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PFD Simulator based Deep Reinforcement Learning for Energy Consumption Minimization of Electric RTO
http://doi.org/10.5626/JOK.2025.52.6.490
This study proposes a method that could generate data through a simulator in situations where data collection is difficult. A deep reinforcement learning agent is then trained based on generated data to maintain stable electric regenerative thermal oxidizer (RTO) operation and minimize energy consumption. First, data were generated from a simulator created using actual equipment Process Flow Diagrams (PFDs) and field operation methods. An environment that incorporated states, actions, and rewards was established for agent training. Performance evaluation results demonstrated that the control using the deep reinforcement learning agent trained with this method enabled more stable operation of the electric RTO system, while simultaneously reducing power consumption by up to approximately 9% compared to the conventional operation strategy.
Graph Structure Learning: Reflecting Types of Relationships between Sensors in Multivariate Time Series Anomaly Detection
http://doi.org/10.5626/JOK.2024.51.3.236
Sensors are used to monitor systems in various fields, such as water treatment systems and smart factories. Anomalies in the system can be detected by analyzing multivariate time series consisting of sensor data. To efficiently detect anomalies, information about the relationships between sensors is required, but this information is generally difficult to obtain. To solve this problem, the previous work used sensor data to identify relationships between sensors, which were then represented using a graph structure. However, in this process, the graph structure only reflects the presence of relationships between sensors, not the types of relationships between sensors. In this pap er, we considered the types of relationships between sensors in graph structure learning and analyzed multivariate time series to detect anomalies in the system. Experiments show that improving detection accuracy in graph structure learning for multivariate time series anomaly detection involves taking into account the different kinds of relationships among sensors.
Knowledge Graph Embedding for Link Prediction using Node-Link Interaction-based Graph Attention Networks
http://doi.org/10.5626/JOK.2022.49.7.555
Knowledge graphs are structures that express knowledge in the real world in the form of nodes and links-based triple form. These knowledge graphs are incomplete and many embedding techniques have been studied to effectively represent nodes and links in low-dimensional vector spaces to find other missing relationships. Recently, many neural network-based knowledge graph link prediction methods have been studied. However existing models consider nodes and links independently when determining the importance of a triple to a node which makes it difficult to reflect the interaction between nodes and links. In this paper, we propose an embedding method that will be used to analyze the importance of triple units by simultaneously considering nodes and links using composition operators, and at the same time prove that the model outperforms other methods in knowledge graph link prediction.
Index-based Searching for Isomorphic Subgraphs in Hypergraph Databases
Dae Geun Ha, Tae Wook Ha, Jung Hyuk Seo, Myoung Ho Kim
http://doi.org/10.5626/JOK.2019.46.7.697
A graph data type can represent relationships of objects in the real world and can be used for analyzing given relationships. A hypergraph is a generalized version of a normal graph where a hyperedge represents a relationship between more than or equal to two objects. In this paper, we propose a method that searches isomorphic subgraphs in a data hypergraph to a given query hypergraph. In order to reduce high computational costs of subgraph isomorphism search, previous studies have explored candidates that might be possible answers for each query node and return isomorphic subgraphs that consist of a combination of candidates. In this research, to enhance search performance, we have decomposed a query hypergraph into several subgraphs and discovered the candidates for each subgraph with the proposed structural index, and the proposed search algorithm checks subgraph isomorphism. With real-world datasets, experimental results demonstrate that the search response time of the proposed method is at least 10 times faster than the existing methods.
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