Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model by Haotian Guo, Keng-Weng Lao, Junkun Hao, Xiaorui Hu

“…Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power.…”
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Mixed Layer Depth Estimation From Multisource Remote Sensing Data Using Clustering-Machine Learning Method by Zengxin Guan, Kaijun Ren, Senliang Bao, Hengqian Yan, Huizan Wang, Yanlai Zhao, Jianbin Liu

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Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning by Yinglin Yang, Xin Zhang, Jianwei Yin, Xiangyang Yu

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A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images by Touba torabipour, Abolfazl Gandomi, Mohammad Ghanimi

“…The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. …”
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Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements by Evgenii Kanin, Alsu Garipova, Sergei Boronin, Vladimir Vanovskiy, Albert Vainshtein, Andrey Afanasyev, Andrei Osiptsov, Evgeny Burnaev

“…This research advances existing geostatistical interpolation techniques by fusing well logging and well test data to build the reservoir permeability map through an optimization framework coupled with machine learning. Unlike traditional variogram-based geostatistical simulation algorithms, our method provides a permeability distribution that is hydrodynamically similar to the actual one, enhancing initial guess in the history matching process. …”
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Bridge Geometric Shape Measurement Using LiDAR–Camera Fusion Mapping and Learning-Based Segmentation Method by Shang Jiang, Yifan Yang, Siyang Gu, Jiahui Li, Yingyan Hou

“…This study proposes a novel framework that utilizes an airborne LiDAR–camera fusion system for data acquisition, reconstructs high-precision 3D bridge models through real-time mapping, and automatically extracts structural geometric shapes using deep learning. The main contributions include the following: (1) A synchronized LiDAR–camera fusion system integrated with an unmanned aerial vehicle (UAV) and a microprocessor was developed, enabling the flexible and large-scale acquisition of bridge images and point clouds; (2) A multi-sensor fusion mapping method coupling visual-inertial odometry (VIO) and Li-DAR-inertial odometry (LIO) was implemented to construct 3D bridge point clouds in real time robustly; and (3) An instance segmentation network-based approach was proposed to detect key structural components in images, with detected geometric shapes projected from image coordinates to 3D space using LiDAR–camera calibration parameters, addressing challenges in automated large-scale point cloud analysis. …”
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Application of mind mapping combined with the teaching method of problem-based learning in undergraduate teaching of clinical dermatology and venereology by JIANG Shui, YU Bo, WANG Jun, CHEN Guanzhi, DING Kun

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Nonlinear Resilient Learning Method Based on Joint Time-Frequency Image Analysis in Underwater Visible Light Communication by Hui Chen, Yiheng Zhao, Fangchen Hu, Nan Chi

“…Unlike the traditional deep neural network (DNN) based post equalizers which merely consider the time domain, the proposed TFDNet exploits time-frequency image analysis which considers the time and frequency domains simultaneously and transforms the signal into 2D time-frequency image, which is further learned by neural network. Experimental results demonstrate that TFDNet outperforms Volterra and DNN based methods for compensating nonlinear distortions through a 1.2 m underwater channel using 64 quadrature amplitude modulation-carrierless amplitude modulation (64QAM-CAP). …”
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METHOD FOR JOINT OPTIMIZATION FEEDFORWARD ARTIFICIAL NEURAL NETWORKS WEIGHTS AND STRUCTURE IN DEEP MULTI-AGENT REINFORCEMENT LEARNING by V. I. Petrenko

“…Increasing the intelligence of tasks solved using mobile cyber-physical systems (MCPS) requires the use of artificial neural networks (ANNs) and methods of multi-agent deep reinforcement learning (MDRL).…”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…Therefore, this paper takes the star-rated hotels in the six districts of Tianjin as the research subject and proposes a few-shot hotel location prediction method based on meta-learning algorithms and transportation accessibility. …”
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Energy management method of integrated energy system based on energy and carbon pricing strategy and reinforcement learning approach by Hao Zhang, Xuntao Shi, Hai Lu, Enbo Luo, Yuanlong Zhang, Kairan Li, Tong Liu, Min Xu

“…Focusing on the low-carbon economic operation of an integrated energy system (IES), this paper proposes a novel energy-carbon pricing and energy management method to promote carbon emission reductions in the IES based on the carbon emission flow theory and reinforcement learning (RL) approach. …”
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Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine by FU Qiang, HU Dong, YANG Tongliang, LUO Guoqing, TAN Weimin

“…ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly, sparse signals were obtained through threshold processing of transformed domain signals. …”
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Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine by FU Qiang, HU Dong, YANG Tongliang, LUO Guoqing, TAN Weimin

“…The proposed method enables rapid fault diagnosis of bearings. …”
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Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia by Chenxi Cui, Yunfeng Hu, Yuhai Bao, Hao Li

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Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm by Ruirui Zhong, Yixiong Feng, Puyan Li, Xuanyu Wu, Ao Guo, Ansi Zhang, Chuanjiang Li

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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

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A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features by Yanlin Shao, Peijin Li, Ran Jing, Yaxiong Shao, Lang Liu, Kunpeng Zhao, Binqing Gan, Xiaolei Duan, Longfan Li

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The prediction method for ground surface settlement of pipe jacking tunnels based on a spatiotemporal transfer learning network by Hairong Huang, Lian Yuan, Jian Chen, Shixia Zhang

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A Novel Trustworthy Toxic Text Detection Method with Entropy-Oriented Invariant Representation Learning for Portuguese Community by Wenting Fan, Haoyan Song, Jun Zhang

“…With the rapid development of digital technologies, data-driven methods have demonstrated commendable performance in the toxic text detection task. …”
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Smartphone Video Imaging Combined with Machine Learning: A Cost-Effective Method for Authenticating Whey Protein Supplements by Xuan Tang, Wenjiao Du, Weiran Song, Weilun Gu, Xiangzeng Kong

“…This study presents a rapid and low-cost method for authenticating sports whey protein supplements using smartphone video imaging (SVI) combined with machine learning. …”
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