IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
    Author
    Contributions by role
    Author 1
    Reviewer 21
    Editor 23
    Membership
    ORCID
    0000-0001-6234-1001
    Jinchao Chen
    School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
    Summary
    Dr. Jinchao Chen is an Associate Professor in School of Computer Science at Northwestern Polytechnical University, Xi’an, China. He has received his Ph.D. degree in Computer Science from the same institution in 2016. He focuses on the multi-processor scheduling, embedded and real-time systems, simulation and verification, decision-making and intelligent control of unmanned aerial vehicles, human-computer interaction systems. He has over 50 papers and 4 ESI highly-cited papers published in international conferences and journals (e.g., IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics, IEEE Real-Time Systems Symposium). He is the Editor-in-Chief of ASP Transactions on Computers and ASP Transactions on Computers, and the Academic Editor of International Journal of Aerospace Engineering. He is a TCP member of many conferences and regular reviewer of IEEE Transactions on Industrial Informatics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, and Concurrency and Computation Practice and Experience. He is a member of IEEE and CCF.
    Edited Journals
    IECE Transactions on Internet of Things
    IECE Transactions on Intelligent Unmanned Systems
    IECE Transactions on Computer Science
    IECE Contributions
    Articles
    1
    Edited
    23
    Research Article | 17 October 2023 | Cited: 2
    Forest Fire Assessment and Analysis in Liangshan, Sichuan Province Based on Remote Sensing
    by
    Yiwei Hua
    ,
    Xingdong Wang
    IECE Transactions on Internet of Things | Volume 1, Issue 1: 15-21, 2023 | DOI:10.62762/TIOT.2023.862892
    Abstract
    Because of the special geographical location, dry weather, high temperature and dense vegetation in Liangshan, Sichuan, it is easy to cause forest fires, so it is of great significance to use remote sensing data to evaluate forest fires in Liangshan, Sichuan. In this paper, the forest fire in Muli County, Liangshan, Sichuan Province on March 28th, 2020 was evaluated by using Landsat-8 remote sensing data which can be obtained free of charge. The NDVI of the pre-processed remote sensing images before and after the fire was calculated respectively. After the difference was made, the threshold of the classification of fire and non-fire areas was determined according to the maximum inter-class d... More >
    Graphical Abstract
    Forest Fire Assessment and Analysis in Liangshan, Sichuan Province Based on Remote Sensing
    Research Article | Feature Paper | 12 June 2023 | Cited: 11
    An Improved YOLOv3-Based Method for Immature Apple Detection
    by
    Zhongqiang Huang
    ,
    Ping Zhang
    ,
    Ruigang Liu
    ,
    Dongxu Li
    IECE Transactions on Internet of Things | Volume 1, Issue 1: 9-14, 2023 | DOI:10.62762/TIOT.2023.539452
    Abstract
    The identification of immature apples is a key technical link to realize automatic real-time monitoring of orchards, expert decision-making, and realization of orchard output prediction. In the orchard scene, the reflection caused by light and the color of immature apples are highly similar to the leaves, especially the obscuration and overlap of fruits by leaves and branches, which brings great challenges to the detection of immature apples. This paper proposes an improved YOLOv3 detection method for immature apples in the orchard scene. Use CSPDarknet53 as the backbone network of the model, introduce the CIOU target frame regression mechanism, and combine with the Mosaic algorithm to impro... More >
    Graphical Abstract
    An Improved YOLOv3-Based Method for Immature Apple Detection
    Research Article | Feature Paper | 17 April 2023 | Cited: 11
    Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
    by
    Yujiang Li
    ,
    Jinghua Cao
    IECE Transactions on Internet of Things | Volume 1, Issue 1: 1-8, 2023 | DOI:10.62762/TIOT.2023.564457
    Abstract
    In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introdu... More >
    Graphical Abstract
    Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
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