• Asian Pacific Journal of Cancer Prevention
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• 제14권5호
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• pp.2835-2838
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• 2013

Background: Prostate cancer features a substantial incidence and mortality burden, similarly to breast cancer, and it ranks among the top ten specific causes of death in males. Objective: To explore the situation of prostate cancer in a healthy population cohort in Eastern Nepal. Materials and Methods: This study was conducted in the Department of General Surgery at B. P. Koirala Institute of Health Sciences, Dharan, Nepal from July 2010 to June 2011. Males above 50 years visiting the Surgical Outpatient Department in BPKIHS were enrolled in the study and screening camps were organized in four Teaching District Hospitals of BPKIHS, all in Eastern Nepal. Digital rectal examination (DRE) was conducted by trained professionals after collecting blood for assessment of serum prostatic specific antigen (PSA). Trucut biopsies were performed for all individuals with abnormal PSA/DRE findings. Results: A total of 1,521 males more than 50 years of age were assessed and screened after meeting the inclusion criteria. The vast majority of individuals, 1,452 (96.2%), had PSA ${\leq}4.0$ ng/ml. Abnormal PSA (>4 ng/ml) was found in 58 (3.8%). Abnormal DRE was found in 26 (1.72%). DRE and PSA were both abnormal in 26 (1.72%) individuals. On the basis of raised PSA or abnormal DRE 58 (3.84%) individuals were subjected to digitally guided trucut biopsy. Biopsy report revealed benign prostatic hyperplasia in 47 (3.11%) and adenocarcinoma prostate in 11 (0.73%). The specificity of DRE was 66.0%with a sensitivity of 90.9% and a positive predictive value of 38.5%. The sensitivity of PSA more than 4ng/ml in detecting carcinoma prostate was 100% and the positive predictive value for serum PSA was 19.0% Conclusions: The overall cancer detection rate in this study was 0.73% and those detected were locally advanced. Larger community-based studies are highly warranted specially among high-risk groups.

• Journal of Yeungnam Medical Science
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• 제2권1호
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• pp.77-80
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• 1985

심전도에 의한 R-R 간격변동은 자율신경계의 기능을 검사하는데 매우 유용하고 또한 교감 신경계와 부교감신경계의 가능을 정량적으로 알아낼 수 있을 것으로 사료되었다. 특히, 당뇨병질환에 있어서 자율신경계의 dysfunction현상을 고찰하는데 매우 유용할 것으로 기대된다(Fig.5 참조). 그러나 임상에 직접 적용시켜온 바로는 기립시, 심호흡시에 발생되는 근전도에 의한 잡음이 간혹 발생되는 경우가 있는데 이것은 전극접착법과 무선송신기에 의해 제거될 것으로 기대되며 향후의 과제로 남아있다.

• 대한임베디드공학회논문지
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• 제18권3호
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• pp.117-124
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• 2023

In the machine vision inspection equipment, diameter measurement is important process in inspection of cylindrical object. However, machine vision inspection equipment requires complex algorithm processing such as camera distortion correction and perspective distortion correction, and the increase in processing time and cost required for precise diameter measurement. In this paper, we proposed the algorithm for diameter measurement of cylindrical object using the laser displacement sensor. In order to fit circle for given four input outer points, grid search algorithms using root-mean-square error and mean-absolute error are applied and compared. To solve the limitations of the grid search algorithm, we finally apply the singular-value-decomposition based circle fitting algorithm. In order to compare the performance of the algorithms, we generated the pseudo data of the outer points of the cylindrical object and applied each algorithm. As a result of the experiment, the grid search using root-mean-square error confirmed stable measurement results, but it was confirmed that real-time processing was difficult as the execution time was 10.8059 second. The execution time of mean-absolute error algorithm was greatly improved as 0.3639 second, but there was no weight according to the distance, so the result of algorithm is abnormal. On the other hand, the singular-value-decomposition method was not affected by the grid and could not only obtain precise detection results, but also confirmed a very good execution time of 0.6 millisecond.

• Asian Pacific Journal of Cancer Prevention
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• 제14권5호
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• pp.2979-2982
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• 2013

Cervical cancer is a commonly-encountered malignant tumor in women. Cervical screening is particularly important due to early symptoms being deficient in specificity. The main purpose of the study is to assess the application value of cervical thinprep cytologic test (TCT) and human papillomavirus (HPV) detection in screening for cervical cancer and precancerous lesions. In the study, cervical TCT and HPV detection were simultaneously performed on 12,500 patients selected in a gynecological clinic. Three hundred patients with positive results demonstrated by cervical TCT and/or HPV detection underwent cervical tissue biopsy under colposcopy, and pathological results were considered as the gold standard. The results revealed that 200 out of 12,500 patients were abnormal by TCT, in which 30 cases pertained to equivocal atypical squamous cells (ASCUS), 80 cases to low squamous intraepithelial lesion (LSIL), 70 cases to high squamous intraepithelial lesion (HSIL) and 20 cases to squamous cell carcinoma (SCC). With increasing pathological grade of cervical biopsy, however, TCT positive rates did not rise. Two hundred and eighty out of 12,500 patients were detected as positive for HPV infection, in which 50 cases were chronic cervicitis and squamous metaplasia, 70 cases cervical intraepithelial neoplasia (CIN) I, 60 cases CIN II, 70 cases CIN III and 30 cases invasive cervical carcinoma. Two hundred and thirty patients with high-risk HPV infection were detected. With increase in pathological grade, the positive rate of high-risk HPV also rose. The detection rates of HPV detection to CIN III and invasive cervical carcinoma as well as the total detection rate of lesions were significantly higher than that of TCT. Hence, HPV detection is a better method for screening of cervical cancer at present.

• Smart Structures and Systems
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• 제29권6호
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• pp.757-766
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• 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.

• 한국정보통신학회논문지
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• 제26권12호
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• pp.1786-1793
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• 2022

최근 코로나 19, 정치적 상황 등 사회적 현안을 악용한 스미싱, 해킹메일 공격이 지속되고 있다. 공격의 대부분은 악성 URL 접근을 유도하여 개인정보를 탈취하는 방식을 취하고 있는데, 이를 대비하기 위해 현재 머신러닝, 딥러닝 기술 연구가 활발하게 진행되고 있다. 하지만 기존 연구에서는 데이터 세트의 특징들이 단순하기 때문에 악성으로 판별할 근거가 부족하다고 판단하였다. 본 논문에서는 URL 데이터 분석을 통해 기존 연구에 반영된 URL 어휘적인 특징 이외에도 "URL Days", "URL Words", "URL Abnormal" 3종, 9개 주요특징을 추가 제안하였고, 4개의 머신러닝 알고리즘 적용을 통해 F1-Score, 정확도 지표로 측정하였다. 기존 연구와 비교 분석 시 평균 0.9%가 향상된 결과 값과 F1-Score, 정확도에서 최고 98.5%가 측정됨에 따라 주요특징이 정확도 및 성능 향상에 기여하였다.

• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.387-399
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• 2009

이동 무선망은인증의 절도와 침입에 의해 계속 고통을 받고 있다. 그러한 두 문제 모두 2가지 다른 방법: 오용 탐지 또는 비정상 행위 기반 탐지로 해결될 수 있다. 이 논문에서, 우리는 이동 무선망의 이동 패턴과 같은 정상 행위를 효율적으로 식별할 수 있는 비유사도 기반 방법을 제안한다. 제안하는 알고리즘에서, 정상 프로파일은 이동 무선망에서 이동 사용자들의 정상 이동 패턴으로부터 구축되어진다. 구축된 정상 프로파일로부터, 가중 비유사도 측정으로 비유사도가 계산되어진다. 만일 가중 비유사도 측정치가 시스템 매개변수인 비유사도 임계치보다 크면, 경고 메시지가 발생된다. 제안된 방법의 성능은 모의실험을 통하여 평가되었다. 그 결과, 제안하는 방법의 성능이 비유사도 측정을 사용하는다른 비정상 행위 탐지 방법의 성능 보다 우수함을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• 대한임베디드공학회논문지
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• 제10권5호
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• pp.297-305
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• 2015

AFLS (Adaptive front lighting System) is being applied to improve safety in driving automotive at night. Safe embedded system for controlling head-lamp has to be tightly designed by considering safety requirement of hardware-dependent software, which is embedded in automotive ECU(Electronic Control Unit) hardware under severe environmental noise. In this paper, we propose an adaptive headlight controller with newly-designed symmetric angle sensor compensator, which is integrated with ECU-based adaptive front light system. The proposed system, on which additional backup hardware and emergency control algorithm are integrated, effectively detects abnormal situation and restore safe status of controlling the light-angle in AFLS operations by comparing result in symmetric angle sensor. The controlled angle value is traced into internal memory in runtime and will be continuously compared with the pre-defined lookup table (LUT) with symmetric angle value, which is used in normal operation. The watch-dog concept, which is based on using angle sensor and control-value tracer, enables quick response to restore safe light-controlling state by performing the backup sequence in emergency situation.

• 한국지능시스템학회논문지
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• 제26권3호
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• pp.246-252
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• 2016

화력발전소의 설비들은 매우 높은 온도와 압력의 환경에서 운전되므로, 설비고장은 상당한 인적 물적 손실로 이어진다. 그러므로 발전설비의 비정상정인 동작 상태를 사전에 확인할 수 있는 고장탐지 시스템이 필수적이다. 본 연구에서는, 화력발전소 증기보일러의 고장탐지를 위해서 마할라노비스 거리(Mahalanobis distance, MD)를 이용하였다. MD 기반의 고장탐지방법에서는, 비정상샘플은 정상샘플들로부터 멀리 떨어져 있다고 가정한다. 정상상태로 동작중인 대상시스템으로부터 수집된 다변량 샘플을 이용하여 평균벡터와 공분산행렬을 계산하고, MD값의 문턱값을 설정한다. 검증단계에서는, 평균벡터와 검증샘플들 간의 MD를 구한 후, 계산된 MD 값이 미리 설정된 문턱값보다 높으면 알람신호가 발생하게 된다. MD 기반의 고장탐지방법의 성능을 검증하기 위해서, 200MW 유연탄 화력발전소의 증기보일러 튜브누설로 인해서 발전정지 된 사례를 사용하였다. 실험결과는 MD 기반의 고장탐지기법이 발전정지가 발생하기 이전의 이상징후를 성공적으로 탐지할 수 있음을 보여준다.