• 재활복지공학회논문지
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• 제10권4호
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• pp.259-265
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• 2016

본 논문에서는 폐질환 조기 검출을 위하여 Broncho vascular, Emphysema, Ground Glass Reticular, Ground Glass, Honeycomb, Normal의 6가지 폐조직에 대한 새로운 분류기법을 제안하였다. 단순 베이즈 분류기와 아다부스트 학습 기법을 도입하여 459개의 결합 히스토그램 특징인자로부터 유효한 특징인자를 선별함으로써 폐조직을 분류하였다. 다중 해상도 해석, 체적 LBP 및 CT 휘도를 기반으로 하는 결합 히스토그램 특징인자는 정확도, 민감도, 특이도 결과에서 기존의 3D AMFM보다 우수한 결과를 보였다. 제안한 특징인자와 3D AMFM 특징인자의 정확도는 각각 90.1%과 85.3%로서 제안한 특징인자의 우수한 분류 성능을 확인하였다.

• 한국전기전자재료학회논문지
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• 제11권11호
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• pp.1035-1042
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• 1998

After the 1990's, a computer-aided partial discharge(PD) measurement system was referred in part of aging diagnosis using digital signal processing as the new technology has been studied. The PD patterns and relevant information for pattern recognition are discussed in PD research area, because discharge quantity(q), the number of discharge pulse(n) and the applied boltage phase($\varphi$) was combined with the system information of the aging state. This paper investigates the discharge phase and quantity, as well as the number of discharge(n) with regard to discharge signals generated in power cable. therefore, according to characteristic analysis on the distribution of $\varphi$, q and n, it is able to apply in the aging analysis of power cable which visual observation is impossible and distribution change of discharge signals offers much information for risk degree on aging progress of insulation materials.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2015년도 춘계 종합학술대회 논문집
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• pp.191-192
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• 2015

전립선비대증(Benign Prostatic Hyperplasia, BPH)은 전립선조직중에 이행구역의 결절성증식과 요도 주위의 과증식(Hyperplasia)이 특징이다. 경직장초음파(TRUS: transrectal ultrasonography)검사를 이용한 진단에 있어 정상조직과 비대되어 있는 조직의 영상 차이를 비교하고 수량화로 나타내었다, 영상분석에는 GLCM 통계적 파라미터 중에서 Autocorrelation, Cluster Prominence, Entropy, Sum average를 4개의 파라미터에서 병변 인식이 가능하였고 인식 효율은 92-98%가 나왔다. 전립선비대증식에 대한 초음파영상을 가지고 컴퓨터영상처리분석을 제안하여 진단시 참고 자료가 될 것으로 기대한다.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2003년도 The 8th Asian Symposium on Precision Forging ASPF
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• pp.115-122
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• 2003

This paper presents the development of a virtual forging factory framework. The technologies of virtual reality and relational database had been integrated in the developed framework using Microsoft $Windows^{(R)}$ programming as the main technique so as to emulate a physical forging factory. The developed virtual forging factory consists of forging cells and a forging cell is comprised of forging machine, forging die, and forging operations forming a forging production line. The technology of virtual reality had been successfully adopted in the production simulation of manufacturing such as CNC and robotics. However, the application in virtual forging factory seems to have not been studied yet. Potential application of a virtual forging factory can be beneficial to (1) computer aided instruction, (2) shorten the learning curve of a novice, (3) remote diagnosis and monitoring when remote monitoring and control technology and signal inspection is considered, (4) improve adverse forging environment when remote forging technology is applied, and (5) virtual reality application.

• 한국정보통신학회논문지
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• 제5권4호
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• pp.829-840
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• 2001

본 연구는 디지털 화상처리기술의 대표적인 응용분야로서 주목받고 있는 X선 사진을 대상으로 한 계산기 지원진단에 관한 연구의 일종으로서, 폐의 중요한 질환중 하나인 폐기종의 진단을 지원하는 계산기 시스템에 관한 연구이다. 구체적인 내용으로서는 흉부X선 사진으로부터 말초혈관을 자동추출하고, 추출된 혈관을 토대로 여러가지의 특징량을 구하여, 최종적으로 폐기종의 병세진행도를 정량평가하는 시스템에 관한 연구이다. 혈관 도형을 추출하여 병의 진행 정도를 정량적으로 평가하기 위해 본 연구에서 제안한 평가방법을 10장의 X선 사진에 설정된 189개의 관심영역에 적용하여, 의사의 평가치와 본 연구의 제안방법에 의한 평가치를 비교·검토함으로써 그 유효성을 검증하였다.

• 대한의용생체공학회:의공학회지
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• 제27권5호
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• pp.291-299
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• 2006

A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.672-675
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• 2010

초음파 영상은 다른 의료 진단 방법에 비해 상대적으로 비용이 적게 들고 데이터 획득이 용이하기 때문에 널리 이용되고 있다. 초음파 영상은 획득 방법에 따라 화질이 차이가 난다. 고식적 초음파 영상에 비해 두 배의 주파수를 사용하는 하모닉 영상은 대조도나 해상도가 향상되고, 영상 내 잡음이 감소한다. 그래서 초음파 영상을 이용한 진단 과정에서 병변의 특징을 육안으로 정확하게 관찰할 수 있고, 이를 통해서 진단 결과의 정확성이 향상된다. 본 논문에서는 초음파 영상의 획득 방법의 차이에 따른 진단 성능의 차이를 컴퓨터를 이용한 병변 분류 성능을 통해서 비교했다. 이를 위해서 초음파를 통해서 획득한 영상에서 병변의 형태 및 질감 특징을 추출하고, 이를 바탕으로 병변을 분류하는 시스템 구성하였다. 실험을 통해서 하모닉 초음파 영상을 이용한 컴퓨터 기반 분류 방법이 고식적 초음파를 이용한 방법에 비해서 6% 정확성 향상이 있는 것을 확인하였다.

• Journal of applied mathematics & informatics
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• 제41권6호
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• Clinical Endoscopy
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• 제56권5호
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• pp.553-562
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• 2023

Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrow-band wavelength light, such as narrow-band imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To gain a better understanding of the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.940-952
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• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.