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  • Open Access


    Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

    Yang Yang1, Yuhan Long1, Yijing Lin2, Zhipeng Gao1, Lanlan Rui1, Peng Yu1,3,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250

    Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the… More >

  • Open Access


    A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

    Ling Wang, Jingchi Jiang*, Jingwen Song, Jie Liu

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 833-848, 2023, DOI:10.32604/iasc.2023.036402

    Abstract It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text. However, only some labeled data for agricultural knowledge graph domain training are available. Furthermore, labeling is costly due to the need for more data openness and standardization. This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction. Knowledge distillation between the target and source data domain is performed, where Bi-LSTM and CRF models are constructed for entity recognition. The experimental result is shown… More >

  • Open Access


    Eye Strain Detection During Online Learning

    Le Quang Thao1,2,*, Duong Duc Cuong2, Vu Manh Hung3, Le Thanh Vinh3, Doan Trong Nghia4, Dinh Ha Hai3, Nguyen Nhan Nhi3

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3517-3530, 2023, DOI:10.32604/iasc.2023.031026

    Abstract The recent outbreak of the coronavirus disease of 2019 (Covid-19) has been causing many disruptions among the education systems worldwide, most of them due to the abrupt transition to online learning. The sudden upsurge in digital electronic devices usage, namely personal computers, laptops, tablets and smartphones is unprecedented, which leads to a new wave of both mental and physical health problems among students, for example eye-related illnesses. The overexposure to electronic devices, extended screen time usage and lack of outdoor sunlight have put a consequential strain on the student’s ophthalmic health because of their young age and a relative lack… More >

  • Open Access


    Motion Enhanced Model Based on High-Level Spatial Features

    Yang Wu1, Lei Guo1, Xiaodong Dai1, Bin Zhang1, Dong-Won Park2, Ming Ma1,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5911-5924, 2022, DOI:10.32604/cmc.2022.031664

    Abstract Action recognition has become a current research hotspot in computer vision. Compared to other deep learning methods, Two-stream convolutional network structure achieves better performance in action recognition, which divides the network into spatial and temporal streams, using video frame images as well as dense optical streams in the network, respectively, to obtain the category labels. However, the two-stream network has some drawbacks, i.e., using dense optical flow as the input of the temporal stream, which is computationally expensive and extremely time-consuming for the current extraction algorithm and cannot meet the requirements of real-time tasks. In this paper, instead of the… More >

  • Open Access


    A Method Based on Knowledge Distillation for Fish School Stress State Recognition in Intensive Aquaculture

    Siyuan Mei1,2, Yingyi Chen1,2,*, Hanxiang Qin1,2, Huihui Yu3, Daoliang Li1,2, Boyang Sun1,2, Ling Yang1,2, Yeqi Liu1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1315-1335, 2022, DOI:10.32604/cmes.2022.019378

    Abstract Fish behavior analysis for recognizing stress is very important for fish welfare and production management in aquaculture. Recent advances have been made in fish behavior analysis based on deep learning. However, most existing methods with top performance rely on considerable memory and computational resources, which is impractical in the real-world scenario. In order to overcome the limitations of these methods, a new method based on knowledge distillation is proposed to identify the stress states of fish schools. The knowledge distillation architecture transfers additional inter-class information via a mixed relative loss function, and it forces a lightweight network (GhostNet) to mimic… More >

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