Ramiz Gorkem Birdal^*

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

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 order to improve the accuracy… More >

Kaicheng Liu^1,3,*, Chen Liang², Xiaoyang Dong², Liping Liu¹

Energy Engineering, Vol.121, No.4, pp. 933-949, 2024, DOI:10.32604/ee.2023.043658

Abstract Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of PV generation is conducted, and… More >

Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long Short-term Memory Network (Bi-LSTM). By… More >

Wei Zhang¹, Ping He^2,*, Ting Li², Fan Yang¹, Ying Liu³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1893-1910, 2023, DOI:10.32604/cmc.2023.044253

Abstract Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these representations through the utilization of… More >

Wenqiu Zhu^1,2, Yongsheng Li^1,2, Qiang Liu^1,2,*, Zhigao Zeng^1,2

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1373-1392, 2023, DOI:10.32604/cmc.2023.037216

Abstract Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs and uncropped IBs. To alleviate… More >

Tiantian Liang^*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga whale optimization (BWO) algorithm is… More > Graphic Abstract

Qingyue Zhao¹, Qiaoyu Gu², Zhijun Gao^3,*, Shipian Shao¹, Xinyuan Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500

Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, an attention layer is designed… More >

Fulu Wei¹, Xin Li¹, Yongqing Guo^1,*, Zhenyu Wang², Qingyin Li¹, Xueshi Ma³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799

Abstract Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract… More >

Neelam Mughees^1,2, Mujtaba Hussain Jaffery¹, Abdullah Mughees³, Anam Mughees⁴, Krzysztof Ejsmont^5,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564

Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked, sequence-to-sequence autoencoder (S2SAE) forecasting model… More >

S. Kavi Priya^*, K. Pon Karthika

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160

Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory… More >