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International Journal of Space-Based and Situated Computing
Number of Followers: 2

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2044-4893 - ISSN (Online) 2044-4907
Published by Inderscience Publishers

[439 journals]

Data privacy and anonymisation of simulated health-care dataset using
the ARX open source tool
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  Authors: Yogesh Beeharry, Noorsabah Y. Fakeeroodeen, Tulsi Pawan Fowdur
  Pages: 125 - 137
  Abstract: Internet of things (IoT) and big data analytics fields have been adopted in many practical applications geared towards the concept of smart cities and smart world. One critical aspect of these concepts is that of smart-health where private health information has to be transmitted over networks. The main concern arising in this scenario is that of data privacy and anonymity. Various open source tools and algorithms have been developed along this line. In this work, an analysis of the <i>K</i>-anonymity and differential privacy alongside t-closeness for sensitive attributes, algorithms have been performed on a simulated health-care dataset using the ARX open-source tool. The results demonstrate that the two models with <i>t-</i>closeness provided similar output results in relation to risk analysis, utility statistics and output data indicating equal appropriateness for both models. However, their property of transformation differed from each other.
  Keywords: data anonymisation; privacy; K-anonymity; differential privacy; t-closeness; ARX open-source tool; healthcare dataset; distribution of risks; attributes identification; Quasi-identifiers
  Citation: International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 125 - 137
  PubDate: 2023-09-03T23:20:50-05:00
  DOI: 10.1504/IJSSC.2023.133235
  Issue No: Vol. 9, No. 3 (2023)
Traffic intersection crossing method for intelligent vehicles based on
game theory
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  Authors: Yikai Wang, Zhuming Nie, Yuquan Wu, Xiaozhao Fang, Xi He, Hongbo Gao
  Pages: 138 - 146
  Abstract: Game theory is a tool for decision-making in the face of uncertain factors. In this paper, by analysing the defects of the existing traffic light timing strategy, combined with the existing traffic conditions, a dynamic traffic crossing strategy based on game theory is proposed. This paper attempts to use game theory to determine the traffic relationship between intersections, adjust the timing of traffic lights in real time, and compare it with the existing timing methods through software simulation. The experimental results show that the dynamic traffic strategy proposed in this paper can improve the traffic efficiency, effectively reduce the maximum queue length, and has a strong adaptability to emergencies.
  Keywords: game theory; traffic strategy; intelligent vehicles; vehicle-road collaboration; real-time adjustment
  Citation: International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 138 - 146
  PubDate: 2023-09-03T23:20:50-05:00
  DOI: 10.1504/IJSSC.2023.133236
  Issue No: Vol. 9, No. 3 (2023)
GPS availability prediction based on air-ground collaboration
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  Authors: Zun Liu, Wenlian Huang, Yanyan Chen, Jie Chen
  Pages: 147 - 157
  Abstract: Robots such as unmanned aerial vehicles (UAVs) have been widely applied in emergency rescue scenes. However, there is a lack of distribution of ground GPS signals in the complex environment, which indirectly affects the take-off and landing of UAVs. To solve this problem, we have proposed an air-ground collaborative mapping system based on the Gaussian Process (GP) and convolutional neural network (CNN). Firstly, CNN is used to predict whether the GPS signals are available. And the GP is used to interpolate and predict the areas not visited by the UAVs, then the GPS signal distribution map is obtained. Compared with the traditional mapping methods, the system does not require size parameters and can build maps more efficiently and quickly.
  Keywords: mapping; robots; rescue scene; Gaussian process; CNN; convolutional neural network
  Citation: International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 147 - 157
  PubDate: 2023-09-03T23:20:50-05:00
  DOI: 10.1504/IJSSC.2023.133240
  Issue No: Vol. 9, No. 3 (2023)
Inventory optimisation based on NSGA-III algorithm
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  Authors: Yaxue Li, Hongzhi Xie, Xiaolin Deng, Jin Zhang, Shuhui Liu, Li Wang
  Pages: 158 - 164
  Abstract: Inventory management is essential to any enterprise, and correctly setting inventory parameters can reduce costs while ensuring adequate inventory levels. To meet the availability requirements of different categories of spare parts for nuclear power plants, this paper combines the NSGA3 algorithm with a single-objective algorithm to solve the inventory parameter setting problem. Based on the historical usage data of nuclear power plant spare parts, we establish both availability rate and spare part cost objective functions and optimise these functions using the NSGA3 algorithm. We then use a single-objective optimisation function to obtain the optimal solution as the parameter setting for nuclear power plant spare parts. By combining these two methods, we can better meet spare parts requirements for different categories, while fully considering the inventory management needs of nuclear power companies and improving management efficiency.
  Keywords: NSGA3; single-objective optimisation; inventory manage; spare parts classification
  Citation: International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 158 - 164
  PubDate: 2023-09-03T23:20:50-05:00
  DOI: 10.1504/IJSSC.2023.133247
  Issue No: Vol. 9, No. 3 (2023)
A multi-tiered spare parts inventory forecasting system
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  Authors: Zhuoqing Xie, Hongzhi Xie, Shuhui Liu, Yijing Huang, Xiaolin Deng, Biwei Liu
  Pages: 165 - 172
  Abstract: To achieve intelligent nuclear power management, we propose a multilevel nuclear power spare parts prediction method. We combine qualitative prediction methods with various cutting-edge quantitative prediction methods to forecast the overall inventory of nuclear power spare parts and individual categories, enabling enterprises to minimise costs and prevent stockouts. Specifically, we classified the spare parts according to their usage characteristics and developed a hybrid model that integrates the CNN+BiLSTM and DLinear models (Zeng et al., 2022), taking into account expert opinions for each category. Experimental results show a significant improvement in accuracy compared to traditional methods.
  Keywords: inventory forecasting; spare parts forecasting; deep learning; quantitative techniques; multi-layer
  Citation: International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 165 - 172
  PubDate: 2023-09-03T23:20:50-05:00
  DOI: 10.1504/IJSSC.2023.133248
  Issue No: Vol. 9, No. 3 (2023)
A hierarchical outlier detection method for spare parts transaction
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  Authors: Haiming Zeng, Hongzhi Xie, Kai Yuan, Biwei Liu, Xiaolin Deng, Li Wang
  Pages: 173 - 181
  Abstract: In nuclear power production, seamless equipment maintenance is integral, significantly achieved through spare parts transactions. Yet, abnormal transaction data can obstruct operational efficiency. Traditional anomaly detection methods, often subjective and non-scalable, struggle to handle these abnormalities effectively. Addressing these shortcomings, this paper presents a Hierarchical Outlier Detection Method, specifically for nuclear power spare parts transaction data. This method integrates data cleansing, probability statistics, and model recognition modules, offering an innovative, robust approach to anomaly detection. Leveraging statistical and neural network approaches, our method exhibits superior adaptability, computational efficiency, and detection performance, substantiated through a real-world dataset of nuclear power plant spare parts transaction records. Its versatile design suggests potential applicability to other industrial scenarios.
  Keywords: anomaly detection; spare parts transactions; data cleansing; probability statistics; model recognition
  Citation: International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 173 - 181
  PubDate: 2023-09-03T23:20:50-05:00
  DOI: 10.1504/IJSSC.2023.133245
  Issue No: Vol. 9, No. 3 (2023)

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