Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor. by Keerthi Nalliboyina, Sakthivel Ramachandran

“…Artificial neurons with bio-inspired firing patterns have the potential to significantly improve the performance of neural network computing. The most significant component of an artificial neuron circuit is a large amount of energy consumption. …”
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A Hybrid Method for Intrusion Detection in the IOT by Hossein Faghih Aliabadi

“…The new approach uses a hybrid algorithm that includes a convolutional neural network (CNN) to extract general features and long-short-term memory (LSTM) to extract periodic features that are in the form of a layer. are cross-connected, it is introduced to detect penetration. …”
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Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot by Ping Rui, Guifang Qiao, Xiulan Wen, Ying Zhang, Dongxia Wang

“…Secondly，in order to improve the identification accuracy and efficiency of joint stiffness parameters, the BP neural network is applied to fit the stiffness error model to optimize the initial population fitness of genetic algorithm. …”
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Optimization and Prediction of Energy Consumption, Daylighting, and Thermal Comfort of Buildings in Tropical Areas by Jianjian Zhang, Lin Ji

“…Finally, the backpropagation (BP) neural network established in this research is shown to achieve good prediction of the target value and achieves the goal of green energy-saving.…”
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Used economy market insight: Sailboat industry pricing mechanism and regional effects. by Zhanni Huang, Hansheng Hu, Di Wu

“…Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. …”
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Optimal Control of Semiactive Two-Stage Vibration Isolation Systems for Marine Engines by YuanYuan Fang

“…Taking the test results of MR damper dynamic characteristics as sample data, the forward and inverse models of the MR damper are identified by the least square method and neural network (NN) method respectively, and the identification results are applied to semiactive control of the two-stage isolation system. …”
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Multimedia Data Processing Technology and Application Based on Deep Learning by Guo Li, Wei Liu

“…Then, the related network results of deep learning (convolution network structure and countermeasure neural network structure) are put forward, and the image comparison of the activation function and the loss function of deep learning is analyzed, which provides functional algorithm support for the experimental analysis of deep learning in multimedia data processing technology. …”
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Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine by Liu Hao, Xiong Xin, Wang Xiaojing, Guo Jiayu, Shen Jiexi

“…To avoid the problems of being easy to be run into local optimum,parameter adjustment difficulties and a long training process,when conducting the learning process using traditional neural network algorithms,the model for health assessment is constructed by using the RBM as an alternative.Experimental results demonstrate that the proposed method can identify the health status of rolling bearing during the dynamic process of performance degradation with a good engineering applicability.…”
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A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates by Mohammad Zakwan, Majid Niazkar

“…In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP-GRG have been applied to estimate the infiltration rates. …”
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Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN by Zhen Tang, Xiaobin Fu, Pingping Xiao

“…Employing the deep neural network (DNN), an OP intelligent prediction algorithm is proposed. …”
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Prediction of Landslide Displacement Based on EMD-TAR Combined Model by CHEN Xi, GAO Yaping, TU Rui

“…This study aims to more accurately predict the displacement changes of landslides with nonlinear volatility development.The empirical mode decomposition is first employed to process the time series of monitoring surface displacement of a landslide,and then the irregularly changing displacement series is converted into modal components with regular changes,which generates displacement components at different frequencies.Each component is predicted separately so that the mutual influence of errors can be avoided.The comprehensive prediction of the changing trend of displacement series is based on the prediction of the changing trends of all components.The improved threshold autoregressive model able to well describe non-stationary harmonics is used to predict the landslide displacement components.Finally,the modal superposition yields the final predicted displacement.In this way,a combined prediction model based on empirical mode decomposition and threshold autoregressive model is established,and its prediction accuracy is verified with Baishuihe landslide data.Compared with a BP neural network model and a long short-term memory network model,the proposed model has a high prediction accuracy,which provides a new method for the prediction of landslide displacement.…”
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Reliability of Dynamic Kinematic Accuracy for Gear Transmission by Peng Xiao Tiyi Liu Tao Zhang Junfu Peng

“…Then， the four order Runge-Kutta method is used to solve the dynamic response of gear transmission， BP neural network is applied to build the input-output relationship model of dynamic system and motion accuracy reliability model of gear transmission system is then created. …”
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Heston-GA Hybrid Option Pricing Model Based on ResNet50 by Zheng Yang, Liqin Zhang, Xiangxing Tao, Yanting Ji

“…Heston model is combined with ResNet50 convolutional neural network model. Based on the optimization of Heston model parameters by genetic algorithm (GA), ResNet50 model is used to correct the deviation between market option price and Heston price, so a new hybrid option pricing model is established based on the empirical research on the European call options of Huatai-PB CSI 300ETF (code 510300), Harvest CSI 300ETF (code 159919), and SSE 50ETF (code 510050). (3) Results. …”
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Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques by Marwa K. Elteir

“…Despite the success of PLMs in various tasks, our results demonstrate that a combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) layers yields the best performance, achieving a test accuracy of 93.85%.…”
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Weather Radar Image Superresolution Using a Nonlocal Residual Network by Haoxuan Yuan, Qiangyu Zeng, Jianxin He

“…Inspired by the striking performance of the convolutional neural network (CNN) applied in feature extraction and nonlocal self-similarity of weather radar images, we proposed a nonlocal residual network (NLRN) on the basis of CNN. …”
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Lightweight decentralized learning-based automatic modulation classification method by Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI

“…In order to solve the problems in centralized learning, a lightweight decentralized learning-based AMC method was proposed.By the proposed decentralized learning, a global model was trained through local training and model weight sharing, which made full use of the dataset of each communication nodes and avoided the user data leakage.The proposed lightweight network was stacked by a number of different lightweight neural network blocks with a relatively low space complexity and time complexity, and achieved a higher recognition accuracy compared with traditional DL models, which could effectively solve the problems of computing power and storage space limitation of edge devices and high communication overhead in decentralized learning based AMC method.The experimental results show that the classification accuracy of the proposed method is 62.41% based on RadioML.2016.10 A.Compared with centralized learning, the training efficiency is nearly 5 times higher with a slight classification accuracy loss (0.68%).In addition, the experimental results also prove that the deployment of lightweight models can effectively reduce communication overhead in decentralized learning.…”
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FAULT DIAGNOSIS OF ROLLING BEARING BASED ON UNSUPERVISED FEATURE ALIGNMENT by ZHANG Tao, JIA Qian, XIN YueJie

“…The convolutional neural network（CNN） is used to extract vibration signal sensitive fault features, and bi-directional Long short-term Memory is used to extract vibration signal sensitive fault features. …”
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Detection for Dangerous Goods Vehicles in Expressway Service Station Based on Surveillance Videos by Kai Huang, Qinpei Zhao

“…Next, we use a convolutional neural network to detect dangerous goods vehicles from the original images. …”
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Optimization of Temperature-Control Measures for Concrete Structures: A Case Study of the Sluice Project by Yaoying Huang

“…In this paper, we investigate crack prevention in sluice pier concrete as a multiple-factor system optimization problem and investigate an optimization method for temperature-control measures using the uniform design method and a neural network model. The minimum ratios for the internal and surface points of the sluice pier concrete are taken as inputs, and the corresponding combinations of temperature-control parameters based on the uniform design method are taken as outputs. …”
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Analyzing information sharing behaviors during stance formation on COVID-19 vaccination among Japanese Twitter users. by Sho Cho, Shohei Hisamitsu, Hongshan Jin, Masashi Toyoda, Naoki Yoshinaga

“…We constructed a dataset of all Japanese posts mentioning vaccines for five months since the beginning of the vaccination campaign in Japan and carried out a stance detection task for all the users who wrote the posts by training an original deep neural network. Investigating the users' stance formations using this large dataset, it became clear that some neutral users became pro-vaccine, while almost no neutral users became anti-vaccine in Japan. …”
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