Safety Supervision Method for Power Operation Site Based on Machine Learning and Image Recognition by Zhengwei CHANG, Qian PENG, Ying CHEN

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Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning by Runzi LIU, Tianci MA, Weihua WU, Chenhong YAO, Qinghai YANG

“…In recent years, with the increasing number of various emergency tasks, how to control the impact on common tasks while ensuring system revenue has become a huge challenge for the dynamic scheduling of relay satellite networks.Aiming at this problem, with the goal of maximizing the total revenue of emergency tasks and minimizing the damage to common tasks, a dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning was proposed.Specifically, in order to take into account the long-term and short-term performance of the system at the same time, a two-layer scheduling framework implemented by upper-level and lower-level DQN was designed.The upper-level DQN was responsible for determining the temporary optimization goal based on long-term performance, and the lower-level DQN determined the scheduling strategy for current task according to the optimization goal.Simulation results show that compared with traditional deep learning methods and the heuristic methods dealing with dynamic scheduling problems, the proposed method can improve the total revenue of urgent tasks while reducing the damage to common tasks.…”
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EFL Lecturers’ Method for Synchronous and Asynchronous Learning through Moodle on Intensive Reading Course by Estika Satriani, Andi Idayani, Destry Pryanti Kadar

“… This research investigates intensive reading instruction at Universitas Islam Riau, Indonesia, using Moodle-based synchronous and asynchronous methods. The qualitative approach was carried out through classroom observation involving 67 students and conducting interviews with 10 students. …”
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Low-Damage Grasp Method for Plug Seedlings Based on Machine Vision and Deep Learning by Fengwei Yuan, Gengzhen Ren, Zhang Xiao, Erjie Sun, Guoning Ma, Shuaiyin Chen, Zhenlong Li, Zhenhong Zou, Xiangjiang Wang

“…Targeting the problem of high damage rate during transplantation of plug seedlings, we have proposed an adaptive grasp method based on machine vision and deep learning, and designed a lightweight real-time grasp detection network (LRGN). …”
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Improved Patch-Mix Transformer and Contrastive Learning Method for Sound Classification in Noisy Environments by Xu Chen, Mei Wang, Ruixiang Kan, Hongbing Qiu

“…The structural influence of urban noise on audio signals complicates feature extraction and audio classification for environmental sound classification methods. To address these challenges, this paper proposes a Contrastive Learning-based Audio Spectrogram Transformer (CL-Transformer) that incorporates a Patch-Mix mechanism and adaptive contrastive learning strategies while simultaneously improving and utilizing adaptive data augmentation techniques for model training. …”
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Method of adaptive knowledge distillation from multi-teacher to student deep learning models by Oleksandr Chaban, Eduard Manziuk, Pavlo Radiuk

“… Transferring knowledge from multiple teacher models to a compact stu-dent model is often hindered by domain shifts between datasets and a scarcity of labeled target data, degrading performance. While existing methods address parts of this problem, a unified framework is lacking. …”
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Deep Learning Image Compression Method Based On Efficient Channel-Time Attention Module by Xiu Ji, Xiao Yang, Zheyu Yue, Hongliu Yang, Boyang Zheng

“…However, traditional image compression methods face significant limitations in both quality and efficiency when applied to high-resolution imagery in such scenarios.To address these challenges, this paper proposes a deep learning–based image compression approach incorporating an Efficient Channel-Temporal Attention Module (ETAM). …”
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GMTBLC: a deep learning-based bi-modal network traffic classification method by WEI Debin, JIANG Qinlong, WEN Jinglong, WANG Xinrui

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Effect of Cooperative Learning Method Anchored on Multiple Intelligence Theory on Students' Achievement in Mathematics by May Ann Bague Valencerina

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Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach by Hai-Bang Ly, Thuy-Anh Nguyen, Binh Thai Pham

“…Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. …”
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Interval Prediction Method for Solar Radiation Based on Kernel Density Estimation and Machine Learning by Meiyan Zhao, Yuhu Zhang, Tao Hu, Peng Wang

“…Then, two interval prediction methods are developed by introducing the KDE to out-of-bag (OOB), introducing kernel density estimation (KDE) to split conformal (SC) based on the three machine learning models. …”
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An Improved Decline Curve Analysis Method via Ensemble Learning for Shale Gas Reservoirs by Yu Zhou, Zaixun Gu, Changyu He, Junwen Yang, Jian Xiong

“…To overcome these limitations, this study proposes an Improved DCA method that integrates multiple base empirical DCA models through ensemble learning. …”
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A New Incremental Learning Method Based on Rainbow Memory for Fault Diagnosis of AUV by Ying Li, Yuxing Ye, Zhiwei Zhang, Long Wen

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A Cross-Modal Emergency Recognition Method Integrating Attentional Collaboration and Contrastive Learning by HUANG Shaonian, PENG Yongtao, WEN Peiran, LIU Yao

“…To address the challenges of image complexity, limited textual information, and inter-modal misleading data in cross-modal emergency recognition, this paper proposes a novel fusion recognition method that integrates attentional collaboration and contrastive learning. …”
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Human action recognition method based on multi-view semi-supervised ensemble learning by Shengnan CHEN, Xinmin FAN

“…Mass labeled data are hard to get in mobile devices.Inadequate training leads to bad performance of classifiers in human action recognition.To tackle this problem, a multi-view semi-supervised ensemble learning method was proposed.First, data of two different inertial sensors was used to construct two feature views.Two feature views and two base classifiers were combined to construct co-training framework.Then, the confidence degree was redefined in multi-class task and was combined with active learning method to control predict pseudo-label result in each iteration.Finally, extended training data was used as input to train LightGBM.Experiments show that the method has good performance in precision rate, recall rate and F1 value, which can effectively detect different human action.…”
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Extensive identification of landslide boundaries using remote sensing images and deep learning method by Chang-dong Li, Peng-fei Feng, Xi-hui Jiang, Shuang Zhang, Jie Meng, Bing-chen Li

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Action-Oriented Deep Reinforcement Learning Method for Precast Concrete Component Production Scheduling by Rongzheng Yang, Shuangshuang Xu, Hao Li, Hao Zhu, Hongyu Zhao, Xiangyu Wang

“…To address these, this study proposes an action-oriented reinforcement learning (AO-DRL) method aimed at minimizing the maximum completion time. …”
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Retracted: Research on intelligent elevation control method of shearer drum based on deep learning by Pu Zhang

“…Abstract Retraction: [Pu Zhang, Research on intelligent elevation control method of shearer drum based on deep learning, IET Software 2023 (https://doi.org/10.1049/sfw2.12089)]. …”
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Development of the program "Method of projects" in the organization of independent work of schoolchildren at the senior stage of learning by Natalia V. Startseva

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Retracted: Sharing Method of Online Teaching Resources of Spoken English Based on Deep Learning by Journal of Electrical and Computer Engineering

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