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您的检索式：作者名="Supaporn LONAPALAWONG"
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| 1 | EcoVis:visual analysis of industrial-level spatio-temporal correlations in electricity consumption显示文摘Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method. | Yong XIAO Kaihong ZHENG Supaporn LONAPALAWONG Wenjie LU Zexian CHEN Bin QIAN Tianye ZHANG Xin WANG Wei CHEN | 2022 | Frontiers of Computer Science2022，16，2： | 1 |
| 2 | Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks显示文摘Analyzing the vulnerability of power systems in cascading failures is generally regarded as a challenging problem. Although existing studies can extract some critical rules, they fail to capture the complex subtleties under different operational conditions. In recent years, several deep learning methods have been applied to address this issue. However, most of the existing deep learning methods consider only the grid topology of a power system in terms of topological connections, but do not encompass a power system’s spatial information such as the electrical distance to increase the accuracy in the process of graph convolution. In this paper, we construct a novel power-weighted line graph that uses power system topology and spatial information to optimize the edge weight assignment of the line graph. Then we propose a multi-graph convolutional network(MGCN) based on a graph classification task, which preserves a power system’s spatial correlations and captures the relationships among physical components. Our model can better handle the problem with power systems that have parallel lines, where our method can maintain desirable accuracy in modeling systems with these extra topology features. To increase the interpretability of the model, we present the MGCN using layer-wise relevance propagation and quantify the contributing factors of model classification. | Supaporn LONAPALAWONG Changsheng CHEN Can WANG Wei CHEN | 2022 | Frontiers of Information Technology & Electronic Engineering2022，23，12： | 0 |
| 3 | Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition显示文摘Analyzing network robustness under various circumstances is generally regarded as a challenging problem.Robustness against failure is one of the essential properties of large-scale dynamic network systems such as power grids,transportation systems,communication systems,and computer networks.Due to the network diversity and complexity,many topological features have been proposed to capture specific system properties.For power grids,a popular process for improving a network’s structural robustness is via the topology design.However,most of existing methods focus on localized network metrics,such as node connectivity and edge connectivity,which do not encompass a global perspective of cascading propagation in a power grid.In this paper,we use an informative global metric algebraic connectivity because it is sensitive to the connectedness in a broader spectrum of graphs.Our process involves decreasing the average propagation in a power grid by minimizing the increase in its algebraic connectivity.We propose a topology-based greedy strategy to optimize the robustness of the power grid.To evaluate the network robustness,we calculate the average propagation using MATCASC to simulate cascading line outages in power grids.Experimental results illustrate that our proposed method outperforms existing techniques. | Supaporn LONAPALAWONG Jiangzhe YAN Jiayu LI Deshi YE Wei CHEN Yong TANG Yanhao HUANG Can WANG | 2022 | Frontiers of Information Technology & Electronic Engineering2022，23，3： | 0 |
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