Digital Library[ Search Result ]
A GRU-based Time-Series Forecasting Method using Patching
http://doi.org/10.5626/JOK.2024.51.7.663
Time series forecasting plays a crucial role in decision-making within various fields. Two recent approaches, namely, the patch time series Transformer (PatchTST) and the long-term time series foraging linear (LTSF-Linear) of the MLP structure have shown promising performance in this area. However, PatchTST requires significant time for both model training and inference, while LTSF-Linear has limited capacity due to its simplistic structure. To address these limitations, we propose a new approach called patch time series GRU (PatchTSG). By leveraging a Gated Recurrent Unit (GRU) on the patched data, PatchTSG reduces the training time and captures valuable information from the time series data. Compared to PatchTST, PatchTSG achieves an impressive reduction in learning time (up to 82%) and inference time (up to 46%).
A GCN-based Time-Series Data Anomaly Detection Method using Sensor-specific Time Lagged Cross Correlation
Kangwoo Lee, Yunyeong Kim, Sungwon Jung
http://doi.org/10.5626/JOK.2023.50.9.805
Anomaly detection of equipment through time series data is a very important because it can prevent further damage and contribute to productivity improvement. Although research studies on time series data anomaly detection are being actively conducted, but they have the following restrictions. First, unnecessary false alarms occur because correlations with other sensors are not considered. Second, although complete graph modeling and GAT have been applied to analyze the correlation of each sensor, this method requires a lot of time due to the increase in unnecessary operations. In this paper, we propose SC-GCNAD(Sensor-specific Correlation GCN Anomaly Detection) to address these problems. SC-GCNAD can analyze the exact correlation of each sensor by applying TLCC that reflects characteristics of time series data. It utilize GCN with excellent model expressiveness. As a result, SC-GCNAD can improve F1-Score by up to 6.37% and reduce analysis time by up to 95.31% compared to the baseline model.
Detecting Design Infringement Using Multi-Modal Visual Data and Auto Encoder based on Convolutional Neural Network
Jeonggeol Kim, Jiyou Seo, Chanjae Lee, Seongmin Jo, Seungmin Kim, Seokmin Yoon, Young Yoon
http://doi.org/10.5626/JOK.2022.49.2.137
Recently, it has become very difficult to distinguish between counterfeit products and authentic goods, and the volume of these forgeries is increasing at an alarming rate. Prompt detection of these counterfeit products is challenging since only humans can identify these forgeries through trained expertise. In this paper, given the photograph and design drawing, we use convolutional neural networks and auto-encoders to detect the possible infringement of design rights without dissembling or damaging the suspected items. We have developed an easy-to-expand system that supports the constant addition of new goods to be examined. We present the result of our system tested with a set of authentic and forged goods.
Improving Subgraph Isomorphism with Pruning by Bipartite Matching
http://doi.org/10.5626/JOK.2021.48.9.973
In recent years, it has become increasingly important to efficiently solve NP-hard graph problems. One of the fundamental problems in graph analysis is subgraph isomorphism. Given a query graph and a data graph, the subgraph isomorphism problem is to determine whether there is an embedding of the query graph in the data graph. Although a lot of practical algorithms have been developed for the problem, existing algorithms showed limited running time scalability in dealing with many real-world graphs. In this paper, we propose a new pruning technique based on bipartite matching which enables us to capture and remove redundancies in the search space. We also conduct experiments on several real datasets to show effectiveness of our technique.
An Autonomous IoT Programming Paradigm Supporting Neuromorphic Models and Machine Learning Models
Sanglok Yoo, Keonmyung Lee, Youngsun Yun, Jiman Hong
http://doi.org/10.5626/JOK.2020.47.3.310
The demands and expectations of the IoT (Internet of Things) application services are increasing with the development of sensor technology and high-speed communication infrastructures. Even with many sensors operating and networked, transmission of all the sensor data to the server for processing is inefficient in terms of communication bandwidth and storage space. Meanwhile, with the recent development of artificial intelligence technology, the demand for intelligent processing of the IoT is increasing. This paper proposes a programming paradigm that can apply neuromorphic model-based models and machine learning models relative to IoT clients, and a programming paradigm that applies machine learning models and knowledge processing models relative to IoT servers. The proposed programming paradigm is expected to be valuable for the intelligent IoT as well as for autonomous IoT environments in that various AI modules can be applied relative to IoT clients and server programs.
Transformation Method for a State Machine to Increase Code Coverage
YoungDong Yoon, HyunJae Choi, HeungSeok Chae
Model-based testing is a technique for performing the test by using a model that represents the behavior of the system as a system specification. Industrial domains such as automotive, military/aerospace, medical, railway and nuclear power generation require model-based testing and code coverage-based testing to improve the quality of software. Despite the fact that both model-based testing and code coverage-based testing are required, difficulty in achieving a high coverage using model-based testing caused by the abstraction level difference between the test model and the source code, results in the need for performing model-based testing separately. In this study, to overcome the limitations of the existing model-based testing, we proposed the state machine transformation method to effectively improve the code coverage using the protocol state machine, one of the typical modeling methods is used as the test model in model-based testing, as the test model. In addition, we performed a case study of both systems and analyzed the effectiveness of the proposed method.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr