• Korean Journal of Radiology
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• 제21권6호
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.792-797
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• 2002

In the following paper two algorithms, suitable for the analysis of panchromatic data as provided by KOMPSAT-1 will be presented. One is a texture analysis which will be used to create a settlement mask based on the variations of gray values. The other is a fusion algorithm which allows the combination of high resolution panchromatic data with medium resolution multispectral data. The procedure developed for this purpose uses the spatial information present in the high resolution image to spatially enhance the low resolution image, while keeping the distortion of the multispectral information to a minimum. This makes it possible to use the fusion results for standard multispecatral classification routines. The procedures presented here can be automated to large extent, making them suitable for a standard processing routine of satellite data.

• Mass Spectrometry Letters
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• 제4권2호
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• pp.25-29
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• 2013

Proteomics for biomarker validation needs high throughput instrumentation to analyze huge set of clinical samples for quantitative and reproducible analysis at a minimum time without manual experimental errors. Sample preparation, a vital step in proteomics plays a major role in identification and quantification of proteins from biological samples. Tryptic digestion a major check point in sample preparation for mass spectrometry based proteomics needs to be more accurate with rapid processing time. The present study focuses on establishing a high throughput automated online system for proteolytic digestion and desalting of proteins from biological samples quantitatively and qualitatively in a reproducible manner. The present study compares online protein digestion and desalting of BSA with conventional off-line (in-solution) method and validated for real time sample for reproducibility. Proteins were identified using SEQUEST data base search engine and the data were quantified using IDEALQ software. The present study shows that the online system capable of handling high throughput samples in 96 well formats carries out protein digestion and peptide desalting efficiently in a reproducible and quantitative manner. Label free quantification showed clear increase of peptide quantities with increase in concentration with much linearity compared to off line method. Hence we would like to suggest that inclusion of this online system in proteomic pipeline will be effective in quantification of proteins in comparative proteomics were the quantification is really very crucial.

• 센서학회지
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• 제9권5호
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• pp.373-379
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• 2000

본 논문에서는 차량이 자동으로 도로를 추적하는 자율주행을 실현하기 위한 선행조건으로서 자기차선의 자장으로부터 차량의 위치와 방향을 추정하기 위한 시스템을 제안한다. 자기차선에 사용되는 원통형 영구자석인 단일자기원에 자기 쌍극자 모델이 적용될 수 있음을 검증하기 위해서 자기센서를 이용하여 위치에 따른 원형 영구자석 자장의 3축 성분을 측정하고 실험 데이터를 자기 쌍극자 모델과 비교하였다. 실험 데이터를 기반으로 한 모델을 이용하여 자장의 3축 성분에 의하여 센서의 위치를 추정할 수 있음을 보인다. 단일 자기원에 검증된 자기 쌍극자 모델을 자기차선으로 확장하고, 센서의 위치와 방향에 따른 자장의 3축 성분의 실험 데이터를 획득한다. 실험 데이터의 맵핑을 이용하여 자장의 3축 성분에 따른 센서의 위치와 방향을 추정한다. 자기차선 상에서 차량의 위치와 센서를 제안된 방법에 의해 추정하고 컴퓨터 시뮬레이션을 통하여 차선추적에 적용한다.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 1999년도 추계학술대회
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• pp.381-389
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• 1999

One of the objectives of any task design is to provide a safe and helpful workplace for the employees. The safety and health module may include means for confronting the design with safety and health regulations and standards as well as tools for obstacles and collisions detection (such as error models and simulators). Virtual Reality is a leading edge technology which has only very recently become available on platforms and at prices accessible to the majority of simulation engineers. The design of an automated manufacturing system is a complicated, multidisciplinary task that requires involvement of several specialists. In this paper, a design procedure that facilitates the safety and ergonomic considerations of an automated manufacturing system are described. The procedure consists of the following major steps: Data collection and analysis of the data, creation of a three-dimensional simulation model of the work environment, simulation for safety analysis and risk assessment, development of safety solutions, selection of the preferred solutions, implementation of the selected solutions, reporting, and training When improving the safety of an existing system the three-dimensional simulation model helps the designer to perceive the work from operators point of view objectively and safely without the exposure to hazards of the actual system.

• 조명전기설비학회논문지
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• 제27권11호
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• pp.82-88
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• 2013

It is important to give lightning warning prior to a cloud-to-ground (CG) discharge within an Area of Concern (AOC) because most of lightning damage and victim are usually occurred by the first lightning in the AOC. The aim of this study is to find the optimal operation conditions of the automated lightning warning systems in order to make the best use of the available data. In this paper, the test-operated results of the automated lightning alert and risk management system (ALARM) based on detections of CG discharge and eletrostatic field and optimized at probability of lightning have been described. It was possible to obtain the following warning performance parameters: probability of detection (POD), false alarm ratio (FAR), probability of lightning (POL) and failure-to-warn rate (FTW). The data obtained from trial operation for 5months were not sufficient but the first analysis of domestic lightning warning was carried out. We have observed that the evaluated statistical results through trial operation depend on the various factors such as analysis methods and criteria, topographical conditions, etc. Also we suggest some methods for improvement of POL and POD including the finding of the optimal electric field threshold level to be used, based on the high values of FAR and FTW found in this work.

• 품질경영학회지
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• 제48권3호
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• pp.381-393
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• 2020

Purpose: The purpose of this study was to find useful solutions by analyzing causes and results about defects on mechanical drawing notes and provide an automated tool with solutions to mechanical engineers. Methods: The collected data for defects on mechanical drawing notes were from ongoing development and mass production projects. Various measurement methods were used based on the Lean 6 Sigma analysis such as Process analysis, C&E diagram and some statistical analysis. Results: The results of this study are as follows; The results of the Lean 6 Sigma analysis, the validity of the selected indicators for improving drawing notes quality was verified through the verification of cause variables. The strategy established to improve the mechanical drawing notes was reflected as an automated program, and the defects were within a manageable range and achieved target Sigma level. Conclusion: Through the application of the "Mechanical drawing notes automation tool", it is expected to resolve the "Voice of Customer, VOC" and "Voice of Business, VOB".

• 한국석유지질학회:학술대회논문집
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• 한국석유지질학회 1998년도 제5차 학술발표회 발표논문집
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• pp.10-14
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• 1998

A systematic methodology is developed for the electrofacies determination from wireline log data using multivariate statistical analysis. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the efficiency and quality of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification matches well to the core and the cutting data with high reliability This methodology for electrofacies classification can be used to define the reservoir characteristics which are helpful to the reservoir management.

• 천문학회지
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• 제29권spc1호
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• pp.125-126
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• 1996

Beijing-Arizona-Taipei-Connecticut (BATC) survey is a long term project to map the spectral energy distribution of various objects using 15 intermediate band filters and aims to cover about 450 sq degrees of northern sky. The SED information, combined with image structure information, is used to classify objects into several stellar and galaxy categories as well as QSO candidates. In this paper, we present a preliminary setup of robust data reduction procedure recently developed at NCU and also briefly discuss general classification scheme: redshift estimate, and automatic detection of variable objects.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.967-976
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• 1999

In this paper we study the Hellinger distance based goodness-of-fit tests that are analogs of likelihood ratio tests. The minimum Hellinger distance estimator (MHDE) in normal models provides an excellent robust alternative to the usual maximum likelihood estimator. Our simulation results show that the Hellinger deviance test (Simpson 1989) based goodness-of-fit test is robust when data contain outliers. The proposed hellinger deviance test(Simpson 1989) is a more direcct method for obtaining robust inferences than an automated outlier screen method used before the likelihood ratio test data analysis.