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    Design and Experimental Evaluation of a Smart Intra-Row Weed Control System for Open-Field Cabbage by Shenyu Zheng, Xueguan Zhao, Hao Fu, Haoran Tan, Changyuan Zhai, Liping Chen

    Published 2025-01-01
    “…These experimental results verified the system’s adaptability and reliability in complex field environments, providing technical support for the adoption of intelligent mechanical weeding systems. Future research will focus on optimizing control algorithms and feedback mechanisms to enhance the system’s dynamic response capability and adaptability, thereby advancing the development of sustainable agriculture and precision field management.…”
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    Location-aware job scheduling for IoT systems using cloud and fog by Xiaomo Yu, Mingjun Zhu, Menghan Zhu, Xiaomeng Zhou, Long Long, Mahdi khodaparast

    Published 2025-01-01
    “…Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An ''intelligent gateway'' based on fog computing can enhance the efficient utilization of cloud and network resources. …”
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    Classical and machine learning tools for identifying yellow-seeded Brassica napus by fusion of hyperspectral features by Fan Liu, Fang Wang, Zaiqi Zhang, Liang Cao, Jinran Wu, You-Gan Wang

    Published 2025-01-01
    “…Additionally, we implement two intelligent models, random forest and support vector classifier to evaluate features selected through lasso-penalized logistic regression.Results and DiscussionOur findings indicate significant recognition accuracies of 96.55% and 98% for the PLSR and Logit-R models, respectively, aligning closely with the accuracy of previous methods. …”
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    A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching by Chengyao Liu, Fei Dong, Kunpeng Ge, Yuanyuan Tian

    Published 2024-01-01
    “…Third, a new domain adaptation approach, supervised joint matching, is developed to conduct joint feature distribution matching and instance reweighting with the consideration of maximum marginal criterion. The intelligent bearing fault diagnosis model is then trained to predict the labels of the target domain's feature data. …”
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