A METHOD FOR MONITORING RICE SEED LOSS BASED ON WOA-BP ALGORITHM by Jin Chen, Ting Shi, Yaoming Li, Yahui Zhu, Caoyuan Niu

“…Aiming at the problems of the slow response speed and low monitoring accuracy of the existing domestic seed loss rate monitoring models, this paper proposed a rice seed loss rate monitoring method based on the whale optimization algorithm-back propagation neural network (WOA-BP). The loss rate monitoring device consisted of a piezoelectric ceramic sensor module, charge amplification circuit, band-pass filter circuit, analog-to-digital (AD) converter, main control unit, etc. …”
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Multisegment Mapping Network for Massive MIMO Detection by Yongzhi Yu, Jianming Wang, Limin Guo

“…This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). …”
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Research on Mechanical Properties and Parameter Identification of Beam-Column Joint with Gusset Plate Angle Using Experiment and Stochastic Sensitivity Analysis by Xian Dong, Yadi Wang

“…An improved chaotic particle swarm optimization (ICPSO) neural network algorithm was used to study the stochastic sensitivity. …”
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A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira, Jessica N. A. Campbell

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. …”
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Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine by Doddy Prayogo, Yudas Tadeus Teddy Susanto

“…The prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). …”
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Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System by Xiaoli Li, Quanbo Liu, Kang Wang, Fuqiang Wang, Guimei Cui, Yang Li

“…In this article, based on process data sampled from 1000 MW unit flue gas desulphurization system in a coal-fired power plant, a multimodel control strategy with multilayer parallel dynamic neural network (MPDNN) is utilized to address the control problem in the context of different anomalies. …”
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Fault Diagnosis of Train Axle Box Bearing Based on Multifeature Parameters by Xiaofeng Li, Limin Jia, Xin Yang

“…In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. …”
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Multifractal Analysis and Compressive Strength Prediction for Concrete through Acoustic Emission Parameters by Zhiqiang Lv, Annan Jiang, Jiaxu Jin, Xiangfeng Lv

“…SVM prediction results using AE parameters perform higher precision than the artificial neural network (ANN). Furthermore, a significant reduction in sample size uses AE parameters to predict concrete strength.…”
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Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence by Tu T. Nguyen, Hoa Pham Duy, Tung Pham Thanh, Hoang Hiep Vu

“…It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. …”
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Brain-inspired multimodal motion and fine-grained action recognition by Yuening Li, Xiuhua Yang, Changkui Chen

“…These methods struggle particularly with video data containing complex combinations of actions and subtle motion variations.MethodsTypically, they depend on handcrafted feature extractors or simple convolutional neural network (CNN) architectures, which makes effective multimodal fusion challenging. …”
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IoT-based approach to multimodal music emotion recognition by Hanbing Zhao, Ling Jin

“…The proposed CGF-Net model combines a 3D Convolutional Neural Network (3D-CNN), Gated Recurrent Unit (GRU), and Fully Connected Network (FCN). …”
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Fault Diagnosis of Batch Reactor Using Machine Learning Methods by Sujatha Subramanian, Fathima Ghouse, Pappa Natarajan

“…Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. …”
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Adaptive Navigating Control Based on the Parallel Action-Network ADHDP Method for Unmanned Surface Vessel by Zhijian Huang, Xinze Liu, Jiayi Wen, Guichen Zhang, Yihua Liu

“…The adaptive controller adopts a RBF neural network approximation based on the Lyapunov stability analysis to ensure the system stability. …”
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Quantifying Spatiotemporal Dynamics of Solar Radiation over the Northeast China Based on ACO-BPNN Model and Intensity Analysis by Xiangqian Li, Zhijun Tong, Enliang Guo, Xiaolong Luo

“…In this study, a new backpropagation neural network (BPNN) model optimized with an Ant Colony Optimization (ACO) algorithm was developed to generate the ACO-BPNN model, which had demonstrated superior performance for simulating solar radiation compared to traditional BPNN modelling, for Northeast China. …”
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ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China by Chang Juan, Wang Genxu, Mao Tianxu, Sun Xiangyang

“…To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. …”
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Unsupervised Learning in a Ternary SNN Using STDP by Abhinav Gupta, Sneh Saurabh

“…This paper proposes a novel implementation of a ternary Spiking Neural Network (SNN) and investigates it using a hierarchical simulation framework. …”
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Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment by Stefano Curcio, Alessandra Saraceno, Vincenza Calabrò, Gabriele Iorio

“…The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. …”
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Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram by Xu Zhang, Xi Hui, Pengwu Wan, Tengfei Hui, Xiongfei Li

“…Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. …”
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Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder by ZhiDan Zhong, Yao Zhao, AoYu Yang, HaoBo Zhang, DongHao Qiao, ZhiHui Zhang

“…First, the convolutional neural network and the autoencoder model are effectively integrated to construct a one-dimensional convolutional autoencoder model. …”
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Deep learning based gasket fault detection: a CNN approach by S. Arumai Shiney, R. Seetharaman, V. J. Sharmila, S. Prathiba

“…Deep learning algorithms are specific for feature extraction and classification together with a convolutional neural network (CNN) module that allows for seamless connection. …”
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