Most recent issue published online in the International Journal of Space-Based and Situated Computing.
International Journal of Space-Based and Situated Computing
http://www.inderscience.com/browse/index.php?journalID=373&year=2023&vol=9&issue=3
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International Journal of Space-Based and Situated Computing
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International Journal of Space-Based and Situated Computing
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http://www.inderscience.com/browse/index.php?journalID=373&year=2023&vol=9&issue=3
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Data privacy and anonymisation of simulated health-care dataset using the ARX open source tool
http://www.inderscience.com/link.php?id=133235
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.
Data privacy and anonymisation of simulated health-care dataset using the ARX open source tool
Yogesh Beeharry; Noorsabah Y. Fakeeroodeen; Tulsi Pawan Fowdur
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 125 - 137
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.]]>
10.1504/IJSSC.2023.133235
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 125 - 137
Yogesh Beeharry
Noorsabah Y. Fakeeroodeen
Tulsi Pawan Fowdur
Faculty of Engineering, Department of Electrical and Electronic Engineering, University of Mauritius, Réduit 80837, Mauritius ' Faculty of Engineering, Department of Electrical and Electronic Engineering, University of Mauritius, Réduit 80837, Mauritius ' Faculty of Engineering, Department of Electrical and Electronic Engineering, University of Mauritius, Réduit 80837, Mauritius
data anonymisation
privacy
K-anonymity
differential privacy
t-closeness
ARX open-source tool
healthcare dataset
distribution of risks
attributes identification
Quasi-identifiers
2023-09-03T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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137
2023-09-03T23:20:50-05:00
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Traffic intersection crossing method for intelligent vehicles based on game theory
http://www.inderscience.com/link.php?id=133236
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.
Traffic intersection crossing method for intelligent vehicles based on game theory
Yikai Wang; Zhuming Nie; Yuquan Wu; Xiaozhao Fang; Xi He; Hongbo Gao
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 138 - 146
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.]]>
10.1504/IJSSC.2023.133236
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 138 - 146
Yikai Wang
Zhuming Nie
Yuquan Wu
Xiaozhao Fang
Xi He
Hongbo Gao
Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China ' Department of Educational Technology, Anhui Normal University, Wuhu, Anhui, 241000, China ' Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China ' School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China ' Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China ' Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China
game theory
traffic strategy
intelligent vehicles
vehicle-road collaboration
real-time adjustment
2023-09-03T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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146
2023-09-03T23:20:50-05:00
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GPS availability prediction based on air-ground collaboration
http://www.inderscience.com/link.php?id=133240
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.
GPS availability prediction based on air-ground collaboration
Zun Liu; Wenlian Huang; Yanyan Chen; Jie Chen
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 147 - 157
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.]]>
10.1504/IJSSC.2023.133240
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 147 - 157
Zun Liu
Wenlian Huang
Yanyan Chen
Jie Chen
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China ' College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China ' School of Software, Henan University of Science and Technology, Luoyang, 471023, China; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China ' School of Software, Henan University of Science and Technology, Luoyang, 471023, China; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
mapping
robots
rescue scene
Gaussian process
CNN
convolutional neural network
2023-09-03T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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157
2023-09-03T23:20:50-05:00
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Inventory optimisation based on NSGA-III algorithm
http://www.inderscience.com/link.php?id=133247
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.
Inventory optimisation based on NSGA-III algorithm
Yaxue Li; Hongzhi Xie; Xiaolin Deng; Jin Zhang; Shuhui Liu; Li Wang
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 158 - 164
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.]]>
10.1504/IJSSC.2023.133247
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 158 - 164
Yaxue Li
Hongzhi Xie
Xiaolin Deng
Jin Zhang
Shuhui Liu
Li Wang
National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China ' Spare Parts Center, China Nuclear Power Operations Co.,Ltd, Shenzhen, 518000, China ' Spare Parts Center, China Nuclear Power Operations Co.,Ltd, Shenzhen, 518000, China ' National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China ' Spare Parts Center, China Nuclear Power Operations Co.,Ltd, Shenzhen, 518000, China ' National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
NSGA3
single-objective optimisation
inventory manage
spare parts classification
2023-09-03T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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164
2023-09-03T23:20:50-05:00
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A multi-tiered spare parts inventory forecasting system
http://www.inderscience.com/link.php?id=133248
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.
A multi-tiered spare parts inventory forecasting system
Zhuoqing Xie; Hongzhi Xie; Shuhui Liu; Yijing Huang; Xiaolin Deng; Biwei Liu
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 165 - 172
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.]]>
10.1504/IJSSC.2023.133248
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 165 - 172
Zhuoqing Xie
Hongzhi Xie
Shuhui Liu
Yijing Huang
Xiaolin Deng
Biwei Liu
National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China ' Spare Parts Center, China Nuclear Power Operations Co. Ltd., Shenzhen, 516545, China ' Spare Parts Center, China Nuclear Power Operations Co. Ltd., Shenzhen, 516545, China ' National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China ' Spare Parts Center, China Nuclear Power Operations Co.Ltd Shenzhen, 516545, China ' National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
inventory forecasting
spare parts forecasting
deep learning
quantitative techniques
multi-layer
2023-09-03T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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165
172
2023-09-03T23:20:50-05:00
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A hierarchical outlier detection method for spare parts transaction
http://www.inderscience.com/link.php?id=133245
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.
A hierarchical outlier detection method for spare parts transaction
Haiming Zeng; Hongzhi Xie; Kai Yuan; Biwei Liu; Xiaolin Deng; Li Wang
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 173 - 181
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.]]>
10.1504/IJSSC.2023.133245
International Journal of Space-Based and Situated Computing, Vol. 9, No. 3 (2023) pp. 173 - 181
Haiming Zeng
Hongzhi Xie
Kai Yuan
Biwei Liu
Xiaolin Deng
Li Wang
National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China ' Spare Parts Center, China Nuclear Power Operations Co., Ltd, 518124, China ' Spare Parts Center, China Nuclear Power Operations Co., Ltd, 518124, China ' National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China ' Spare Parts Center, China Nuclear Power Operations Co., Ltd, 518124, China ' National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China
anomaly detection
spare parts transactions
data cleansing
probability statistics
model recognition
2023-09-03T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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181
2023-09-03T23:20:50-05:00