LSTM-Based Deep Model for Investment Portfolio Assessment and Analysis by Haohua Yang

“…The standard long short-term memory (LSTM) neural network has the shortcoming of low effectiveness of the fiscal cycle sequence. …”
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Torque Dynamic Coordinated Control for HEV based on the ISG Motor Torque Compensation by Deng Tao, Lu Renzhi, Li Yanan, Lin Chunsong

“…For the fluctuations problem of driveline torque which results from working-mode switching of dual-clutch parallel hybrid electric vehicles with integrated starter / Generator（ ISG）,focusing on study the process of transferring from pure electric working model to only engine working model.Based on the method of optimal the total efficiency of hybrid electric vehicle,the steady-state energy management control strategy is established,and then steady torque distribution area is achieved,which is the basis of coordination and control of dynamic torque.For the problem adopted the engine dynamic torque estimate genetic algorithm based on BP neural network algorithm,and ISG motor torque compensation method is proposed for dynamic torque coordinated control strategy.The simulation results show that the method proposed above can significantly decreases the torque fluctuations of working model transferring process,at the same time,the jerk to driveline is greatly reduced.…”
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Bidirectional RNN-based private car trajectory reconstruction algorithm by Zhu XIAO, Xin QIAN, Hongbo JIANG, Chenglin CAI, Fanzi ZENG

“…To address the problem that in the complex urban environment, due to the inevitable interruption of GNSS positioning signal and the accumulation of errors during vehicle driving, the collected vehicle trajectory data was likely to be inaccurate and incomplete.a bidirectional weighted trajectory reconstruction algorithm was proposed based on RNN neural network.The GNSS-OBD trajectory acquisition device was used to collect vehicle trajectory information, and multi-source data fusion was adopted to achieve bidirectional weighted trajectory reconstruction.Furthermore, the neural arithmetic logic unit (NALU) was leveraged with the purpose of enhancing the extrapolation ability of deep network and ensuring the accuracy of trajectory reconstruction.For the evaluation, real-world experiments were conducted to evaluate the performance of the proposed method in comparison with existing methods.The root mean square error (RMSE) indicator shows the algorithm accuracy and the reconstructed trajectory is visually displayed through Google Earth.Experimental results validate the effectiveness and reliability of the proposed algorithm.…”
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Planetary Gearbox Fault Diagnosis based on LMD Sample Entropy and ELM by Zhang Ning, Wei Xiuye, Xu Jinhong

“…In order to solve the difficult problem of early fault feature extraction of planetary gearbox and consider that the planetary gearbox vibration signal is coupling and nonlinear，and the signal has multiple transmission paths，a planetary gearbox fault diagnosis method based on Local Mean Decomposition（LMD） and Sample Entropy and Extreme Learning Machine（ELM） is proposed.Firstly，the vibration signal is adaptively decomposed into a plurality of PF components by LMD，and the first four PF components including the main fault information are selected in combination with the correlation coefficient and the variance contribution rate.Secondly，the Sample Entropy of the signal is calculated to form a feature vector.Finally，the feature vector is input into ELM for fault classification.Experiments are carried out on the planetary gearbox test bench，compared with the probabilistic neural network classification algorithm，and compared with the feature vector based on Singular Value Decomposition （SVD）.The results verify the effectiveness of the proposed method.…”
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Harmonic Classification with Enhancing Music Using Deep Learning Techniques by Wen Tang, Linlin Gu

“…Technique of machine learning such the convolutional neural network (CNN) will systematically extract the chord sequence to achieve the superiority context model. …”
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Kinematics Analysis of Grasping Manipulator based on ART-RBF Learning Algorithm by Kai Wang, XiaoJin Wan

“…On the basis of traditional RBF neural network, adaptive control generates the number of hidden layer nodes, and the similarity soft competition is applied in the first stage of learning. …”
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The Application of the Depth Model of Precise Matching between People and Posts Based on Ability Perception in Human Resource Management by Hui Cao

“…In order to solve the problem that job seekers’ job-seeking ability is difficult to match the job requirements, this paper combines neural network with traditional HRM (human resource management) algorithm based on ability perception and designs a depth model of accurate matching of people and posts in HR field, which can improve the quality of data training of traditional algorithm. …”
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Forecasting Stock Prices with Artificial Intelligence by Zhao Danxuan

“…The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). …”
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Research on Literary Translation Based on the Improved Optimization Model by Hongjian Liu

“…In order to improve the accuracy of the intelligent translation literature of the semantic ontology optimization model, the conversion layer, including the forward neural network layer, residual connection layer, and normalization layer, is added between the encoder and decoder of the semantic ontology optimization model. …”
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Construction and Practice of Multiple Mixed Teaching Mode Based on Big Data Analysis: A Case Study of “International Trade” Course by Xiaoyuan Wu

“…Furthermore, the convolution neural network model is used to improve the consistency between the talent cultivation of international business major and the talent demand of enterprises by interviewing teachers and questionnaire survey of students. …”
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Joint vibrotactile coding for machine recognition and human perception by Ying FANG, Yiwen XU, Tiesong ZHAO

“…In order to accurately transmit the content meaning of vibrotactile signals and achieve intelligent recognition and signal reconstruction, a joint vibrotactile coding scheme for machine recognition and human perception was proposed.At the encoding end, the original three-dimensional vibrotactile signals were converted into one-dimensional signals.Then the semantic information of the signals was extracted using a short-time Fourier transform before being effectively compressed and transmitted.At the decoding end, a fully convolutional neural network was used to intelligently recognize based on the semantic information.The difference between the original signals and the reconstructed signals based on semantic information was used as compensation for the semantic information, and the quality of the reconstructed signals was gradually improved to meet human perceptual needs.The experimental results show that the proposed scheme achieve tactile recognition with semantic information at a lower bit rate while improving the compression efficiency of tactile data, thus satisfying human perceptual needs.…”
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Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic by Ofra Tirosh-Becker, Michal Kessler, Oren Becker, Yonatan Belinkov

“…Then, we experiment with both an off-the-shelf morphological tagger and several specially designed neural network taggers. Finally, we perform a real-world evaluation of new texts that were never tagged before in comparison with human expert annotators. …”
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Adaptive Finite-Time Fault-Tolerant Control for Half-Vehicle Active Suspension Systems with Output Constraints and Random Actuator Failures by Jie Lan, Tongyu Xu

“…Unknown functions and coefficients are approximated by the neural network (NN). Assisted by the stochastic practical finite-time theory and FTC theory, the proposed controller can ensure systems achieve stability in a finite time. …”
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Attention-based deep learning for accurate cell image analysis by Xiangrui Gao, Fan Zhang, Xueyu Guo, Mengcheng Yao, Xiaoxiao Wang, Dong Chen, Genwei Zhang, Xiaodong Wang, Lipeng Lai

“…X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. …”
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Fast panoramic image stitching algorithm based on parameter regression by Fan GUO, Xiaohu LI, Wentao LIU, Jin TANG

“…In reality, the field of view of images acquired by cameras was usually limited, and the demand for panoramic images was increasing.Therefore, a fast panoramic image stitching algorithm based on parameter regression was proposed for panoramic image sequences.The traditional image registration task was transformed into deep learning combined with machine learning, a multi-scale deep convolutional neural network (MDCNN) based on Gaussian difference pyramid was designed to extract features of stitching images, and LightGBM regression model was used to predict stitching parameters.The transformation matrix and the focal length of the camera were obtained to align the images, and a hyperbolic image fusion algorithm was designed to eliminate the stitching seam between the images.The experimental results show that the proposed algorithm can quickly mosaic images and obtain clearer and more natural panoramic mosaic effects than the existing representative algorithms.It also has good adaptability for infrared images.…”
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Spam Email Detection using Naïve Bayes classifier by Wang Liansong

“…Various algorithms such as the tree-based model, support vector machine Algorithm, and Convolutional Neural Network have been explored in prior research to tackle this challenge. …”
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A Prediction Method for the RUL of Equipment for Missing Data by Chen Wenbai, Liu Chang, Chen Weizhao, Liu Huixiang, Chen Qili, Wu Peiliang

“…We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). …”
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Deep Learning-based Gold Price Prediction: A Novel Approach using Time Series Analysis by Hewa Majeed Zangana, Salah Ramadan Obeyd

“…The system leverages Long Short-Term Memory (LSTM), a specialized recurrent neural network architecture, to capture temporal dependencies and patterns in the time series data of gold prices. …”
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A lightweight and precision dual track 1D and 2D feature fusion convolutional network for machinery equipment fault diagnosis by Chaoquan Mo, Ke Huang, Houxin Ji

“…Abstract Addressing the issues of a single-feature input channel structure, scarcity of training fault data, and insufficient feature learning capabilities in noisy environments for intelligent diagnostic models of mechanical equipment, we propose a method based on a one-dimensional and two-dimensional dual-channel feature information fusion convolutional neural network (1D_2DIFCNN). By constructing a one-dimensional and two-dimensiona dual-channel feature information fusion convolutional network and introducing a Convolutional Block Attention Mechanism, we utilize Random Overlapping Sampling Technique to process raw vibration signals. …”
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ANN Synthesis Model of Single-Feed Corner-Truncated Circularly Polarized Microstrip Antenna with an Air Gap for Wideband Applications by Zhongbao Wang, Shaojun Fang

“…A computer-aided design model based on the artificial neural network (ANN) is proposed to directly obtain patch physical dimensions of the single-feed corner-truncated circularly polarized microstrip antenna (CPMA) with an air gap for wideband applications. …”
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