• 한국컴퓨터정보학회논문지
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• 제21권12호
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• pp.35-41
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• 2016

In this paper, we propose a new real-time dead pixel detection method based on spatial compare filtering, which are usually used in the small target detection. Actually, the soft dead and the small target are cast in the same mold. Our proposed method detect and remove the dead pixels as applying the spatial compare filtering, into the pixel outputs of a detector after the non-uniformity correction. Therefore, we proposed method can effectively detect and replace the dead pixels regardless of the non-uniformity correction performance. In infrared camera, there are usually many dead detector pixels which produce abnormal output caused by manufactural process or operational environment. There are two kind of dead pixel. one is hard dead pixel which electronically generate abnormal outputs and other is soft dead pixel which changed and generated abnormal outputs by the planning process. Infrared camera have to perform non-uniformity correction because of structural and material properties of infrared detector. The hard dead pixels whose offset values obtained by non-uniformity correction are much larger or smaller than the average can be detected easily as dead pixels. However, some dead pixels(soft dead pixel) can remain, because of the difficulty of uncleared decision whether normal pixel or abnormal pixel.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.25-27
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• 2021

최근 딥러닝 기술의 발전으로 CCTV 카메라를 통해 획득한 영상 정보에서 객체의 이상행동을 분석하기 위한 컴퓨터 비전 기반 AI 기술들이 연구되었다. 위험 지역이나 보안 지역에는 범죄 예방 및 경계 감시를 위해 감시카메라가 설치되어 있는 경우가 다수 존재한다. 이러한 이유로 기업들에서는 감시카메라 환경에서 침입, 배회, 낙상, 폭행 같은 주요한 상황을 판단하기 위한 연구들이 진행되고 있다. 본 논문에서는 객체 검출 및 추적 방법을 사용한 실시간 이상 행위 분석 알고리즘을 제안한다.

• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.256-266
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• 2016

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

• 대한의용생체공학회:의공학회지
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• 제25권2호
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• pp.129-135
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• 2004

재택건강관리 시스템은 주로 정상인들로부터 빈번하게 측정한 생체신호의 실시간 처리과정을 필요로 한다. 본 논문에서는 이러한 환경에서 측정되는 심전도 신호에서 DRS를 검출하기 위한 단순화된 알고리즘과 검출된 QRS의 정상과 비정상 여부만을 분류하는 알고리즘에 대하여 기술한다. 기존에 사용되고 있는 실시간 QRS 검출 알고리즘을 세분화하여 단순화된 QRS 검출 알고리즘을 제안함으로서 저가형 소형 단말기에서도 사용이 가능하도록 하였다. 또한 검출된 QRS들로부터 QRS 폭, R-R 간격, DRS 형태변수를 추출하여 QRS의 정상과 비정상을 판단하는 알고리즘을 개발하였다. 단순화된 QRS 검출기의 성능과 정상과 비정상의 분류성능은 각각 약 99%와 96%로 나타났다. 본 논문에서 제안된 QRS 검출과 분류를 위한 알고리즘들은 복잡한 신호처리 과정이 필요치 않으므로 재택건강관리 시스템에서의 실시간 심전도처리에 사용될 수 있을 것이다

• Journal of Computing Science and Engineering
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• 제7권4호
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• pp.272-284
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• 2013

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.619-628
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• 2007

This study is devoted to suggesting the norm of detection abnormal users who are inferior to the other users in the recommender system compared with estimation accuracy. To select the abnormal users, we propose the pre-filtering method by using the preference ratings to the item rated by users. In this study, the experimental result shows the possibility of detecting the abnormal users before the process of preference estimation through the prediction algorithm. And It will be possible to improve the performance of the recommender system by using this detecting norm.

• Smart Structures and Systems
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• 제20권2호
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• pp.219-229
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• 2017

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

• 한국식물병리학회지
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• 제12권3호
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• pp.363-367
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• 1996

배나무잎 이상반점증상의 이병여부를 조기에 판별할 수 있는 가장 간편한 검정방법을 개발코자 시험하였다. 접목시기는 늦어질수록 병징발현율이 감소하였다. 접목방법은 2중절접, 2중삭아절접 순으로 양호하였으나 숙련된 기술이 필요한 이중절접방법보다 간편한 2중삭아절접 방법이 대량검정에 적합하였다. 이상반점증상의 전염에 필요한 최소 접촉시간은 1일 이상이었고 칼루스가 형성되어 접목부위가 활착된 21일 이후에서 발병이 가장 높았다.

• 디지털산업정보학회논문지
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• 제13권1호
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• pp.49-57
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• 2017

IoT can be connected through a single network not only objects which can be connected to existing internet but also objects which has communication capability. This IoT environment will be a huge change to the existing communication paradigm. However, the big security problem must be solved in order to develop further IoT. Security mechanisms reflecting these characteristics should be applied because devices participating in the IoT have low processing ability and low power. In addition, devices which perform abnormal behaviors between objects should be also detected. Therefore, in this paper, we proposed D-IDS technique for efficient detection of malicious attack nodes between devices participating in the IoT. The proposed technique performs the central detection and distribution detection to improve the performance of attack detection. The central detection monitors the entire network traffic at the boundary router using SVM technique and detects abnormal behavior. And the distribution detection combines RSSI value and reliability of node and detects Sybil attack node. The performance of attack detection against malicious nodes is improved through the attack detection process. The superiority of the proposed technique can be verified by experiments.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.1038-1042
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• 2002

Monitoring of the drill wear and hole quality change is conducted during the drilling process. Cutting force measured by tool dynamometer is a evident feature estimating abnormal state of drilling. One major difficulty in using tool dynamometer is that the work piece must be mounted on the dynamometer, and thus the machining process is disturbed and discontinuous. Acoustic transducer do not disturb the normal machining process, and provide a relatively easy way to monitor a machining process for industrial application. For this advantage, AE signal is used to estimate the abnormal state. In this study vision system is used to detect flank wear tendency and hole quality, there are many formal factors in hole quality decision circularity, cylindricity, straightness, and so on, but these are difficult to measure in on-line monitoring. The movement of hole center and increasement of hole diameter is presented to determine hole quality As the results of this experiment, AE RMS signal and measurements by vision system are shown the similar tendency as abnormal state of drilling. And detection of the abnormal states using BPNs was achieved 96.4% reliability.