportation based on IAGA-ELM algorithm

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International Journal of Simulation and Process Modelling 2023年第3期20卷 159-171页

作者： Xu, E. Jin, Yujia Chen, Yinong Zhao, Shenghui Wang, Yang School of Information Science and Technology Bohai University Liaoning Province Jinzhou121013 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China School of Information Science and Technology Bohai University Liaoning Jinzhou121013 China School of Computing and Augmented Intelligence Arizona State University TempeAZ85287 United States

Cold chain transportation is often used to ensure the quality of seafood. In order to reduce the impact of environmental factors on product quality during cold chain transportation, an environmental factor prediction method based on improved adaptive genetic algorithm (IAGA) and extreme learning machine (ELM) is developed to predict the environmental factors generated in cold chain transportation. The environmental factor data composed of the gases released in the decay mechanism of cold fresh seafood is collected for training ELM with fast training speed and strong generalisation ability. Furthermore, the prediction model of environmental factors in the process of seafood cold chain transportation is established. Because initial connection weights and threshold values of traditional ELM have the characteristics of randomness, IAGA is used to address this issue. Compared with the existing ELM, GA-ELM, AGA-ELM and other models, our experimental results show that the proposed model obtains higher prediction accuracy and lower error rate. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Forecasting

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Low-cost Granary Vehicles Positioning Using a Distributed LED Network 24

Low-cost Granary Vehicles Positioning Using a Distributed LE...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Liu, Jingya Ma, Pengtao Zhang, Lingmin Geng, Yu Yan, Lei Northeastern University School of Computer and Communication Engineering Qinhuangdao066004 China Hebei Normal University of Science and Technology Institute of Mathematics and Information Technology Qinhuangdao066004 China

ISBN: (纸本)9798350363760

To address the safety hazards and inefficiency of traditional manual grain depot management, the application of granary vehicles has emerged as a promising solution. However, the absence of GPS signals indoors and the cumulative errors inherent in inertial navigation system (INS) make it difficult for vehicles to achieve the required positioning accuracy for operations. To achieve accurate positioning in the grain depot work area, this paper proposes a distributed auxiliary positioning scheme based on a light emitting diode (LED) network, combined with a attitude angle-oriented variable state dimension (AAVSD)-Kalman filter to achieve low-cost and high-precision positioning. The scheme selects the positioning method according to the vehicle's attitude angle: LED network positioning is used when the attitude angle is within the certain threshold range. Simulation results show that the proposed scheme effectively solves the problem of INS cumulative error and significantly improves the positioning accuracy of granary vehicles without excessive budget. © 2024 IEEE.

关键词： Budget control

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Symbolic Regression Using Genetic Programming with Chaotic Method-Based Probability Mappings 13th

Symbolic Regression Using Genetic Programming with Chaotic ...

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13th International Conference on Frontier Computing, FC 2023

作者： Cao, Pu Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu965-8580 Japan Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819993413

In this study, we propose a novel pre-learning approach for genetic programming (GP) that aims to investigate the effect of the probability of being selected for each operator. Furthermore, we present a technique that combines chaos theory and searches for a relatively good possibility mapping for each operator using one-dimensional chaotic mapping. We conducted several sets of comparative experiments on real-world data to test the viability of the proposal. These experiments included comparisons with conventional GP, examination of the impact of various chaotic mappings on the proposed algorithm, and implementation of different optimization strategies to find the relative optimal probability mapping. The experimental results demonstrate that the proposed method can achieve better results than conventional GP in the tested dataset, without considering the total quantitative calculation amount. Through statistical tests, it has been proven that the proposed method is significantly different from the conventional method. However, the discussion regarding the circumstances under which the proposed method can obtain better results when the total calculation amount is limited is not yet fully explored due to the small-scale nature of the experiments. Our future studies will focus on improving and fully discussing this idea. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic programming

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Rotating machinery fault diagnosis based on residual shrinking convolution and GSoP attention mechanism

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Zhendong yu Chongji/Journal of Vibration and Shock 2025年第13期44卷 277-287页

作者： Liu, Baoluo Li, Chen Nie, Yalin Wang, Guoyong School of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang471000 China

To address the challenges of extracting effective features from vibration signals in environments with strong background noise and variable load conditions, which results in low fault diagnosis accuracy and poor generalization ability, a rotating machinery fault diagnosis method is proposed that combines residual shrinkage convolution and the Global Second-Order Pooling (GSoP) attention mechanism. It constructs residual shrinkage convolution using soft thresholding filtering technology with multichannel, multi-scale convolution, and integrates attention factors to the filtered signals after soft thresholding filters to suppress irrelevant features and enhance effective features. Furthermore, leveraging high-order statistical modeling concepts, the GSoP attention mechanism is added after the residual shrinkage convolution layer, enhancing the model's ability to extract discriminative features through the second-order statistical information of high-level feature maps. Finally, testing experiments were conducted using the Case Western Reserve University bearing dataset and the University of Connecticut gearbox dataset. The proposed method achieved diagnostic accuracies of 98.87% and 99.64% under 6dB SNR conditions, respectively. Under varying noise and load conditions, the diagnostic performance of the proposed method outperformed that of the comparison models. The experimental results indicate that the proposed method shows good fault identification ability and generalization capability in complex operating environments. © 2025 Chinese Vibration engineering Society. All rights reserved.

关键词： Rotating machinery

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Latha Mary, S Angel Ulagapriya, K. Poonguzhali, A. Menaha, R. David, Beaulah Priyadharshini, T.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350334050

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection. © 2023 IEEE.

关键词： Optical radar

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Prediction of Web Page Relevance in Focused Crawling Using Artificial Neural Networks 3

Prediction of Web Page Relevance in Focused Crawling Using A...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Dhabliya, Dharmesh Alawadi, Ahmed Hussein Varkale, Akshay Aljanabi, Mohammad Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Deportment of Computer Science & Engineering Bhopal India Alirqia University Department of Computer Baghdad Iraq

ISBN: (纸本)9798350342338

In the rapidly expanding digital ecosystem, efficient web crawling is paramount to extract relevant information from the vast expanse of the internet. This research addresses the challenge of predicting web page relevance in focused crawling using Artificial Neural Networks (ANNs). The study leverages a curated dataset of web pages, annotated for their relevance to specific domains. A multi-layer perceptron architecture was employed for the ANN, with features extracted from the web pages serving as inputs. The model was trained and validated using a split of 70% training and 30% testing data. Experimental results indicate that the ANN achieved an accuracy of 93.20%, outperforming traditional methods by a margin of 8%. This demonstrates the potential of ANNs in enhancing the efficiency of focused web crawlers, ensuring more accurate data extraction in less time. The findings pave the way for integrating deep learning techniques into web crawling systems, optimizing them for the evolving digital landscape. © 2023 IEEE.

关键词： Artificial Neural Networks Data Extraction Deep Learning Techniques Focused Crawling Web Page Relevance

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