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  • Open Access

    ARTICLE

    Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

    Kaicheng Liu1,3,*, 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 >

  • Open Access

    ARTICLE

    Spatio-temporal pattern detection in spatio-temporal graphs

    Use case of invasive team sports and urban road traffic

    Kamaldeep Singh Oberoi1, Géraldine Del Mondo2

    Revue Internationale de Géomatique, Vol.31, No.2, pp. 377-399, 2022, DOI:10.3166/RIG.31.377-399 c 2022

    Abstract Spatio-temporal (ST) graphs have been used in many application domains to model evolving ST phenomenon. Such models represent the underlying structure of the phenomenon in terms of its entities and different types of spatial interactions between them. The reason behind using graph-based models to represent ST phenomenon is due to the existing well-established graph analysis tools and algorithms which can be directly applied to analyze the phenomenon under consideration. In this paper, considering the use case of two distinct, highly dynamic phenomena - invasive team sports, with a focus on handball and urban road traffic, we propose a spatio-temporal graph… More >

  • Open Access

    ARTICLE

    Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of Key Regions

    Wenqiu Zhu1,2, Yongsheng Li1,2, Qiang Liu1,2,*, Zhigao Zeng1,2

    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 >

  • Open Access

    ARTICLE

    Mortality Rates of Ventricular Septal Defect for Children in Kazakhstan: Spatio-Temporal Epidemiological Appraisal

    Akkerbez Adilbekova1,3,*, Shukhrat Marassulov1, Bakhytzhan Nurkeev1, Saken Kozhakhmetov2, Aikorkem Badambekova3

    Congenital Heart Disease, Vol.18, No.4, pp. 447-459, 2023, DOI:10.32604/chd.2023.028742

    Abstract Objective: The aim is to study the trends in ventricular septal defect (VSD) mortality in children in Kazakhstan. Methods: The retrospective study was done for the period 2011–2020. Descriptive and analytical methods of epidemiology were applied. The universally acknowledged methodology used in sanitary statistics is used to calculate the extensive, crude, and age-specific mortality rates. Results: Kazakhstan is thought to be seeing an increase in mortality from VSDs in children. As a result, this study for the years 2011 to 2020 was conducted to retrospectively assess data from the central registration of the Bureau of National Statistics that was available… More > Graphic Abstract

    Mortality Rates of Ventricular Septal Defect for Children in Kazakhstan: Spatio-Temporal Epidemiological Appraisal

  • Open Access

    ARTICLE

    Spatio-Temporal Characteristics of Heat Transfer of Methanation in Fluidized Bed for Pyrolysis and Gasification Syngas of Organic Solid Waste

    Danyang Shao1, Xiaojia Wang1,*, Delu Chen1, Fengxia An1,2

    Journal of Renewable Materials, Vol.11, No.10, pp. 3659-3680, 2023, DOI:10.32604/jrm.2023.029220

    Abstract Methanation is an effective way to efficiently utilize product gas generated from the pyrolysis and gasification of organic solid wastes. To deeply study the heat transfer and mass transfer mechanisms in the reactor, a successful three-dimensional comprehensive model has been established. Multiphase flow behavior and heat transfer mechanisms were investigated under reference working conditions. Temperature is determined by the heat release of the reaction and the heat transfer of the gas-solid flow. The maximum temperature can reach 951 K where the catalyst gathers. In the simulation, changes in the gas inlet velocity and catalyst flow rate were made to explore… More >

  • Open Access

    ARTICLE

    A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

    Tao Liu1, Kejia Zhang1,*, Jingsong Yin1, Yan Zhang1, Zihao Mu1, Chunsheng Li1, Yanan Hu2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2563-2582, 2023, DOI:10.32604/csse.2023.041228

    Abstract Spatio-temporal heterogeneous data is the database for decision-making in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the… More >

  • Open Access

    ARTICLE

    Multimodal Spatiotemporal Feature Map for Dynamic Gesture Recognition

    Xiaorui Zhang1,2,3,*, Xianglong Zeng1, Wei Sun3,4, Yongjun Ren1,2,3, Tong Xu5

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 671-686, 2023, DOI:10.32604/csse.2023.035119

    Abstract Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language translation. Existing researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition, but the networks are affected by much interference information in the input data and thus fit to some unimportant features. In this paper, we proposed a novel method for encoding spatio-temporal information, which can enhance the key features required for gesture recognition, such as shape, structure, contour, position and hand motion of gestures, thereby improving the accuracy of… More >

  • Open Access

    ARTICLE

    Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

    Umair Muneer Butt1,2,*, Hadiqa Aman Ullah2, Sukumar Letchmunan1, Iqra Tariq2, Fadratul Hafinaz Hassan1, Tieng Wei Koh3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5017-5033, 2023, DOI:10.32604/cmc.2023.035512

    Abstract Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models… More >

  • Open Access

    ARTICLE

    Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

    L. Maria Michael Visuwasam1,*, D. Paul Raj2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2435-2446, 2023, DOI:10.32604/csse.2023.024742

    Abstract Technological developments create a lot of impacts in the tourism industry. Emerging big data technologies and programs generate opportunities to enhance the strategy and results for transport security. However, there is a difference between technological advances and their integration into the methods of tourism study. The rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental issues. This study addresses the gap by using a synthesized application (app) for demographic surveys and Global Navigation Satellite System (GNSS) technology to implement research processes. This article focuses on managing visitors within the famous Freycinet… More >

  • Open Access

    ARTICLE

    Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

    Yizhi Liu1,3, Rutian Qing1,3, Yijiang Zhao1,3,*, Xuesong Wang1,3, Zhuhua Liao1,3, Qinghua Li1,2, Buqing Cao1,3

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2505-2519, 2023, DOI:10.32604/csse.2023.021615

    Abstract Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads. But how to alleviate sparsity and further enhance accuracy is still challenging. Addressing at these issues, we propose to fuse spatio-temporal contexts into deep factorization machine (STC_DeepFM) offline for pick-up area recommendation, and within the area to recommend pick-up points online using factorization machine (FM). Firstly, we divide the urban area into several grids with equal size. Spatio-temporal contexts are destilled from pick-up points or points-of-interest (POIs) belonged to the preceding grids. Secondly, the contexts are integrated into deep factorization machine (DeepFM) to mine… More >

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