• Title/Summary/Keyword: Anomaly

Search Result 2,202, Processing Time 0.033 seconds

Ebstein`s anomaly ; St. Jude Medical valve replacement using partial artificial annulus formation - A Case Report - (Ebstein 기형에 인공판윤을 이용한 금속형 St. Jude Medical 인공판막 대치술)

  • Lee, Jong-Guk;Jo, Jae-Min
    • Journal of Chest Surgery
    • /
    • v.25 no.8
    • /
    • pp.826-831
    • /
    • 1992
  • Ebstein`s anomaly is characterized by a downward displacement of a malformed tricuspid valve, The ideal surgical management of Ebstein`s anomaly is not yet established. Recently we experience one case of Ebstein`s anomaly, which was treated sussessfully by partial artificial annulus formation, and tricuspid valve replacement with St. Jude Medical valve. We have achieved excellent results with mechanical valve replacement and partial artificial annulus formation using wessex pericardial patch. On follow up for 4 years, the patient is well and in functional class I.

  • PDF

LOCAL ANOMALIES AROUND THE THIRD PEAK IN THE CMB ANGULAR POWER SPECTRUM OF WMAP 7-YEAR DATA

  • Ko, Kyeong Yeon;Park, Chan-Gyung;Hwang, Jai-Chan
    • Journal of The Korean Astronomical Society
    • /
    • v.46 no.2
    • /
    • pp.75-91
    • /
    • 2013
  • We estimate the power spectra of the cosmic microwave background radiation (CMB) temperature anisotropy in localized regions of the sky using the Wilkinson Microwave Anisotropy Probe (WMAP) 7-year data. We find that the north and south hat regions at high Galactic latitude ($|b|{\geq}30^{\circ}C$) show an anomaly in the power spectrum amplitude around the third peak, which is statistically significant up to 3. We try to explain the cause of the observed anomaly by analyzing the low Galactic latitude ($|b|$ < $30^{\circ}C$) regions where the galaxy contamination is expected to be stronger, and the regions weakly or strongly dominated byWMAP instrument noise. We also consider the possible effect of unresolved radio point sources. We find another but less statistically significant anomaly in the low Galactic latitude north and south regions whose behavior is opposite to the one at high latitude. Our analysis shows that the observed north-south anomaly at high latitude becomes weaker on regions with high number of observations (weak instrument noise), suggesting that the anomaly is significant at sky regions that are dominated by the WMAP instrument noise. We have checked that the observed north-south anomaly has weak dependences on the bin-width used in the power spectrum estimation, and on the Galactic latitude cut. We also discuss the possibility that the detected anomaly may hinge on the particular choice of the multipole bin around the third peak. We anticipate that the issue of whether or not the anomaly is intrinsic one or due to WMAP instrument noise will be resolved by the forthcoming Planck data.

Anomaly behavior detection using Negative Selection algorithm based anomaly detector (Negative Selection 알고리즘 기반 이상탐지기를 이용한 이상행 위 탐지)

  • 김미선;서재현
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2004.05b
    • /
    • pp.391-394
    • /
    • 2004
  • Change of paradigm of network attack technique was begun by fast extension of the latest Internet and new attack form is appearing. But, Most intrusion detection systems detect informed attack type because is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, visibilitys to apply human immunity mechanism are appearing. In this paper, we create self-file from normal behavior profile about network packet and embody self recognition algorithm to use self-nonself discrimination in the human immune system to detect anomaly behavior. Sense change because monitors self-file creating anomaly detector based on Negative Selection Algorithm that is self recognition algorithm's one and detects anomaly behavior. And we achieve simulation to use DARPA Network Dataset and verify effectiveness of algorithm through the anomaly detection rate.

  • PDF

Normal data based rotating machine anomaly detection using CNN with self-labeling

  • Bae, Jaewoong;Jung, Wonho;Park, Yong-Hwa
    • Smart Structures and Systems
    • /
    • v.29 no.6
    • /
    • pp.757-766
    • /
    • 2022
  • To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

Emerging Topic Detection Using Text Embedding and Anomaly Pattern Detection in Text Streaming Data (텍스트 스트리밍 데이터에서 텍스트 임베딩과 이상 패턴 탐지를 이용한 신규 주제 발생 탐지)

  • Choi, Semok;Park, Cheong Hee
    • Journal of Korea Multimedia Society
    • /
    • v.23 no.9
    • /
    • pp.1181-1190
    • /
    • 2020
  • Detection of an anomaly pattern deviating normal data distribution in streaming data is an important technique in many application areas. In this paper, a method for detection of an newly emerging pattern in text streaming data which is an ordered sequence of texts is proposed based on text embedding and anomaly pattern detection. Using text embedding methods such as BOW(Bag Of Words), Word2Vec, and BERT, the detection performance of the proposed method is compared. Experimental results show that anomaly pattern detection using BERT embedding gave an average F1 value of 0.85 and the F1 value of 1 in three cases among five test cases.

Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space

  • Lee, Hansung;Moon, Daesung;Kim, Ikkyun;Jung, Hoseok;Park, Daihee
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.9 no.3
    • /
    • pp.1173-1192
    • /
    • 2015
  • The Support Vector Data Description (SVDD) has achieved great success in anomaly detection, directly finding the optimal ball with a minimal radius and center, which contains most of the target data. The SVDD has some limited classification capability, because the hyper-sphere, even in feature space, can express only a limited region of the target class. This paper presents an anomaly detection algorithm for mitigating the limitations of the conventional SVDD by finding the minimum volume enclosing ellipsoid in the feature space. To evaluate the performance of the proposed approach, we tested it with intrusion detection applications. Experimental results show the prominence of the proposed approach for anomaly detection compared with the standard SVDD.

Clinical analysis of cardiovascular surgery: a report of 1144 cases (심혈관질환 수술에 대한 임상적 고찰)

  • 유회성
    • Journal of Chest Surgery
    • /
    • v.17 no.3
    • /
    • pp.331-338
    • /
    • 1984
  • From 1959 to Jun. 30 84, 1144 cases of various cardiovascular diseases were operated consisting of 421 open heart surgery under extra-corporeal circulation or hypothermia and 723 conventional surgery at department of Thoracic and Cardiovascular Surgery in National Medical Center. There were 470 congenital anomaly and 674 acquired lesions. Out of 470 congenital anomaly, acyanotic anomaly was 289 and cyanotic anomaly was 181. Among 647 acquired lesions, 473 was cardiac lesion, 87 was pericardial lesion and 105 was vascular diseases. Over all operative mortality was 9.0%, consisting of 7.6% for acyanotic, 19.3% for cyanotic anomaly and 6.8% for acquired lesion. Mortality for 723 conventional surgery was 6.2%, and 421 open heart surgery was 13.8%.

  • PDF

A New Semantic Kernel Function for Online Anomaly Detection of Software

  • Parsa, Saeed;Naree, Somaye Arabi
    • ETRI Journal
    • /
    • v.34 no.2
    • /
    • pp.288-291
    • /
    • 2012
  • In this letter, a new online anomaly detection approach for software systems is proposed. The novelty of the proposed approach is to apply a new semantic kernel function for a support vector machine (SVM) classifier to detect fault-suspicious execution paths at runtime in a reasonable amount of time. The kernel uses a new sequence matching algorithm to measure similarities among program execution paths in a customized feature space whose dimensions represent the largest common subpaths among the execution paths. To increase the precision of the SVM classifier, each common subpath is given weights according to its ability to discern executions as correct or anomalous. Experiment results show that compared with the known kernels, the proposed SVM kernel will improve the time overhead of online anomaly detection by up to 170%, while improving the precision of anomaly alerts by up to 140%.

A Flow-based Detection Method for VoIP Anomaly Traffic (VoIP 이상 트래픽의 플로우 기반 탐지 방법)

  • Son, Hyeon-Gu;Lee, Young-Seok
    • Journal of KIISE:Information Networking
    • /
    • v.37 no.4
    • /
    • pp.263-271
    • /
    • 2010
  • SIP/RTP-based VoIP services are being popular. Recently, however, VoIP anomaly traffic such as delay, interference and termination of call establishment, and degradation of voice quality has been reported. An attacker could intercept a packet, and obtain user and header information so as to generate an anomaly traffic, because most Korean VoIP applications do not use standard security protocols. In this paper, we propose three VoIP anomaly traffic generation methods for CANCEL;BYE DoS and RTP flooding, and a detection method through flow-based traffic measurement. From our experiments, we showed that 97% of anomaly traffic could be detected in real commercial VoIP networks in Korea.

Relationship between Accrual Anomaly and Stock Return: The Case of Vietnam

  • DANG, Hung Ngoc;TRAN, Dung Manh
    • The Journal of Asian Finance, Economics and Business
    • /
    • v.6 no.4
    • /
    • pp.19-26
    • /
    • 2019
  • The study investigates the impact of accrual anomaly on stock return ratio of listed firms in Vietnam. Data were collected from listed firms for the period from 2008 to 2018. To learn about the causes of accrual anomaly in returns and future rate of returns on the Vietnamese stock market, this research is based on accrual analysis of Richardson, Sloan, Soliman, and Tuna (2006) on growth and effective components. We employ GLS regression model for examining the impact of accrual anomaly on stock return ratio and T-test for checking the difference between the lowest and the highest portfolio. The results show that accounting distortion is the main factor impacting the stock return, not growth determinant. Both two determinants of accounting distortion and growth contribute the explanation of the impact of accrual anomaly on profit and future stock return ratio. Experimental evidence confirms an abnormal existence of accrual in the Vietnam stock market. Aggregate accrual is negatively correlated with future operating profit and future stock return. However, after considering the factors contributing to the impact of future profitability and return on stock returns, the study results show that accounting distortion can account for low sustainability of income that is not growth.