A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects by Rui Li, Xuanwen Yang, Jun Lou, Junsong Zhang

“…To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN). To capture high-level information distributed in time, spatial, and frequency domains, TSGCN considers both neural oscillation changes in different time windows and topological structures between different brain regions. …”
Get full text

Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning by Lingyu Shen, Jianfeng Li, Jingjing Pan, Junpeng Shi, Rui Xu, Hao Wang, Weiming Deng

“…By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. …”
Get full text

Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images by Shadman Q. Salih, Hawre Kh. Abdulla, Zanear Sh. Ahmed, Nigar M. Shafiq Surameery, Rasper Dh. Rashid

“…Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. …”
Get full text

Prediction of Residual Life of Rolling Bearings Based on Multi-Scale Improved Temporal Convolutional Network (MITCN) Model by Keru Xia, Qi Li, Luyuan Han, Zhaohui Ren, Hengfa Luo

“…By introducing a multi-scale expanded causal convolution residual structure, improved temporal convolutional network (ITCN) modules with different expansion factors capture information on different time scales and combine soft threshold functions and channel attention mechanisms to adaptively generate thresholds and eliminate redundant information. …”
Get full text

Real-Time Series Arc Fault Detection and Appliances Classification in AC Networks Based on Competing Convolutional Kernels by Francesco Ferracuti, Riccardo Felicetti, Luca Cavanini, Patrick Schweitzer, Andrea Monteriu

“…This article presents a method to detect and classify series arc faults affecting domestic AC electrical circuits by the analysis of electric current time series data, based on the HYDRA (HYbrid Dictionary-Rocket Architecture) algorithm, a fast dictionary method for time series classification employing competing convolutional kernels. The key novel contributions are twofold: Competing convolutional kernels are suitable to effectively extract features representing an effective set of arc fault detection indicators, and the classification performed in this way is feasible to be executed in real time. …”
Get full text

Detection of degraded forests in Guinea, West Africa, using convolutional neural networks and Sentinel-2 time series by An Vo Quang, An Vo Quang, Nicolas Delbart, Gabriel Jaffrain, Camille Pinet

“…Altogether, the results show that the method is transferable and applicable across different years and among the different Guinean forest regions, such as the Ziama, Diécké, and Nimba massifs. …”
Get full text

Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect by Maria Rugenstein, Senne VanLoon, Elizabeth A. Barnes

“…Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. …”
Get full text

Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory by Wenli JI, Xin DAN, Chenyang MA, Jing CHAI, Yuyi WU, Fengqi QIU, Wentao LIU, Wulin LEI, Yongliang LIU

Get full text

Using a Region-Based Convolutional Neural Network (R-CNN) for Potato Segmentation in a Sorting Process by Jaka Verk, Jernej Hernavs, Simon Klančnik

“…Specifically, Mask R-CNN is implemented and evaluated based on its performance with different parameters in order to achieve the best segmentation results. …”
Get full text

Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results by José Humberto Trueba Perdomo, Ignacio Herrera Aguilar, Francesca Gasparini

“…As a final result, the application shows the signal power value and a spectrogram of the convoluted signal. Moreover, the created application compares different EEG channels at the same time, in a fast and straightforward way, through a time and frequency analysis. …”
Get full text

Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network by Yuta Mizuno, Atsushi Hosoi, Hiroyuki Koshita, Dai Tsunoda, Hiroyuki Kawada

“…Herein, fatigue life prediction was conducted by combining the digital image correlation method, which is a non-destructive testing technique, with a convolutional neural network (CNN), using Xception as the network architecture. …”
Get full text

Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network by Yudong Han, Zhaobo Li, Jiaqi Li

“…In this study, a public participation solution is proposed, and a one-dimensional convolutional neural network (1D-CNN) is introduced to directly process acceleration signals, addressing the limitations of traditional machine-learning classification methods. …”
Get full text

Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images by Zhenyuan Sun, Zhenyuan Sun, Zhenyuan Sun, Zhi Yang, Zhi Yang, Yimin Ding, Yimin Ding, Boyan Sun, Boyan Sun, Saiju Li, Saiju Li, Zhen Guo, Zhen Guo, Lei Zhu, Lei Zhu

“…RC-Dino introduces two innovative components: a novel self-calibrating convolutional layer named RSCconv and an adaptive spatial feature fusion module called ASCFF. …”
Get full text

Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks by Yue Zhang, Yang Yu, Zheng Yang, Qiang Liu

Get full text

Optimizing forest stand aggregation in fragmented stands using graph convolutional networks: A case study in Japan by YangYu You, Hyun Bae Kim, Takuyuki Yoshioka

“…This study proposes a novel approach to forest stand aggregation by integrating Geographic Information Systems (GIS) with Graph Convolutional Networks (GCNs), enabling a data-driven modeling of spatial interactions among forest stands. …”
Get full text

A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data by Wenbo Xuan, Pengchao Chen, Rui Li, Fuxiang Wang, Kuan Fu, Zhitao Wen

“…Secondly, a data block classification algorithm is developed to calculate peak values for segmented differential data, and based on a predefined targeted threshold, distinguish different types of MFL data. Subsequently, based on the distinct data types, targeted one-dimensional fully convolutional autoencoder models are constructed to effectively achieve dimensionality reduction compression and reconstruction of the MFL data. …”
Get full text

Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering by Yaochun Wu, Rongzhen Zhao, Wuyin Jin, Linfeng Deng, Tianjing He, Sencai Ma

“…Training deep neural networks, such as convolutional neural networks (CNNs), require plenty of labeled samples. …”
Get full text

Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers by Umar Islam, Yasser A. Ali, Muna Al-Razgan, Hanif Ullah, Mohmmed Amin Almaiah, Zeeshan Tariq, Khalid Mohammad Wazir

“…The model was trained on a large, expertly annotated set of more than 12,000 ultrasound images across different anatomical planes for effective identification of fetal structures and anomaly detection. …”
Get full text

Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour by Marta Biesok, Jan Juszczyk, Pawel Badura

“…We produce different weight combinations to determine connectivity maps driven by particular image specifics. …”
Get full text

Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor by Sudeep Samanta, Jitendra Nath Bera, Amitava Biswas

“…By employing 14 mother wavelets as convolution filters, the method effectively extracts critical features from stator current signatures, streamlining the fault detection and classification process. …”
Get full text