Clutch Online Adaptive Engagement Control of ISG Type Hybrid Electric Vehicle by Yan Ting, Yang Lin, Chen Liang

“…The clutch model is updated online by Artificial Neural Network and clutch torque is estimated by Kalman Filtering in real time to solve clutch parametric dynamic uncertainty. …”
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Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm by Qicai Zhou, Hehong Shen, Jiong Zhao, Xingchen Liu, Xiaolei Xiong

“…Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. …”
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Pretraining Enhanced RNN Transducer by Junyu Lu, Rongzhong Lian, Di Jiang, Yuanfeng Song, Zhiyang Su, Victor Junqiu Wei, Lin Yang

“…Recurrent neural network transducer (RNN-T) is an important branch of current end-to-end automatic speech recognition (ASR). …”
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A Follow-Up Risk Identification Model Based on Multi-Source Information Fusion by Shuwei Guo, Yunyu Bo, Jie Chen, Yanan Liu, Jiajia Chen, Huimin Ge

“…In Stage 1, a deep feedforward neural network autoencoder reconstructs preprocessed multi-source heterogeneous indicators of human-vehicle-road-environment. …”
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AI based scheme on voice service quality evaluation and assurance by Guangyu LI, Yanping LIANG, Li YU, Kun WANG, Yangming CHEN, Sijia LIU

“…The voice service is one of the mainstream services under the telecommunications network.The mainstream voice service quality evaluation method in the industry is to compare the voice and audio of the transceiver and calculate the POLQA MOS, which is the standard benchmark for voice quality analysis.For telecom operators, perceive voice service quality efficiently without any infringement of user privacy, and carry out precise service quality assurance, is one of the key tasks.A system using mathematical statistics and neural network to learn the mapping between voice service feature data from network side and voice quality from user side to generate evaluation model with high accuracy and precision was proposed.On this basis, the system made use of data information from multiple fields including wireless network user level and cell level, to analyze reasons and work out solutions for poor service quality, so as to assure service quality efficiently and accurately.The result shows that the scheme has achieved good practical application results.…”
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Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader by Qiang Liu, Songyong Liu, Qianjin Dai, Xiao Yu, Daoxiang Teng, Ming Wei

“…In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. …”
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Traffic Prediction of Space-Integrated-Ground Information Network Based on Improved LSTM Algorithm by Chengsheng PAN, Yufu WANG, Li YANG

“…The space-integrated-ground information network is easy to interrupt and the traffi c fl uctuation is not stable due to the problems of high traffi c burst and topological time-varying, which makes the traffi c prediction diffi cult much higher than the ground.In order to solve this problem, an improved LSTM algorithm was put forward.Firstly, the traffi c autocorrelation was judged by analyzd the infl uence of the lag variable of traffi c sequence on the predicted value; Secondly, the noise and breakpoint of the training set were eliminated by replacing the interruption with the predicted value; Finally, Dropout algorithm was used to reduce the impact of noise and neural network over fi tting, and accurately predict the traffi c data of the integrated intelligent network.The simulation results showed that in OPNET simulation environment, compared with other algorithms, the accuracy of this algorithm was improved by 59.21%, and the training speed of the algorithm was improved by 11.11%, which could provide eff ective data support for the overall scheduling of the integrated intelligent network.…”
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Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing by Naveen S. Pagad, Pradeep Nijalingappa, Tulika Chakrabarti, Prasun Chakrabarti, Pugazhenthan Thangaraju

“…Tests have been conducted using the N2C2 corpus, and the proposed methodology achieves a greater than 20% improvement in accuracy over existing neural network-based and transformer-based methods.…”
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Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM by Guangpei Sun, Peng Jiang, Huan Xu, Shanen Yu, Dong Guo, Guang Lin, Hui Wu

“…The LSSVM algorithm increases the fitting accuracy and decreases correction error in comparison with SVM and BP neural network, which provides important references for the implementation of environmental protection measures.…”
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Rapid qualitative and quantitative analysis of strong aroma base liquor based on SPME-MS combined with chemometrics by Zongbao Sun, Junkui Li, Jianfeng Wu, Xiaobo Zou, Chi-Tang Ho, Liming Liang, Xiaojing Yan, Xuan Zhou

“…Results showed that SPME-MS combined with a back-propagation artificial neural network (BPANN) method yielded almost the same recognition performance compared to linear discriminant analysis (LDA) in distinguishing different grades of SABL, with 84% recognition rate for the test set. …”
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The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest by Xiwen Qin, Dingxin Xu, Xiaogang Dong, Xueteng Cui, Siqi Zhang

“…At present, the technology of intelligent identification of bearing mostly relies on deep neural network, which has high requirements for computer equipment and great effort in hyperparameter tuning. …”
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Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN by Jianghua Nie, Yongsheng Xiao, Lizhen Huang, Feng Lv

“…Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. …”
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Dynamics Model Abstraction Scheme Using Radial Basis Functions by Silvia Tolu, Mauricio Vanegas, Rodrigo Agís, Richard Carrillo, Antonio Cañas

“…We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF). Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. …”
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Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation by Qingfeng Huang, Yage Huang, Zhiwei Zhang, Yujie Zhang, Weijian Mi, Chao Mi

“…The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. …”
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An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine by Derya Avci, Akif Dogantekin

“…The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. …”
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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. …”
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Identification of Tree-Related High-Impedance Earth Faults Based on Long-Term Fluctuations in Zero-Sequence Current by Pengwei Wang, Bingyin Xu, Tony Yip, Dong Liang, Guofeng Zou

“…An identification method based on an improved grey wolf optimization probabilistic neural network is constructed. Validation with staged test results demonstrates that the proposed method achieves a success rate of 97.5%, accurately distinguishing THIEFs from other types of HIEF.…”
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Movie scene segmentation using object detection and set theory by Ijaz Ul Haq, Khan Muhammad, Tanveer Hussain, Soonil Kwon, Maleerat Sodanil, Sung Wook Baik, Mi Young Lee

“…In this article, we investigated this problem through a novel intelligent Convolutional Neural Network (CNN) based three folded framework. The first fold segments the input movie into shots, the second fold detects objects in the segmented shots and the third fold performs object-based shots matching for detecting scene boundaries. …”
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Quality of service optimization algorithm based on deep reinforcement learning in software defined network by Cenhuishan LIAO, Junyan CHEN, Guanping LIANG, Xiaolan XIE, Xiaoye LU

“…Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.An algorithm of quality of service optimization algorithm of based on deep reinforcement learning (AQSDRL) was proposed to solve the QoS problem of SDN in the data center network (DCN) applications.AQSDRL introduces the softmax deep double deterministic policy gradient (SD3) algorithm for model training, and a SumTree-based prioritized empirical replay mechanism was used to optimize the SD3 algorithm.The samples with more significant temporal-difference error (TD-error) were extracted with higher probability to train the neural network, effectively improving the convergence speed and stability of the algorithm.The experimental results show that the proposed AQSDRL effectively reduces the network transmission delay and improves the load balancing performance of the network than the existing deep reinforcement learning algorithms.…”
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Intelligent CSI feedback method for fast time-varying FDD massive MIMO system by Yong LIAO, Shuai WANG, Ning SUN

“…In the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the channel state information (CSI) matrix existed noise caused by the wireless channel interference and the time correlation caused by Doppler shift.Because of these effects, the communication system couldn’t guarantee the requirements of reliability and low delay.An intelligent CSI feedback method was adopted.The convolutional neural network (CNN) and batch normalization (BN) network was used to extract the noise in the CSI matrix and learned the spatial structure of the channel.The time correlation between the CSI matrices through the attention mechanism was extracted to improve the accuracy of CSI reconstruction.The data was generated by the standard fast time-varying channel model simulation to train the network offline.System simulation and analysis show that the proposed method can effectively suppress the influence of noise and extract the time correlation caused by Doppler.Compared with the traditional CSI compression feedback algorithm and CsiNet algorithm, the proposed method has better NMSE and cosine similarity performance.…”
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