• Journal of Industrial Convergence
• /
• v.21 no.8
• /
• pp.43-49
• /
• 2023

Since 2018, due to climate change, heat waves and cold waves have caused gradual damage to social infrastructure. Since the damage caused by cold weather has increased every year due to climate change in recent 4 years, the damage that was limited to a specific area is now appearing all over the country, and a lot of efforts are being concentrated from experts in various fields to minimize this. However, it is not easy to study real-time observation of sudden abnormal low temperature in existing studies to reflect local characteristics in discontinuously measured data. In this study, based on the weather-related data that affects the occurrence of cold-weather damage, we developed an algorithm pattern that can identify the time when abnormal cold temperatures occurred after searching for weather patterns at the time of cold-weather damage. The results of this study are expected to be of great help to the related field in that it is possible to confirm the time when the abnormal low temperature occurs due to the data generated in real time without relying on the past data.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.13-20
• /
• 2023

In this paper, the actual data of VOC reduction facilities was analyzed through a model that detects and predicts data anomalies. Using the USAD model, which shows stable performance in the field of anomaly detection, anomalies in real-time data are detected and sensors that cause anomalies are searched. In addition, we propose a method of predicting and warning, when abnormalities that time will occur by predicting future outliers with an auto-regressive model. The experiment was conducted with the actual data of the VOC reduction facility, and the anomaly detection test results showed high detection rates with precision, recall, and F1-score of 98.54%, 89.08%, and 93.57%, respectively. As a result, averaging of the precision, recall, and F1-score for 8 sensors of detection rates were 99.64%, 99.37%, and 99.63%. In addition, the Hamming loss obtained to confirm the validity of the detection experiment for each sensor was 0.0058, showing stable performance. And the abnormal prediction test result showed stable performance with an average absolute error of 0.0902.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1995.10a
• /
• pp.284-288
• /
• 1995

최근 애매성이 수반되는 정보를 Zadeh는 멤버쉽함수(membership function)를 이용하여 새로운 정보처리 방식으로서 퍼지이론을 제안하였고, 그후 의료계에서도 퍼지이론을 도입한 진단법들이 제안되었다. 회전기계의 이상진단법으로는 주파수득점법(Point counting method), 퍼지역연산법(Inverse method of fuzzy theory)등이 보고되고 있으며, 저자들도 퍼지이론을 이용하여 구름베어링의 결함진단, 회전기계의 간이 이상진단법등을 보고하였다. 이들은 주로 진동주파수의 스펙트럼 데이터 만을 이용하고 있고, 다른 많은 데이터를 복합적으로 이용할 수 없다. 이 때문에 주로 소규모 문제의 간이진단에서는 효과적이나 진단대상이 복잡하고 대규모로 되면 보다 정확한 원인 추정이 곤란하게 된다. 또한 수치데이터만을 취급할 수 있으므로 진동전문가가 진단에 이용하는 각종의 수치화 될 수 없는 데이터(언어적인 정보)가 취급될 수 없다. 따라서 이들의 진단법은 개략적인 진단은 가능하나 상세한 원인까지는 진단할 수 없는 단점이 있다. 회전기계의 이상판단시 참고가 되는 각종 정보로는 주로 진동진폭의 크기, 진폭과 위상의 변화, 진폭의 변화, 진동파형, 진동벡터의 시간변화 등이 있고, 이들은 수치적으로 표현할 수 있는 계량데이터와 판단의 경계가 불명확한 언어정보(범위데이터)로 나눌 수 있다. 후자는 애매성(fuzziness)을 많이 포함하고 있으며, 엄밀히 측정되는 수치데이터에서도 퍼지성을 가지고 있다. 이러한 언어적인 정보의 애매성을 퍼지추론에서는 [수치적 진리치](numeric truth)와 [언어적 진리치](linguistic truth)의 개념으로 표현하게 되었다. 수치적 진리치는 확실함의 척도를 [0,1] 사이의 수치를 이용하여 표현하고 있으며, 이 수치는 소견의 확실도로서 가능성을 표현한 것이다. 예를 들면, 진동진폭 스펙트럼상에 2X 성분이 상당히 크게 나타나 정렬불량의 가능성이 0.7 정도라고 판정하는 것 등은 이러한 수치적진리치를 이용하는 방법이다. 그러나 상기의 수치적 표현만으로는 확실도를 한개의 수치로서 대표하게 하는 것은 진단의 정밀도에 문제가 있을 것으로 생각된다. 따라서 언어적진리치가 도입되어 [상당히 확실], [확실], [약간 확실] 등의 언어적인 표현을 이용하여 애매성을 표현하게 되었다. 본 논문에서는 간이진단 결과로부터 추출된 애매한 진단결과중에서 가장 가능성이 높은 이상원인을 복수로 선정하고, 여러 종류의 수치화할 수 없는 언어적(linguistic)인 정보ㄷㄹ을 if-then 형식의 퍼지추론으로 종합하는 회전기계의 이상진단을 위한 정밀진단 알고리즘을 제안하고 그 유용성을 검토한다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.27 no.1
• /
• pp.8-14
• /
• 2023

The existing self-supervised learning-based CutPaste generated synthetic data by cutting and attaching specific patches from normal images and then performed anomaly detection. However, this method has a problem in that there is a clear difference in the boundary of the patch. NSA for solving these problems have achieved higher anomaly detection performance by generating natural synthetic data through Poisson Blending. However, NSA has the disadvantage of having many hyperparameters that need to be adjusted for each class. In this paper, synthetic data similar to normal were generated by a simple method of making the size of the synthetic patch very small. At this time, since the patches are so locally synthesized, models that learn local features can easily overfit synthetic data. Therefore, we performed anomaly detection using gMLP, which learns global features, and even with simple synthesis methods, we were able to achieve higher performance than conventional self-supervised learning techniques.

• Journal of Internet Computing and Services
• /
• v.23 no.6
• /
• pp.39-48
• /
• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.6
• /
• pp.1063-1068
• /
• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar due to its observation principle and disturb weather forecasting process. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo with data mining techniques. This paper conducts researches about implementation of classification method which can separate the anomalous propagation echo in the raw radar data using naive Bayes classifier with various kinds of observation results. Considering that collected data has a class imbalanced problem, this paper includes SMOTE method. It is confirmed that the fine classification results are derived by the suggested classifier with balanced dataset using actual appearance cases of the echo.

• Journal of the Korea Convergence Society
• /
• v.12 no.2
• /
• pp.1-6
• /
• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2011.11a
• /
• pp.117-120
• /
• 2011

240 Hz 이상의 높은 프레임율을 가지는 LCD 기반 평판 FHD급 디스플레이 시스템은 높은 프레임율로 인하여 디스플레이 패널로 전송해야하는 유효한 데이터율이 1.9GB/s까지 이르게 되며, 수평/수직 동기를 감안하면 2GB/s 이상의 데이터 전송 대역이 필요하다. DRAM을 이용하여 이런 데이터 대역폭을 제공하려면 다수의 메모리 장치를 사용해야하기 때문에, 비용 상승, 전력소모량 증가 등의 문제를 야기한다. 이런 문제를 해결하기 위하여 본 논문에서는 JPEG 기반의 정지 영상 압축 시스템을 제안한다. 제안한 시스템은 8개의 디코더가 동시에 동작하는 구조를 가지고 있으며, 단일 데이터 열로부터 8개의 데이터 열을 용이하게 구분할 수 있도록 128-bit 데이터에 정렬된 64-bit 마커를 사용한다. 제안한 64-bit 마커는 마커 에뮬레이션을 야기하지 않도록 설계되었기 때문에, 인코더와 디코더의 구현 복잡도를 낮출 수 있고 단일 데이터열을 8개의 데이터열로 분리하는 작업을 매우 용이하게 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.815-818
• /
• 2009

USN 환경에서의 센서는 일반적으로 많은 제약사항을 가지고 있다. 센서의 제한된 전원의 문제는 센서의 동작 수명과 관련된 것으로 최근의 연구들에서 중요 이슈가 되고 있다. 본 논문에서는 고도화되는 USN 환경에서 발생되는 다차원 스트림데이터를 수집하는데 있어서 센서의 전원 문제를 해결하고 데이터를 효율적으로 수집하기 위한 기법을 제안한다. 제안하는 기법은 센서에 이상 이벤트를 정의하고 이상 이벤트에 해당하는 데이터를 수집하는 경우에만 데이터를 전송하도록 하여 센서의 통신 빈도를 줄여 센서의 전원 문제를 해결하고 스트림 데이터를 기호화 하여 처리함으로써 스트림 데이터를 효율적으로 수집할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.82-84
• /
• 2021

Through the smart factory construction project, sensor data such as power, vibration, pressure, and temperature are collected from production facilities, and services such as predictive maintenance, defect prediction, and abnormality detection are developed through data analysis. In general, in the case of manufacturing data, because the imbalance between normal and abnormal data is extreme, an anomaly detection service is preferred. In this paper, FFT method is used to extract feature data of manufacturing data as a pre-stage of the anomaly detection service development. Using this method, we classified the produced products and confirmed results. In other words, after FFT of the representative pattern for each product, we verified whether product classification was possible or not, by calculating correlation coefficient.