Multimodal Multiobject Tracking by Fusing Deep Appearance Features and Motion Information by Liwei Zhang, Jiahong Lai, Zenghui Zhang, Zhen Deng, Bingwei He, Yucheng He

“…After that, we use Convolutional Neural Network (CNN) to learn the deep appearance features of objects and employ Kalman Filter to obtain the motion information of objects. …”
Get full text

Rapid maize seed vigor classification using deep learning and hyperspectral imaging techniques by Papis Wongchaisuwat, Pongsan Chakranon, Achitpon Yinpin, Damrongvudhi Onwimol, Kris Wonggasem

“…This study involved acquiring hyperspectral imaging data, preprocessing images, and designing convolutional neural network architectures. We explored various network structures, including one-dimensional (1DCNN), two-dimensional (2DCNN), and three-dimensional convolutional neural networks (3DCNN). …”
Get full text

Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…To verify the effectiveness of the proposed model, we extensively apply it to neural network-based models. We compare and analyze the performance of the proposed model with the comparisons using actual electricity usage data for 4710 households. …”
Get full text

Research on cutting mechanism and process optimization method of gear skiving by Peng Wang, Yuanchao Ni, Xiaoqiang Wu, Jiaxue Ji, Geng Li, Jiahao Wu

“…Furthermore, a prediction model of cutting force and cutting temperature is established using a neural network optimized by genetic algorithm. This prediction model allows for the construction of a multi-objective optimization model for the process parameters. …”
Get full text

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution by Mohamed Khalid AlOmar, Faidhalrahman Khaleel, Abdulwahab Abdulrazaaq AlSaadi, Mohammed Majeed Hameed, Mohammed Abdulhakim AlSaadi, Nadhir Al-Ansari

“…In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. …”
Get full text

Deep Recurrent Model for Server Load and Performance Prediction in Data Center by Zheng Huang, Jiajun Peng, Huijuan Lian, Jie Guo, Weidong Qiu

“…Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. …”
Get full text

Rain removal method for single image of dual-branch joint network based on sparse transformer by Fangfang Qin, Zongpu Jia, Xiaoyan Pang, Shan Zhao

“…Additionally, since tokens with low relevance in the Transformer may influence image recovery, this study introduces a residual sparse Transformer branch (RSTB) to overcome the limitations of the Convolutional Neural Network’s (CNN’s) receptive field. Indeed, RSTB preserves the most valuable self-attention values for the aggregation of features, facilitating high-quality image reconstruction from a global perspective. …”
Get full text

Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea by Maximilian Berthold, Pascal Nieters, Rahel Vortmeyer-Kley

“…We employed generalized additive mixed models to characterize similar blooming patterns and trained an artificial neural network within the Universal Differential Equation framework to learn a differential equation representation of these pattern. …”
Get full text

Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM by Farooq Shaik, D. Vinay Kumar, N. Channa Keshava Naik, G. Radha Krishna, T. M. Yunus Khan, Abdul Saddique Shaik, Abdulrajak Buradi, Addisu Frinjo Emma

“…Abstract The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. …”
Get full text

Balanced coarse-to-fine federated learning for noisy heterogeneous clients by Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang

“…However, heterogeneous clients have different deep neural network structures, and these models have different sensitivity to various noise types, the fixed noise-detection based methods may not be effective for each client. …”
Get full text

FlowMFD: Characterisation and classification of tor traffic using MFD chromatographic features and spatial–temporal modelling by Liukun He, Liangmin Wang, Keyang Cheng, Yifan Xu

“…In addition, FlowMFD utilises a cascaded model with a two‐dimensional convolutional neural network (2D‐CNN) and a bidirectional gated recurrent unit to capture spatial‐temporal dependencies between MFDCF. …”
Get full text

L1 Adaptive Fault-Tolerant Attitude Tracking Control of UAV Systems Subject to Faults and Input Saturation by Yan Zhou, Huiying Liu, Huijuan Guo, Yongjian Chen

“…After the conversion and reorganization of such uncertainties including actuator faults, sensor faults, input saturation, and external disturbances, a nonlinear uncertain system model is developed. Second, a L1 neural network adaptive fault-tolerant controller is designed to deal with uncertainties, where radial basis function neural networks (RBFNNs) are applied to approximate the nonlinear function in the system model. …”
Get full text

Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors by Akiyasu Yamamoto, Akinori Yamanaka, Kazumasa Iida, Yusuke Shimada, Satoshi Hata

“…Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. …”
Get full text

Deep Learning-Based English-Chinese Translation Research by Yao Huang, Yi Xin

“…The problems of gradient disappearance and gradient explosion are easy to occur in the recurrent neural network in the long-distance sequence. The short and long-term memory networks cannot reflect the information weight problems in long-distance sequences. …”
Get full text

Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm by Yanhai Yang, Baitong Qian, Qicheng Xu, Ye Yang

“…The pavement degradation in each climatic zone was related to the climate characteristics of the region. Probabilistic neural network (PNN) and support vector machine (SVM) climate regionalization predictive models were established with MATLAB. …”
Get full text

Prediction of Mine Dust Concentration Based on Grey Markov Model by Zhou Xu, Guo Liwen, Zhang Jiuling, Qin Sijia, Zhu Yi

“…The model was applied to the prediction of mine dust concentration and compared with the prediction results of the BP neural network model, grey prediction model, and ARIMA (1, 2, 1) model. …”
Get full text

Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment by Shifeng Niu, Guiqiang Li

“…From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). …”
Get full text

A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability by Jinglai Sun, Darui Ren, Yu Song, Mingyuan Yu, Zhaofei Chu, Baoguo Liu, Shaogang Li, Xinyang Guo

“…The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. …”
Get full text

AI-Based Screening Method for Early Identification of Invasive Ductal Carcinoma in Breast Cancer by Dominik Jánošík, Sila Yavuz

“…To achieve this, we utilized a deep learning neural network algorithm, employing histopathological microscopic datasets and histological microscopic images from 124 and 576 patients with ductal carcinoma of the breast, respectively. …”
Get full text

Active Vibration Control of the Sting Used in Wind Tunnel: Comparison of Three Control Algorithms by Xing Shen, Yuke Dai, Mingxuan Chen, Lei Zhang, Li Yu

“…This paper details three algorithms, respectively, Classical PD Algorithm, Artificial Neural Network PID (NNPID), and Linear Quadratic Regulator (LQR) Optimal Control Algorithm, which can realize active vibration control of sting used in wind tunnel. …”
Get full text