• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.2
• /
• pp.313-316
• /
• 2022

In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an analysis model is created that judges the data received from the charger as normal and abnormal. In addition, a model is created to determine the cause of the abnormality using the existing charging data based on the analysis of the type of charger abnormality. Finally, it is solved using unsupervised learning method to find new patterns of abnormal data.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.6
• /
• pp.141-146
• /
• 2015

In order to classify and analyze variously compounded sound and voice signal from FPGA microphone, there are numerous systems to detect abnormality signal, however, they have a lot of problems to implement the abnormality signal detection efficiently and effectively. Therefore, we proposed a method that implements classifying the signal effectively and outputting the detection efficiently based on the algorithm applied FIFO structure (First-in First-out) by using microphone sensor which able to input the sound signal, and statistical variance and coefficient of variation (CV). The result showed 96.3% detection when the experiment was performed more than 100 times with the proposed algorithm applied system.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.9
• /
• pp.723-732
• /
• 2016

This paper proposes an approach to fused navigation of an unmanned surface vehicle(USV) and to detection of the outlier or interference of global positioning system(GPS). The method fuses available sensor measurements through extended Kalman filter(EKF) to find the location and attitude of the USV. The method uses error covariance of EKF for detection of GPS outlier or interference. When outlier or interference of the GPS is detected, the method excludes GPS data from navigation process. The measurements to be fused for the navigation are GPS, acceleration, angular rate, magnetic field, linear velocity, range and bearing to acoustic beacons. The method is tested through simulated data and measurement data produced through ground navigation. The results show that the method detects GPS outlier or interference as well as the GPS recovery, which frees navigation from the problem of GPS abnormality.

• Journal of Korean Society for Quality Management
• /
• v.49 no.3
• /
• pp.327-340
• /
• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.493-505
• /
• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Journal of Multimedia Information System
• /
• v.3 no.2
• /
• pp.35-42
• /
• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.6
• /
• pp.1-7
• /
• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.6
• /
• pp.287-294
• /
• 2016

APT attack aimed at the interruption of information and communication facilities and important information leakage of companies. it performs an attack using zero-day vulnerabilities, social engineering base on collected information, such as IT infra, business environment, information of employee, for a long period of time. Fragmentary response to cyber threats such as malware signature detection methods can not respond to sophisticated cyber-attacks, such as APT attacks. In this paper, we propose a cyber intrusion detection model for countermeasure of APT attack by utilizing heterogeneous system log into big-data. And it also utilizes that merging pattern-based detection methods and abnormality detection method.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.9
• /
• pp.33-40
• /
• 2022

In this paper, we propose a system that analyzes drone photographic images of panel-type factory roofs and conducts abnormal detection of bolts. Currently, inspectors directly climb onto the roof to carry out the inspection. However, safety accidents caused by working conditions at high places are continuously occurring, and new alternatives are needed. In response, the results of drone photography, which has recently emerged as an alternative to the dangerous environment inspection plan, will be easily inspected by finding the location of abnormal bolts using deep learning. The system proposed in this study proceeds with scanning the captured drone image using a sample image for the situation where the bolt cap is released. Furthermore, the scanned position is discriminated by using AI, and the presence/absence of the bolt abnormality is accurately discriminated. The AI used in this study showed 99% accuracy in test results based on VGGNet.

• Ecology and Resilient Infrastructure
• /
• v.6 no.2
• /
• pp.120-127
• /
• 2019

Medium-scale levee experiments were performed to monitor the infiltration and failure of levee body by applying fiber optic temperature sensing. In this study, bio-polymer soil was spread in the levee slope to increase the strength and intensity. Therefore, the infiltration and failure by overflows were produced in a different way compared to general soil type of levees. This was also observed in the experiment data for temperature changes monitored by fiber-optic distributed temperature sensing system. Through the analysis of temperature changes at specific location by time, the location and initiation time for physical changes and infiltration in levee body could be identified based on temperature variation. In this experiment, the time of rapid changes in temperature was ahead in the inland slope rather than the forceland slope. It was corresponding to the levee failure sequence of first inland slope failure and then the forceland slope failure.