• 정보처리학회논문지D
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• 제11D권5호
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• pp.1087-1094
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• 2004

분석 및 설계단계의 산출물을 작성할 때는 대부분 정형화와 표준화를 따른다. 그러나 실제 시스템 개발현장에서는 단계별 산출물들의 개별적인 특성으로 인하여 모든 산출물간에는 연속적이며 자동화된 과정을 통해 산출물들이 작성될 수는 없다. 그 결과 작성된 개발 산출물간의 일관성이 유지되지 못하는 관계로 최종 산출물에서 여러 가지 문제가 야기됨을 알 수 있다. 그러므로 본 논문에서는 객체지향 방법론에 따라 개발되는 시스템에 대해 분석 및 설계단계의 산출물간 일관성을 유지하기 위한 방안을 제시하고, 이를 지원하기 위한 도구를 개발한다.

• 전기학회논문지
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• 제63권9호
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• pp.1171-1179
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• 2014

ISO(Independent System Operator) or TSO(Transmission System Operator) use power system analysis program to simulate contingency analysis and remedial actions to operate power system stably. Generally, power system analysis program provides automated analysis functions(or modules) to deal with wide area power system. However, because of missed contingency cases, automated contingency analysis has no practical use or has limitation. And in case of remedial action, it doesn't support automated function or takes a lot of times to study, because of simulation in manual for each cases. This paper suggests that new relation with buses and transmission line properties of power system DB used for power system analysis program to simulate automated contingency including all contingency cases needed in the field without missed cases. And it proposes automated remedial action scheme based on practical approach analysis to alleviate overloading or voltage problems. Finally it deals with automated contingency/remedial action analysis(automated scheduled outage) program which is developed by KEPCO and its performance and proposed schemes are proven by case study for real Korean power system data.

• 디지털콘텐츠학회 논문지
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• 제16권6호
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• pp.893-900
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• 2015

국제해사기구에서는 e-Navigation의 개발과정에 소프트웨어 품질에 대한 고려가 필수적으로 도입될 수 있도록 해양소프트웨어품질보증을 진행해오고 있다. 소프트웨어 품질 확보를 위해서는 소프트웨어 개발 프로젝트 전반에 걸쳐 정해진 절차를 따르고, 각 절차를 수행한 결과로 산출물의 작성이 필요하다. 이를 지원하는 도구의 활용은 소프트웨어 개발의 생산성에 중요한 요소가 될 수 있다. 본 논문에서는 실시간 모니터링과 문서의 자동화가 가능한 도구를 활용하여 해양 소프트웨어 개발에 적용하기 위한 사례를 소개한다. 도구 활용에 대한 기대효과 및 해양 SQA에 적용하기 위한 절차의 개선에 대해서 논의한다. 개선된 절차를 도구에 반영하여 해양 SQA에 맞춤형 도구를 개발하는 것을 향후 목표로 한다.

• 정보보호학회논문지
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• 제25권6호
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• pp.1377-1383
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• 2015

클라우드 서비스 이용자의 증가로 클라우드 스토리지상에 개인이 생성한 중요한 파일이 다수 존재한다. 즉, 클라우드 사용 흔적은 주요 증거가 될 수 있기에 조사할 필요성이 있다. 클라우드 서비스를 조사하는 방법에는 스토리지 서버 공급자(CSP:Cloud Service Provider)를 이용하여 조사하는 방법과 클라이언트를 조사하는 방법이 있다. 이 중 본 논문에서는 클라이언트 컴퓨터를 조사할 수 있는 도구(Cloud Artifact)를 개발하였다. Cloud Artifact는 Google Drive, Dropbox, Evernote, N드라이브, Daum 클라우드, Ucloud, LG Cloud, T 클라우드, iCloud 9가지 클라우드 서비스 아티팩트를 수집 및 분석한다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제5권4호
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• pp.498-506
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• 1999

본 논문에서는 ATM 통신프로토콜을 위한 자동시험시스템인 ACTS(ATM Conformance Test System)의 개발과 적용을 소개한다. ACTS는 ITU-T 또는 ATM Forum의 ATM 사용자-망 접속표준을 준용하는 ATM 단말과 망장비의 프로토콜적합성을 확인하는데 사용된다. 본 논문에서는 ACTS 개발에 사용된 방법을 소개한 뒤, ACTS를 실제 ATM 단말과 장비에 적용한 사례들을 소개한다. ACTS의 적용을 통하여 시험대상 장비들이 지닌 프로토콜구현상의 문제점을 파악하였고, 문제에 대한 원인분석을 수행하였다. 또한 이러한 문제점들이 상호운용에 미치는 장애를 예측함으로써, ACTS를 상호운용하는 ATM 장비를 확보하기 위한 유용한 도구로 사용할 수 있음을 보인다.Abstract This paper presents development and application of ACTS(ATM Conformance Test System), an automated test system for ATM protocols. ACTS is a test system that checks conformance of ATM terminal and network equipment implementing either ITU-T or ATM Forum user-network interface. This paper, after presenting the methodology and process used for developing ACTS, conducts case studies of its applications to real ATM equipment. By applying ACTS, we were able to detect numerous problems in protocol implementations of ATM equipment and analyse causes of the problems, thereby demonstrating the efficacy of ACTS as an efficient automated testing tool. Furthermore, by predicting the potential effects of the problems on interoperability, we show how ACTS can be used as a useful tool for ensuring interoperable ATM equipment.

• 대한치과교정학회지
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• 제52권1호
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• pp.3-19
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• 2022

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

• Archives of Plastic Surgery
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• 제51권1호
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• pp.30-35
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• 2024

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

• Structural Engineering and Mechanics
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• 제46권3호
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• pp.433-445
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• 2013

Vibration-based structural identification has become an important tool for structural health monitoring and safety evaluation. However, various kinds of uncertainties (e.g., observation noise) involved in the field test data obstruct automation system identification for accurate and fast structural safety evaluation. A practical way including a data preprocessing procedure and a vector backward auto-regressive (VBAR) method has been investigated for practical bridge identification. The data preprocessing procedure serves to improve the data quality, which consists of multi-level uncertainty mitigation techniques. The VBAR method provides a determinative way to automatically distinguish structural modes from extraneous modes arising from uncertainty. Ambient test data of a cantilever beam is investigated to demonstrate how the proposed method automatically interprets vibration data for structural modal estimation. Especially, structural identification of a truss bridge using field test data is also performed to study the effectiveness of the proposed method for real bridge identification.

• Korean Journal of Radiology
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• 제25권2호
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• pp.146-156
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• 2024

Objective: Automated breast ultrasound (ABUS) is a relevant imaging technique for early breast cancer diagnosis and is increasingly being used as a supplementary tool for mammography. This study compared the performance of ABUS and handheld ultrasound (HHUS) in detecting and characterizing the axillary lymph nodes (LNs) in patients with breast cancer. Materials and Methods: We retrospectively reviewed the medical records of women with recently diagnosed early breast cancer (≤ T2) who underwent both ABUS and HHUS examinations for axilla (September 2017-May 2018). ABUS and HHUS findings were compared using pathological outcomes as reference standards. Diagnostic performance in predicting any axillary LN metastasis and heavy nodal-burden metastases (i.e., ≥ 3 LNs) was evaluated. The ABUS-HHUS agreement for visibility and US findings was calculated. Results: The study included 377 women (53.1 ± 11.1 years). Among 385 breast cancers in 377 patients, 101 had axillary LN metastases and 30 had heavy nodal burden metastases. ABUS identified benign-looking or suspicious axillary LNs (average, 1.4 ± 0.8) in 246 axillae (63.9%, 246/385). According to the per-breast analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of ABUS in predicting axillary LN metastases were 43.6% (44/101), 95.1% (270/284), 75.9% (44/58), 82.6% (270/327), and 81.6% (314/385), respectively. The corresponding results for HHUS were 41.6% (42/101), 95.1% (270/284), 75.0% (42/56), 82.1% (270/329), and 81.0% (312/385), respectively, which were not significantly different from those of ABUS (P ≥ 0.53). The performance results for heavy nodal-burden metastases were 70.0% (21/30), 89.6% (318/355), 36.2% (21/58), 97.3% (318/327), and 88.1% (339/385), respectively, for ABUS and 66.7% (20/30), 89.9% (319/355), 35.7% (20/56), 97.0% (319/329), and 88.1% (339/385), respectively, for HHUS, also not showing significant difference (P ≥ 0.57). The ABUS-HHUS agreement was 95.9% (236/246; Cohen's kappa = 0.883). Conclusion: Although ABUS showed limited sensitivity in diagnosing axillary LN metastasis in early breast cancer, it was still useful as the performance was comparable to that of HHUS.

• 디지털콘텐츠학회 논문지
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• 제18권6호
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• pp.1175-1182
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• 2017

제4차 산업혁명은 IoT(Internet of Things) 빅데이터(BigData) 인공지능 등 다양한 기술들의 융합을 통하여 스마트 공장(Smart factory) 구현을 목표로 새로운 산업화를 시도하고 있다. 스마트 공장 실현을 위해서 다양한 이종기계들 간의 유연한 데이터 교환 방법이 가능한 통신 기술이 필요하고 표준 기술을 기반 생산 장비의 확장성이 고려될 수 있어야 한다. 본 연구에서는 이종기기를 포함하는 다수의 생산 설비로부터 데이터를 수집 및 통합하고, 모든 생산 장비를 감시할 수 있는 MTConnect기반 이종 공작기계 상태 정보 및 가공 정보 관리시스템을 제안한다. 개발된 시스템 기술은 유연 자동화 생산 라인의 핵심 기술로서 오류 검출, 가공 상태 관리 등 무인 자동화 라인의 중요한 정보를 제공 및 관리하는 기술을 제공할 수 있다.