中国科学院数学与系统科学研究院期刊网

25 June 2024, Volume 12 Issue 3
    

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  • Haowen BAO, Zishu CHENG, Yuying SUN, Yongmiao HONG, Shouyang WANG
    Journal of Systems Science and Information. 2024, 12(3): 309-322. https://doi.org/10.21078/JSSI-2023-0028
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    The worldwide spread of COVID-19 has caused a grave threat to human life, health, and socio-economic development. It is of great significance to study the transmission mechanism of COVID-19 and evaluate the effect of epidemic prevention policies. This paper employs a spatial dynamic panel data (SDPD) model to analyze the temporal and spatial spread of COVID-19, incorporating the time-varying features of epidemic transmission and the impact of geographic interconnections. Empirical studies on the COVID-19 outbreak in Shanghai during early 2022 show that the intra-regional transmission of COVID-19 dominated the cross-regional one. Additionally, strict policies are found to effectively reduce the transmission risk of COVID-19 and curb the spillover effect of the epidemic in Shanghai on other regions. Based on these results, we provide three policy suggestions. Furthermore, this research methodology can be extended to investigate other infectious diseases, thereby providing a scientific framework and theoretical basis for evaluating the spread risk of pandemics and formulating appropriate strategies.

  • Gulnaz ABDUKADYROVA, Adilia TABYSHOVA, Minara NAZEKOVA, Jyldyz ALYMBAEVA, Cholpon TOKTOSUNOVA
    Journal of Systems Science and Information. 2024, 12(3): 323-339. https://doi.org/10.21078/JSSI-2023-0148
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    The research topic related to the analysis of banking activity is relevant since the banking sector is complex and dynamic and requires constant monitoring and analysis to make informed decisions. Using a mathematical model of system dynamics to analyse banking activities can help banks make better decisions, manage risk, ensure the stability and efficiency of operations, and adapt to changing conditions. The purpose of the study is to determine the influence of various factors, in particular economic activity, on the final profit of the bank. Among the methods used, the analytical method, the functional method, the system analysis method, the deduction method, the synthesis method, and the comparison method were applied. In the course of this study, an extensive analytical review of banking activities was carried out using a system dynamics model, taking into account the requirements of the International Financial Reporting Standards (IFRS 9). The study included various scenario analyses to explore the impact of changes in economic conditions on the bank's loan portfolio, risk level, and profitability. Both economic growth scenarios and recession scenarios were considered in order to more fully assess their impact on the financial condition of the bank. A forecast and analysis of the risks associated with lending, investments, and other bank operations was carried out. The practical significance lies in the application of the results obtained to address issues related to banking in order to increase the efficiency of this process and achieve a new level of development.

  • Tong LIU, Saike HE
    Journal of Systems Science and Information. 2024, 12(3): 340-359. https://doi.org/10.21078/JSSI-2023-0091
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    In an era where power systems face increased cyber threats, social media data, especially public sentiment during outages, emerges as a crucial component for devising defense strategies. We present a methodology that integrates sentiment analysis of social media data with advanced reinforcement learning techniques to tackle uncertain load redistribution cyberattacks. This approach first employs VADER and Support Vector Machine (SVM) sentiment analysis on collected social media data, revealing insightful information about power outages and public sentiment. Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning method, is then applied in the second stage to leverage these insights, manage outage uncertainty, and optimize defense strategies. The efficacy of this methodology is demonstrated on a modified IEEE 6-bus system. The results underscore our approach's effectiveness in utilizing social media data for a nuanced, targeted response to cyberattacks. This pioneering methodology offers a promising direction for enhancing power grid resilience against cyberattacks and natural disasters, highlighting the value of social media sentiment analysis in power systems security.

  • Shuang SUN, Yan CHEN, Zaiji PIAO
    Journal of Systems Science and Information. 2024, 12(3): 360-378. https://doi.org/10.21078/JSSI-2023-0167
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    Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms, a study was conducted on the preprocessing process of trajectory time series. Firstly, an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS (Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation). On the basis of considering trajectory coverage, this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation. Then the compression efficiency of typical offline and online algorithms, such as the Douglas-Peucker algorithm, the Sliding Window algorithm and its enhancements, was compared before and after segmentation. The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation. Additionally, the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales, providing reasonable compression accuracy.

  • Yahya BENREMDANE, Said JAMAL, Oumaima TAHERI, Jawad LAKZIZ, Said OUASKIT
    Journal of Systems Science and Information. 2024, 12(3): 379-392. https://doi.org/10.21078/JSSI-2023-0179
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    The present research employs artificial intelligence to come up with an automatic solution for the modulation's classification of various radio signal varieties. As a result, the work we performed involved selecting the database required for supervised deep learning, evaluating the performance of current techniques on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. We started by examining the automatic classification models that are currently in usage. In light of the difficulty of forecasting in low Signal Noise Ratio (SNR) situations, we suggested an ensemble learning strategy based on adjusted ResNet and Transformer Neural Network, which is effective at extracting multi-scale features from the raw I/Q sequence data. Finally, we produced an architecture that is simple to use and apply to communication signals. The architecture of this solution is strong and optimal, enabling it to determine the type of modulation with up to 95% accuracy automatically.

  • Lixia MA, Jingjing WANG, Yiwei RU, Yunjie WEI
    Journal of Systems Science and Information. 2024, 12(3): 393-411. https://doi.org/10.21078/JSSI-2023-0017
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    Deep synthesis technology is an emerging artificial intelligence technology. There have been a large number of audio and video contents based on deep synthesis technology spreading in the Internet. In this paper, we take the deep synthetic videos on YouTube platform as the research object, and investigate the factors influencing the propagation effect of deep synthetic videos by establishing an ordered probit model. It is found that the effect of deep synthesized video transmission of YouTube platform is mainly influenced by factors such as the video type, video duration, influence of publishers and forms of fraud. In addition, the comparative analysis of ordinary video and in-depth synthesized video reveals that both the video transmission effect are significantly affected by the video type, video duration and the influence of publishers.

  • Qiguo GONG, Hai YANG
    Journal of Systems Science and Information. 2024, 12(3): 412-422. https://doi.org/10.21078/JSSI-E2022092
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    The 2017 Nobel Prize in Economics was awarded to Thaler, whose concept of nudges has been widely used. Although existing research has proposed numerous nudging strategies, no research comprehensively examines the role of nudging, resulting limited in attention across various fields. To fill this gap, this study aims to present how to analyze the role of nudges in the system using a systems thinking approach. The analysis indicates that nudges have a multiplier effect in the system and can achieve significant results at a small cost. We use three cases to analyze the effective impact of nudges on decision making: changing the default setting in organ donation, providing the check list to avoid mistakes in surgery, and the approach to prevent the coronavirus epidemic applied in China.

  • Xu XIAO, Feiyang ZHANG, Wenhan YIN, Dezhi ZHENG
    Journal of Systems Science and Information. 2024, 12(3): 423-432. https://doi.org/10.21078/JSSI-2023-0113
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    Addressing the vulnerability of contact-based keyboard password systems to disclosure, this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface (BCI) technology that detects steady-state visual evoked potential (SSVEP) signals. The system first lets a testee look at a digital stimulus source flashing at a specific frequency, and uses a wearable dry electrode sensor to collect the SSVEP signal. Secondly, a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at, and feeds back a code result through headphones. Finally, after all password codes are input, the system makes a judgment and provides visual feedback to the testee. Experiments were conducted to test the accuracy of the system, where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain, and each of them was tested for 12 times. The results demonstrate that this SSVEP-BCI-based system is feasible, achieving an average accuracy rate of 97.2%, and exhibits promising applications in various domains such as financial transactions and identity recognition.