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  • Open Access

    ARTICLE

    Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM

    Jingcheng Qian1, Mingfang Zhu1, Yingnan Zhao2,*, Xiangjian He3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 197-209, 2021, DOI:10.32604/csse.2021.016911

    Abstract Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power. Temporal-spatial wind speed features contain rich information; however, their use to predict wind speed remains one of the most challenging and less studied areas. This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attention-based long short-term memory (LSTM), termed 2Attn-LSTM, a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data. To eliminate the unevenness of the original wind speed,… More >

  • Open Access

    ARTICLE

    Brain Storm Optimization Based Clustering for Learning Behavior Analysis

    Yu Xue1,2,*, Jiafeng Qin1, Shoubao Su2, Adam Slowik3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 211-219, 2021, DOI:10.32604/csse.2021.016693

    Abstract Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students’ learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data. This study proposes a… More >

  • Open Access

    ARTICLE

    Crowdsourced Requirements Engineering Challenges and Solutions: A Software Industry Perspective

    Huma Hayat Khan1,*, Muhammad Noman Malik2, Youseef Alotaibi3, Abdulmajeed Alsufyani4, Saleh Alghamdi5

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 221-236, 2021, DOI:10.32604/csse.2021.016510

    Abstract Software crowdsourcing (SW CS) is an evolving software development paradigm, in which crowds of people are asked to solve various problems through an open call (with the encouragement of prizes for the top solutions). Because of its dynamic nature, SW CS has been progressively accepted and adopted in the software industry. However, issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained. If the requirements are not clear to the development team, it has a significant effect on the quality of the software product. This study aims to identify… More >

  • Open Access

    ARTICLE

    A Markov Model for Subway Composite Energy Prediction

    Xiaokan Wang1,2,*, Qiong Wang1, Liang Shuang3, Chao Chen4

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 237-250, 2021, DOI:10.32604/csse.2021.015945

    Abstract Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard train operating conditions for simulation,… More >

  • Open Access

    ARTICLE

    An Effective CU Depth Decision Method for HEVC Using Machine Learning

    Xuan Sun1,2,3, Pengyu Liu1,2,3,*, Xiaowei Jia4, Kebin Jia1,2,3, Shanji Chen5, Yueying Wu1,2,3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 275-286, 2021, DOI:10.32604/csse.2021.015255

    Abstract This paper presents an effective machine learning-based depth selection algorithm for CTU (Coding Tree Unit) in HEVC (High Efficiency Video Coding). Existing machine learning methods are limited in their ability in handling the initial depth decision of CU (Coding Unit) and selecting the proper set of input features for the depth selection model. In this paper, we first propose a new classification approach for the initial division depth prediction. In particular, we study the correlation of the texture complexity, QPs (quantization parameters) and the depth decision of the CUs to forecast the original partition depth of the current CUs. Secondly,… More >

  • Open Access

    ARTICLE

    Research on the Novel Honeycomb-Like Cabin Based on Computer Simulation

    Yong Wang, Yongyan Wang*, Songmei Li, Nan Qin, Peng Du, Tongtong Zhou

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 179-195, 2021, DOI:10.32604/csse.2021.014469

    Abstract The antiknock capability and thermal protection performance of rescue capsules mainly depend on the structural design of the cabin. By designing a new type of cabin structure, it can resist the impact of explosion shock waves and thermal shocks. In this paper, a new honeycomb-like cabin is proposed; the model has a novel thermal insulation layer design. Then, the antiknock capabilities and thermal protection analysis are carried out by using computer software. The “Autodyn” analysis module in ANSYS Workbench 17.0 has been used to simulate the explosion of TNT with a certain quality in a single room. The pressure map… More >

  • Open Access

    ARTICLE

    A survey on the Metaheuristics for Cryptanalysis of Substitution and Transposition Ciphers

    Arkan Kh Shakr Sabonchi*, Bahriye Akay

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 87-106, 2021, DOI:10.32604/csse.2021.05365

    Abstract This paper presents state-of-art cryptanalysis studies on attacks of the substitution and transposition ciphers using various metaheuristic algorithms. Traditional cryptanalysis methods employ an exhaustive search, which is computationally expensive. Therefore, metaheuristics have attracted the interest of researchers in the cryptanalysis field. Metaheuristic algorithms are known for improving the search for the optimum solution and include Genetic Algorithm, Simulated Annealing, Tabu Search, Particle Swarm Optimization, Differential Evolution, Ant Colony, the Artificial Bee Colony, Cuckoo Search, and Firefly algorithms. The most important part of these various applications is deciding the fitness function to guide the search. This review presents how these algorithms… More >

  • Open Access

    ARTICLE

    Inverse Length Biased Maxwell Distribution: Statistical Inference with an Application

    Amer Ibrahim Al-Omari1, Ayed R.A. Alanzi2,*

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 147-164, 2021, DOI:10.32604/csse.2021.017362

    Abstract In this paper, we suggested and studied the inverse length biased Maxell distribution (ILBMD) as a new continuous distribution of one parameter. The ILBMD is obtained by considering the inverse transformation technique of the Maxwell length biased distribution. Statistical characteristics of the ILBMD such as the moments, moment generating function, mode, quantile function, the coefficient of variation, coefficient of skewness, Moors and Bowley measures of kurtosis and skewness , stochastic ordering, stress-strength reliability, and mean deviations are obtained. In addition, the Bonferroni and Lorenz curves, Gini index, the reliability function, the hazard rate function, the reverse hazard rate function, the… More >

  • Open Access

    ARTICLE

    Cyclic Autoencoder for Multimodal Data Alignment Using Custom Datasets

    Zhenyu Tang1, Jin Liu1,*, Chao Yu1, Y. Ken Wang2

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 37-54, 2021, DOI:10.32604/csse.2021.017230

    Abstract The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data. Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret. We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition. To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network. The feature dimensions of the two modal data are aligned under the premise of stabilizing… More >

  • Open Access

    ARTICLE

    COVID-19 Automatic Detection Using Deep Learning

    Yousef Sanajalwe1,2,*, Mohammed Anbar1, Salam Al-E’mari1

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 15-35, 2021, DOI:10.32604/csse.2021.017191

    Abstract The novel coronavirus disease 2019 (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people. The first step to mitigate and control its spread is to identify and isolate the infected people. But, because of the lack of reverse transcription polymerase chain reaction (RT-CPR) tests, it is important to discover suspected COVID-19 cases as early as possible, such as by scan analysis and chest X-ray by radiologists. However, chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case. In this paper, an automated novel detection… More >

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