• Title, Summary, Keyword: Anomaly

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Tropospheric Anomaly Detection in Multi-Reference Stations Environment during Localized Atmospheric Conditions-(2) : Analytic Results of Anomaly Detection Algorithm

  • Yoo, Yun-Ja
    • Journal of Navigation and Port Research
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    • v.40 no.5
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    • pp.271-278
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    • 2016
  • Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.

Ebstein's Anomaly -A Case Report- (엡스타인 심기형 -1례 보고-)

  • 전찬규
    • Journal of Chest Surgery
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    • v.27 no.1
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    • pp.57-59
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    • 1994
  • Ebstein`s anomaly, a rare congenital cardiac anomaly, is characterized by downward displacement of abnormal tricuspid valve. Indication for surgical repair and the optimal surgical approach are still controversy. Recently, we experience a case of Ebstein anomaly, which was treated by atrilized right ventricular plication and annuloplasty. The patient was discharged with good result on 17th post-operative day.

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The Impacts of Decomposition Levels in Wavelet Transform on Anomaly Detection from Hyperspectral Imagery

  • Yoo, Hee Young;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.28 no.6
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    • pp.623-632
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    • 2012
  • In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.

The use of Local API(Anomaly Process Instances) Detection for Analyzing Container Terminal Event (로컬 API(Anomaly Process Instances) 탐지법을 이용한 컨테이너 터미널 이벤트 분석)

  • Jeon, Daeuk;Bae, Hyerim
    • The Journal of Society for e-Business Studies
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    • v.20 no.4
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    • pp.41-59
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    • 2015
  • Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.

Semi-Supervised Learning Based Anomaly Detection for License Plate OCR in Real Time Video

  • Kim, Bada;Heo, Junyoung
    • International journal of advanced smart convergence
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    • v.9 no.1
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    • pp.113-120
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    • 2020
  • Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

Complex Cardiac Anomaly Assiciated With the DiGeorge Syndrome; A Case Report (DiGeorge 증후군에 동반된 복합 심기형 치험 1례)

  • 문준호
    • Journal of Chest Surgery
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    • v.26 no.11
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    • pp.886-889
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    • 1993
  • The DiGeorge syndrome is a rare congenital anomaly of absent or hypoplastic thymus and parathyroid glands. Authors experienced a case of DiGeorge syndrome with complex cardiac anomaly. The complex cardiac anomaly was tetralogy of Fallot with origin of the right pulmonaly artery from the posterolateral ascending aorta.His face showed hypertelorism,short philtrum,"fish-like"mouth and micrognathia. This patient underwent total correction of tetralogy of Fallot and end-to-side anastomosis between right pulmonaly artery and side of main pulmonaly artery. He expired on postoperative second day due to right heart failure and hypoxia.d hypoxia.

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Dependence of spacecraft anomalies at different orbits on energetic electron and proton fluxes

  • Yi, Kangwoo;Moon, Yong-Jae;Lee, Ensang;Lee, Jae-Ok
    • The Bulletin of The Korean Astronomical Society
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    • v.41 no.1
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    • pp.45.2-45.2
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    • 2016
  • In this study we investigate 195 spacecraft anomalies from 1998 to 2010 from Satellite News Digest (SND). We classify these data according to types of anomaly : Control, Power, Telemetry etc. We examine the association between these anomaly data and daily peak particle (electron and proton) flux data from GOES as well as their occurrence rates. To determine the association, we use two criteria that electron criterion is >10,000 pfu and proton criterion is >100 pfu. Main results from this study are as flows. First, the number of days satisfying the criteria for electron flux has a peak near a week before the anomaly day and decreases from the peak day to the anomaly day, while that for proton flux has a peak near the anomaly day. Second, we found a similar pattern for the mean daily peak particle (electron and proton) flux as a function of day before the anomaly day. Third, an examination of multiple spacecraft anomaly events, which are likely to occur by severe space weather effects, shows that anomalies mostly occur either when electron fluxes are in the declining stage, or when daily proton peak fluxes are strongly enhanced. This result is very consistent with the above statistical studies. Our results will be discussed in view of the origins of spacecraft anomaly.

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Anomaly Detection and Diagnostics (ADD) Based on Support Vector Data Description (SVDD) for Energy Consumption in Commercial Building (SVDD를 활용한 상업용 건물에너지 소비패턴의 이상현상 감지)

  • Chae, Young-Tae
    • Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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    • v.12 no.6
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    • pp.579-590
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    • 2018
  • Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

Clinical Report of 103 Cases of Open Heart Surgery in 1984 (1984 년도 년간 개심술 103례 보고)

  • 김규태
    • Journal of Chest Surgery
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    • v.18 no.3
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    • pp.398-406
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    • 1985
  • 103 cases of open heart surgery were performed in the Department of Thoracic and Cardiovascular Surgery, Kyungpook National University Hospital in 1984. There were 90 congenital cardiac anomaly and 13 acquired heart diseases. Out of 90 congenital cardiac anomaly, 63 cases of acyanotic group and 27 cases of cyanotic group were noted. In 63 cases of acyanotic group, 11 ASD, 45 VSD and 7 other acyanotic anomaly were included. In 27 cases of cyanotic group, 4 Trilogy of Fallot, 15 TOF, 3 Pentalogy of Gasul and 5 other cyanotic anomaly were found. Among 13 cases of acquired heart diseases, 12 valvular lesions and 1 atrial myxoma were noted. Two open mitral commissurotomy and ten valve replacements were performed for 12 valve lesions. The frequent complications were acute respiratory insufficiency and low cardiac output syndrome occurred in 5 cases. The perioperative mortality was 4.8% in acyanotic congenital cardiac anomaly, 7.4% in cyanotic congenital cardiac anomaly, and 0% in acquired heart diseases. Overall mortality for 103 cases of open heart surgery was 4.9%.

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