Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model by Xinyun WU, Jiafei CHEN, Caiquan XIONG, Donghua LIU, Xiang WAN, Zexi CHEN

“…To address this issue, this study introduces a method consisting of temporal convolutional network (TCN), convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) to predict vessel trajectories, called TCC. …”
Get full text

CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
Get full text

TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency by Lesia Mochurad, Roman Levkovych

“…In this work, a new model is proposed that combines the advantages of Temporal Convolutional Networks (TCN) and Quasi-Recurrent Neural Networks (QRNN) for energy consumption forecasting. …”
Get full text

Driving Behavior Classification Using a ConvLSTM by Alberto Pingo, João Castro, Paulo Loureiro, Sílvio Mendes, Anabela Bernardino, Rolando Miragaia, Iryna Husyeva

“…This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). …”
Get full text

TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition by Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, Yi-Wei Chen, Hui-Fuang Ng, Timothy K. Shih

“…To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. …”
Get full text

Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach by Junlong Li, Lurui Fang, Xiangyu Wei, Mengqiu Fang, Yue Xiang, Peipei You, Chao Zhang, Chenghong Gu

“…Then, it adopts the rough forecasting results and accessible data for all LV networks within an MV network to train the convolutional neural networks and gated recurrent unit (CNN‐GRU) network. …”
Get full text

Resilience driven EV coordination in multiple microgrids using distributed deep reinforcement learning by Yuxin Wu, Ting Cai, Xiaoli Li

“…The proposed method applies an architecture with multi-actor, single-learner to reduce training complexity, employing a convolutional neural network to capture spatial characteristics from the CPTN, and incorporating a long short-term memory to derive temporal sequence features across multiple time steps, thereby enhancing the exploration efficiency of the action space. …”
Get full text

An interpretable deep learning framework for medical diagnosis using spectrogram analysis by Shagufta Henna, Juan Miguel Lopez Alcaraz, Upaka Rathnayake, Mohamed Amjath

“…Convolutional Neural Networks (CNNs) are widely utilized for their robust feature extraction capabilities, particularly in medical classification tasks. …”
Get full text

Upper limb human-exoskeleton system motion state classification based on semg: application of CNN-BiLSTM-attention model by Dongwei Zhao, Xiangming Ye, Song Wang, Chenfeng Zhang, Shouqian Sun, Xuequn Zhang, Ruidong Cheng

“…We propose a deep learning model combining convolutional neural networks, bidirectional long short-term memory networks, and attention mechanism to enhance the accuracy of motion state recognition in complex scenarios. …”
Get full text

An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…Extensive experiments are conducted to assess the model’s performance compared to several state-of-the-art techniques, including standard GAN, convolutional neural network (CNN), deep belief network (DBN), time-series transformer (TST), bidirectional encoder representations from transformers (BERT), and extreme gradient boosting (XGBoost). …”
Get full text

A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. …”
Get full text

A multimodal deep learning model with differential evolution-based optimized features for classification of power quality disturbances by Md Nurul Islam

“…The selective important features are given to a multimodal deep learning model built using multiple layers of convolution neural network, long short-term memory and deep neural network to extract and infuse the deep spatial and temporal features of the signals before being fed to the SoftMax neural network for classification task. …”
Get full text

Semantic Segmentation of Rice Fields in Sub-Meter Satellite Imagery Using an HRNet-CA-Enhanced DeepLabV3+ Framework by Yifan Shao, Pan Pan, Hongxin Zhao, Jiale Li, Guoping Yu, Guomin Zhou, Jianhua Zhang

“…The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. …”
Get full text

Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.…”
Get full text

Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting by Chris Lochhead, Robert B. Fisher

“…To address this applicational barrier, an end-to-end pipeline is introduced here for creating graph feature embeddings, generated using a bespoke Spatio-temporal Graph Convolutional Network and per-joint Principal Component Analysis. …”
Get full text

Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation an... by Aoyang Bai, Hongyun Song, Yan Wu, Shurong Dong, Gang Feng, Hao Jin

“…To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention Transformer Network), an advanced neural network specifically tailored for multichannel temporal tasks. …”
Get full text

A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

“…Second, considering the spatiotemporal correlation coupling and quality inheritance characteristics of complex manufacturing processes, to better utilize the optimal feature subset and fully explore the deep information within and between different procedures, this study proposes a three-dimensional (feature-time-procedure) sample space representation method. Temporal-spatial feature extraction is performed using a residual network, and the final quality prediction value is obtained through local-global feature fusion. …”
Get full text

Intelligent identification of ballastless track subgrade settlement based on vehicle-rail vibration data by Chong Li, Yu Guo

“…By leveraging the convolutional neural network (CNN)’s capability to extract spatial features and the long short-term memory (LSTM)’s strength in capturing temporal dependencies, the hybrid network effectively models the relationships between dynamic indicators and subgrade settlement. …”
Get full text

Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Subsequently, the BiLSTM network established temporal dependencies of deformation data from both forward and backward directions, effectively capturing long-term dependencies in dam deformation processes through its dual-directional temporal modeling capability. …”
Get full text

Performance degradation assessment method for linear motor feed systems driven by digital twins by Zeqing Yang, Yiding Yao, Wei Cui, Yingshu Chen, Beibei Liu, Yi Jin, Yanrui Zhang, Hongwei Zhao, Guofeng Zhang, Wei Yi, Zonghua Zhang

“…A current simulation dataset for both normal and demagnetization degradation conditions is constructed.In response to the challenges posed by the temporal dependence of operational data and the difficulty of identifying degradation states, and leveraging the superior data transformation capabilities of Gramian Angular Field (GAF), which effectively preserves temporal information, this study proposes a performance degradation assessment model combining GAF encoding with the AlexNet convolutional neural network. …”
Get full text