• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.43-52
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• 2024

This paper presents an anomaly detection system that uses an LSTM-Autoencoder model to identify early-stage anomalies in the blade pitch system of floating wind turbines. The sensor data used in power plant monitoring systems is primarily composed of multivariate time-series data for each component. Comprising two unidirectional LSTM networks, the system skillfully uncovers long-term dependencies hidden within sequential time-series data. The autoencoder mechanism, learning solely from normal state data, effectively classifies abnormal states. Thus, by integrating these two networks, the system can proficiently detect anomalies. To confirm the effectiveness of the proposed framework, a real multivariate time-series dataset collected from a wind turbine model was employed. The LSTM-autoencoder model showed robust performance, achieving high classification accuracy.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.1-8
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• 2021

In this paper, we propose the construction of an IOT that monitors foresters' physical abnormalities in real time, performs emergency measures, and provides alarms for natural disasters or heatstroke such as a nearby forest fire or landslide. Nodes provided to foresters include 6-axis sensors, temperature sensors, GPS, and LoRa, and transmit the measured data to the network server through the gateway using LoRa communication. The network server uses 6-axis sensor data to determine whether or not a forester has any signs of abnormal body, and performs emergency measures by tracking GPS location. After analyzing the temperature data, it provides an alarm when there is a possibility of heat stroke or when a forest fire or landslide occurs in the vicinity. In this paper, it was confirmed that the real-time detection of physical abnormalities of foresters and the establishment of disaster early warning IOT is possible by analyzing the data obtained by constructing a node and a gateway and constructing a network server.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2037-2042
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• 2017

The manufacturing facility is generally operated by a pre-set program under the existing factory automation system. On the other hand, the manufacturing facility must decide how to operate autonomously in Industry 4.0. Determining the operation mode of the production facility itself means, for example, that it detects the abnormality such as the deterioration of the facility at the shop-floor, prediction of the occurrence of the problem, detection of the defect of the product, In this paper, we propose a manufacturing process modeling using a queue for detection of manufacturing process abnormalities at the shop-floor, and detect abnormalities in the modeling using SVM, one of the machine learning techniques. The queue was used for M / D / 1 and the conveyor belt manufacturing system was modeled based on ${\mu}$, ${\lambda}$, and ${\rho}$. SVM was used to detect anomalous signs through changes in ${\rho}$.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.135-143
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• 2022

As the world has implemented social distancing and telecommuting due to the spread of COVID-19, real-time streaming sessions based on routing protocols have increased dependence on the Internet due to the activation of video and voice-related content services and cloud computing. BGP is the most widely used routing protocol, and although many studies continue to improve security, there is a lack of visual analysis to determine the real-time nature of analysis and the mis-detection of algorithms. In this paper, we analyze BGP data, which are powdered as normal and abnormal, on a real-world basis, using an anomaly detection algorithm that combines statistical and post-processing statistical techniques with Rule-based techniques. In addition, we present an interactive spatio-temporal analysis plan as an intuitive visualization plan and analysis result of the algorithm with a map and Sankey Chart-based visualization technique.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.69-79
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• 2010

To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1141-1148
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• 2016

This paper proposed a heart attack predictive monitoring system using QRS pattern of ECG for wearable healthcare. It detects abnormal heart pattern with a ECG (X, Y) coordinate pattern DB on wearable monitoring smart watch. We showed the acute heart failure prevention system and method with a proposed scheme. Especially, It proved the method which can do first aid in gold time through abnormal heart analysis with a digital ECG(X, Y) pattern information when acute heart failure occurs.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.1
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• pp.85-94
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• 2022

To detect anomaly signs of thermal runaway in advance, this study analyzed the signals from various sensors installed in lithium-ion batteries. The thermal runaway mechanism was analyzed, and measurement variables for anomalies of a battery cell were surface temperature, strain, and gas concentration. The changes and characteristics of three variables during the thermal runaway process were analyzed under the abuse environment: the overheat and the overcharge. In experiment, the thermal runaway of the battery proceeded in the initial developing stage, the outgassing stage, and the ignition stage. Analysis from the measured data indicated that the suitable variable to detect all stages of thermal runaway is the surface temperature of the battery, and surface strain is alternative.

• Journal of IKEEE
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• v.25 no.3
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• pp.430-436
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• 2021

The non-contact body temperature measurement system is one of the key factors, which is manage febrile diseases in mass facilities using optical and thermal imaging cameras. Conventional systems can only be used for simple body temperature measurement in the face area, because it is used only a deep learning-based face detection algorithm. So, there is a limit to detecting abnormal symptoms of the infants and young children, who have difficulty expressing their opinions. This paper proposes an improved facial expression recognition algorithm for detecting abnormal symptoms in infants and young children. The proposed method uses an object detection model to detect infants and young children in an image, then It acquires the coordinates of the eyes, nose, and mouth, which are key elements of facial expression recognition. Finally, facial expression recognition is performed by applying a selective sharpening filter based on the obtained coordinates. According to the experimental results, the proposed algorithm improved by 2.52%, 1.12%, and 2.29%, respectively, for the three expressions of neutral, happy, and sad in the UTK dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.1001-1004
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• 2018

As the industry generates a lot of data, it is increasingly dependent on the IoT platform. For this reason, the performance and anomaly detection of IoT platform is becoming an important factor. In this paper, we propose a system model of IoT platform that detects device anomaly without performance issue. The proposed system uses Micro Batch which calculates the data transmission cycle to provide Soft Real-time service. In the industry, it was difficult to collect abnormal data, so the Hotelling's $T^2$ model was applied to the data analysis experiment. And the Hotelling's $T^2$ model successfully detected anomalies.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2011.11a
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• pp.514-517
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• 2011

화재감지 및 경보시스템의 설계 목표는 화재발생 초기 단계에서 화재징후를 발견, 피난의 개시를 신속하게 통수하는 것이다. 화재감지기는 다양한 건물 및 환경에 설치되기 때문에 주위 온도 및 가연물의 종류 등 환경적 측면을 심층적으로 고려하지 않으면 적절하게 감지하지 못하는 경우가 발생할 수 있다. 즉, 가연물 종류 등을 고려한 설계에 따라 설치된 화재감지기는 화재시 설계치 대로 조기에 화재를 감지하여 화재 예방 및 화재로 인한 피해를 최소화할 수 있는 것이다. 본 논문은 화재시 조기에 화재를 감지기하여 건축물내 인명피난을 목적으로 설치되는 화재감지기의 응답특성을 분석한 연구이다. 화재감지기의 응답특성을 분석하기 위하여 다양한 가연물을 발생시키고 화재감지기 종류별로 설치한 다음 각 화재감지기의 응답특성을 분석하였다. 그 결과 정온식 열감지기는 열방출률이 적은 화재를 조기에 감지하는 것에 적합하지 않은 것으로 나타났다. 광전식 연기감지기는 회색 계통의 목재류 화재성상에서 응답특성이 떨어졌고, 동일한 공간에서 화원의 수평거리와 동작시간이 비례한다고 볼 수 없었다.