• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.593-601
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• 2012

This paper develops and analyzes a new multiple-hypothesis Receiver Autonomous Integrity Monitoring (RAIM) algorithm as a candidate for future standard architecture. The proposed algorithm can handle simultaneous multiple failures as well as a single failure. It uses measurement residuals and satellite observation matrices of several consecutive epochs for Failure Detection and Exclusion (FDE). The proposed algorithm redueces the Minimum Detectable Bias (MDB) via the Relative RAIM (RRAIM) scheme. Simulation results show that the proposed algorithm can detect and filter out multiple failures in tens of meters.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1535-1536
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• 2008

GPS/INS/지자기 컴파스 통합 항법 시스템에서 지자기 컴파스의 이상 검출 방법을 제안하였다. 지자기 컴파스의 이상은 Hard iron과 Soft iron 효과에 의하여 발생하므로 이와 관련된 값을 통합 필터 측정치의 시험 통계치로 이용하여 검출하는 방법을 제안하였다. 제안한 방법의 효용성을 모의 실험과 차량 실험을 통하여 검증하였다.

• Smart Media Journal
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• v.13 no.4
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• pp.48-56
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• 2024

In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detection. The first method is an outlier detection using Mean Squared Error (MSE), and the second is an outlier detection using a Dynamic Threshold, which analyzes variability against the average value of previous data to identify outliers. The MSE-based method demonstrated a 94.98% accuracy rate, while the Dynamic Threshold method, which uses standard deviation, showed superior performance with 99.66% accuracy.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.745-751
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• 2009

Outlying or extreme observations are defined to be subject data for which one or more bioavailability measures are discordant with corresponding data for that subject and/or for the rest of the subjects in a study. The presence of outlying observations can have very serious consequences on the conclusions resulting from a bioequivalence study. Two statistical methods are proposed by generalizing the current well known methods and an illustrated example is presented with discussion.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.505-510
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• 2013

In order to detect and identify the GPS clock anomaly in the Differential GPS real environment, this paper addresses a method for minimizing the measurement noise of reference receivers. It estimates the real measurement noise that removed the uncommon error source from pseudorange measurement to minimize the measurement noise. Based on the output of two reference receivers, it first removes the uncommon errors, then optimizes the measurement noise by applying the correction data. Finally, it detects and identifies the satellite clock anomaly using the minimized measurement noise. The method will increase the availability of current DGPS reference system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.62-64
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• 2012

개선된 정확도 성능을 확보하기 위하여 보강 시스템을 이용한 많은 연구가 진행되고 있다. Network RTK는 다중 기준국의 반송파 측정치 보정정보를 이용하여 시공간 오차를 보강한 측위성능을 얻기 위한 기법으로 현재에도 꾸준히 연구되고 있다. 그러나 성능개선을 목적으로 한 알고리즘 개선안에 대한 연구는 지속적으로 연구되었지만, 무결성 확보를 위한 연구는 아직 연구된 바가 없다. 본 논문에서는 Network RTK에서의 무결성 확보를 위한 기초연구로 위성이상이 발생한 경우에 이상을 검출하고 이상 위성을 식별할 수 있는 알고리즘을 제안하였다. 그리고 시뮬레이터를 사용하여 오차 시나리오가 인가된 위성 신호를 생성하고, 이중주파수용 상용 수신기를 사용하여 수신한 데이터를 사용하여 제안한 알고리즘의 성능을 검증하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.63-73
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• 2011

The Pseudorange Corrections (PRC), which are used in DGPS as calibration messages, can contain outliers, noise, and anomalies, and these abnormal events are unpredictable. When those irregular PRC are used, the positioning error gets higher. In this paper, we propose a strategy of detecting and correcting outliers, noise, and anomalies by modeling the changing pattern of PRC through polynomial curve fitting techniques. To validate our strategy, we compared positioning errors obtained without PRC calibation with those with PRC calibration. As a result, we found that our algorithm performs very well; the horizontal RMS error was 3.84 m before the correction and 1.49 m after the correction.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.58-63
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• 2010

In this paper, we propose a simple technique for the detection of a frequency jump in the GPS clock behavior. GPS satellite atomic clocks have characteristics of a second order polynomial in the long term and a non-periodic frequency drift in the short term, showing a sudden frequency jump occasionally. As satellite clock anomalies influence on GPS measurements, it requires to develop a real time technique for the detection of the clock anomaly on the real-time GPS precise point positioning. The proposed technique is based on Teager energy which is mainly used in the field of various signal processing for the detection of a specific signal or symptom. Therefore, we employed the Teager energy for the detection of the jump phenomenon of GPS satellite atomic clocks, and it showed that the proposed clock anomaly detection strategy outperforms a conventional detection methodology.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.29-30
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• 2009

센서 데이터의 마이닝 기술은 의사결정을 위한 통합정보 및 예측정보를 제공하는 USN 지능형 미들웨어의 주요 구성 요소이다. 본 논문에서는 수질 센서 데이터 마이닝 시스템을 개발하기위해 대표적인 데이터 마이닝 기법인 클러스터링의 적용 모델을 소개한다. 적용 모델의 클러스터링을 통해 중간노드에서의 데이터 이상치 검출과 호스트에서의 시간대별 데이터 변화 검출이 가능하다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.157-163
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• 2022

Recently, with the development of automation in the industrial field, research on anomaly detection is being actively conducted. An application for anomaly detection used in factory automation is camera-based defect inspection. Vision camera inspection shows high performance and efficiency in factory automation, but it is difficult to overcome the instability of lighting and environmental conditions. Although camera inspection using deep learning can solve the problem of vision camera inspection with much higher performance, it is difficult to apply to actual industrial fields because it requires a huge amount of normal and abnormal data for learning. Therefore, in this study, we propose a network that overcomes the problem of collecting abnormal data with 72 geometric transformation deep learning methods using only normal data and adds an outlier exposure method for performance improvement. By applying and verifying this to the MVTec data set, which is a database for auto-mobile parts data and outlier detection, it is shown that it can be applied in actual industrial sites.