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Search Results (29)
  • Open Access

    ARTICLE

    Data-Driven Method for Predicting Remaining Useful Life of Bearings Based on Multi-Layer Perception Neural Network and Bidirectional Long Short-Term Memory Network

    Yongfeng Tai1, Xingyu Yan2, Xiangyi Geng3, Lin Mu4, Mingshun Jiang2, Faye Zhang2,*

    Structural Durability & Health Monitoring, Vol.19, No.2, pp. 365-383, 2025, DOI:10.32604/sdhm.2024.053998 - 15 January 2025

    Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >

  • Open Access

    ARTICLE

    Uncovering Causal Relationships for Debiased Repost Prediction Using Deep Generative Models

    Wu-Jiu Sun1, Xiao Fan Liu1,2,*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4551-4573, 2024, DOI:10.32604/cmc.2024.057714 - 19 December 2024

    Abstract Microblogging platforms like X (formerly Twitter) and Sina Weibo have become key channels for spreading information online. Accurately predicting information spread, such as users’ reposting activities, is essential for applications including content recommendation and analyzing public sentiment. Current advanced models rely on deep representation learning to extract features from various inputs, such as users’ social connections and repost history, to forecast reposting behavior. Nonetheless, these models frequently ignore intrinsic confounding factors, which may cause the models to capture spurious relationships, ultimately impacting prediction performance. To address this limitation, we propose a novel Debiased Reposting Prediction… More >

  • Open Access

    ARTICLE

    Pressure Classification Analysis on CNN-Transformer-LSTM Hybrid Model

    Peng Xia1, Wu Zeng2,*, Yin Ni1, Ye Jin3

    Journal on Artificial Intelligence, Vol.6, pp. 361-377, 2024, DOI:10.32604/jai.2024.059114 - 13 December 2024

    Abstract Stress is defined as a subjective reflection of an internal psychological state of tension or arousal, manifesting as an interpretive, emotional, and defensive coping process within the body. Prolonged and sustained stress can significantly increase the risk of psychological and physiological disorders. Heart rate variability (HRV) is a key biomarker for assessing autonomic cardiac function, typically increasing during relaxation and decreasing under stress. Although measuring stress through physiological parameters like HRV is a common approach, achieving ultra-high accuracy based on HRV measurements remains a challenging task. In this study, the role of HRV features as… More >

  • Open Access

    ARTICLE

    A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants

    Shaoxiong Wu1, Ruoxin Li1, Xiaofeng Tao1, Hailong Wu1,*, Ping Miao1, Yang Lu1, Yanyan Lu1, Qi Liu2, Li Pan2

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

    Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

  • Open Access

    ARTICLE

    An Aerial Target Recognition Algorithm Based on Self-Attention and LSTM

    Futai Liang1,2, Xin Chen1,*, Song He1, Zihao Song1, Hao Lu3

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1101-1121, 2024, DOI:10.32604/cmc.2024.055326 - 15 October 2024

    Abstract In the application of aerial target recognition, on the one hand, the recognition error produced by the single measurement of the sensor is relatively large due to the impact of noise. On the other hand, it is difficult to apply machine learning methods to improve the intelligence and recognition effect due to few or no actual measurement samples. Aiming at these problems, an aerial target recognition algorithm based on self-attention and Long Short-Term Memory Network (LSTM) is proposed. LSTM can effectively extract temporal dependencies. The attention mechanism calculates the weight of each input element and… More >

  • Open Access

    ARTICLE

    Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

    Chunming Wu1, Shupeng Zheng2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665 - 20 June 2024

    Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

  • Open Access

    ARTICLE

    A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

    Jiajie Shen1, Yan Wang1,*, Dongxu Zhang2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.047903 - 20 June 2024

    Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions.… More >

  • Open Access

    ARTICLE

    Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

    Lin Ma1, Liyong Wang1, Shuang Zeng1, Yutong Zhao1, Chang Liu1, Heng Zhang1, Qiong Wu2,*, Hongbo Ren2

    Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332 - 21 May 2024

    Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

  • Open Access

    ARTICLE

    Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

    Fei Wang, Zhen Dong*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2315-2329, 2024, DOI:10.32604/cmc.2024.048443 - 15 May 2024

    Abstract Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models, SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model. The dataset is first preprocessed using solo thermal encoding and normalization functions. Then the spiral convolution-Long Short-Term Memory Network model is constructed, which consists of spiral convolution, a two-layer long short-term memory network, and a classifier. It is shown through experiments that the model is characterized by high accuracy, small model computation, and fast convergence speed relative to previous deep learning models. The model More >

  • Open Access

    ARTICLE

    The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

    Ramiz Gorkem Birdal*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4015-4028, 2024, DOI:10.32604/cmc.2024.048324 - 26 March 2024

    Abstract Maintaining a steady power supply requires accurate forecasting of solar irradiance, since clean energy resources do not provide steady power. The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network (CNN), but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions. This paper proposes a hybrid approach based on deep learning, expanding the feature set by adding new air pollution concentrations, and ranking these features to select and reduce their size to improve efficiency. In… More >

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