• Asian Pacific Journal of Cancer Prevention
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• 제15권3호
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• pp.1327-1331
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• 2014

Background: Electrical impedance tomography (EIT) is a new non-invasive, mobile screening method which does not use ionizing radiation to the human breast; allows conducting quantitative assessment of the images besides the visual interpretation. The aim of this study was to correlate the quantitative assessment and visual interpretation of breast electrical impedance tomographs and associated factors. Materials and Methods: One hundred and fifty mammography patients above 40 years and undergoing EIT were chosen using convenient sampling. Visual interpretation of the images was carried out by a radiologist with minimum of three years experience using the breast imaging - electrical impedance (BI-EIM) classification for detection of abnormalities. A set of thirty blinded EIT images were reinterpreted to determine the intra-rater reliability using kappa. Quantitative assessment was by comparison of the breast average electric conductivity with the norm and correlations with visual interpretation of the images were determined using Chi-square. One-way ANOVA was used to compare the mean electrical conductivity between groups and t-test was used for comparisons with pre-existing Caucasians statistics. Independent t-tests were applied to compare the mean electrical conductivity of women with factors like exogenous hormone use and family history of breast cancer. Results: The mean electrical conductivity of Malaysian women was significantly lower than that of Caucasians (p<0.05). Quantitative assessment of electrical impedance tomography was significantly related with visual interpretation of images of the breast (p<0.05). Conclusions: Quantitative assessment of electrical impedance tomography images was significantly related with visual interpretation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.503-508
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• 1992

A new method for making automatic electroencephalogram(EEG) report based on the automatic quantitative interpretation of awake EEG was developed. We first analysed a. relationship between EEG reports and quantitative EEG interpretation done by a qualified electroencephalographer(EEGer) for 22 subjects. Based on the analysed relationship and usual process of report making by the EEGer, we defined all terminology necessary for EEG report and established rules for EEG report making. By the combined use of the proposed EEG report making and the method for automatic quantitative EEG interpretation presented at '90 KACC, we were able to make the automatic EEG reports which were equivalent to the EEG reports written by the EEGer. As all the procedures were programmed in a personal computer equipped with an AD (analogue-to-digital) converter, the automatic EEG reports were obtained in almost real time in usual actual EEG recording situation with only a few seconds time lag for the analysis in the computer. The proposed report making method and the quantitative EEG interpretation method will be effectively applicable to the clinical use as an assistant tool for physicians.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1190-1195
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• 1990

A new system for automatic interpretation of the awake electroencephalogram(EEG) was developed in this work. We first clarified all the necessary items for EEG interpretation in accordance with an analysis of visual inspection of the rhythms by a qualified electroencephalographer (EEGer), and then defined each item quantitatively. Concerning the automatic interpretation, we made an effort to find out specific EEG parameters which faithfully represent the procedure of visual interpretation by the qualified EEGer. Those specific EEG parameters were calculated from periodograms of the EEG time series. By using EEG data of 14 subjects, the automatic EEG interpretation system was constructed and compared with the visual interpretation done by the EEGer. The automatic EEG interpretation thus established was proved to be in agreement with the visual interpretation by the EEGer.

• 한국수학교육학회지시리즈C:초등수학교육
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• 제17권2호
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• pp.113-125
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• 2014

본 연구의 목적은 초등학교 6학년 학생들의 그래프 해석이 어떠한지를 살펴보고, 이를 토대로 그래프 해석 능력을 향상시킬 수 있는 지도 방안을 모색하는 데 있다. 연구문제 해결을 위하여 그래프 해석 검사 결과를 분석하고 그래프 해석 지도 방안을 마련하여 적용하였다. 이를 통해 학생들은 그래프를 해석할 때 양적인면과 질적인 면을 종합적으로 볼 수 있게 되었으며, 그래프와 실생활을 관련지어 생각하면서 그래프의 실용성을 체득하게 되었다. 이를 토대로 학생들의 그래프 해석능력을 향상 시킬 수 있도록 지도 방안을 개선하는데 도움이 될 수 있을 것이다.

• 지구물리와물리탐사
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• 제8권1호
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• pp.112-117
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• 2005

물리검층은 석탄광 탐사에 일상적으로 적용된다. 현재 검층의 주요 적용 목적은 탄층의 심도 탐지와 탄질, 층서 및 암반강도의 정성적 추정이다. 그러나 만약 정량적 해석이 이루어진다면 더 많은 정보가 얻어질 수 있는데, 정량적 해석의 이해를 돕기 위해, 검층 반응을 Sydney Basin과 Bowen Basin의 호주산 검은 석탄(black coal) 광산지역에서 흔히 발견되는 쇄설퇴적암의 광물조성에 따라 논의하였으며, 검층 반응은 충분한 신뢰도로 광물조성에 대비될 수 있음을 알았다. 해석의 모호성은 만약 모든 종류의 검층자료가 있다면 더 잘 해결될 수 있을 것이다. 음파검층에서 측정된 속도와 계산값을 비교함으로써 일관성을 검토하는 방법 또한 서술하였다. 정량해석의 중요한 목적은 지질공학적 특성화이며, 이 논문에서는 물리검층으로부터 유추될 수 있는 물리적 성질을 고려한 쇄설암의 분류 시스템을 제안하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.19-23
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• 1996

A full automatic interpretation of awake electroencephalogram (EEG) had been developed by the authors and presented at the past KACCs in series. The automatic EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data and adaptable EEG interpretation. The automatic EEG interpretation reveals essentially the same findings as the electroencephalographer's (EEG's), and then would be applicable in clinical use as an assistant tool for EEGer. The method had been developed through collaboration works between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as an artificial realization of human expert skill. The procedure for the artificial realization was summarized in a methodology for artificial realization of human skill which will be applicable in other fields of systems control.

• 대한영상의학회지
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• 제82권4호
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• pp.808-816
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• 2021

폐기종은 만성 폐쇄성 폐질환을 유발하는 질환으로, CT는 폐기종을 정확하게 진단하는 데 가장 유용한 검사이다. 폐기종의 중증도는 시각적 분류 혹은 정량적 분석 등의 방법으로 평가할 수 있으며, 최근에는 딥러닝을 활용한 폐기종 연구도 다양하게 이루어지고 있다. 이러한 폐기종의 중증도 분류 방법은 다양한 연구에서 그 임상적 유용성을 입증받고 있으며, 한계점으로 지적되고 있는 측정의 신뢰성을 향상시키려는 노력 또한 이어지고 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.505-508
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• 1995

The automatic interpretation of awake background electroencephalogram (EEG), consisting of quantitative EEG interpretation and EEG report making, has been developed by the authors based on EEG data visually inspected by an electroencephalographer (EEGer). The present study was focused on the adaptability of the automatic EEG interpretation which was accomplished by the constructive neural network with forgetting factor. The artificial neural network (ANN) was constructed so as to give the integrative decision of the EEG by using the input signals of the intermediate judgment of 13 items of the EEG. The feature of the ANN was that it adapted to any EEGer who gave visual inspection for the training data. The developed method was evaluated based on the EEG data of 57 patients. The re-trained ANN adapted to another EEGer appropriately.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.786-791
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• 2002

Mapping systems using Satellite Imagery has not been well-established compare to conventional Arial Photograph mapping systems. In order for satellite imagery to produce a stable quality of maps, it requires to follow the standard mapping procedures. In this satellite imagery study, we proposed four methods of mapping procedures. Mapping methods were established by generating trial maps and analyzing types of input data and functions of DPW (Digital Photogrammetric Workstation). On quantitative aspect, accuracy of each steps were measured by increasing 2 GCPs each time from the minimum of 6 GCPs. In DLT, with the minimum of 10 points, RMSE is 2 pixels at most. Besides that, interpretation and stereoscopic plotting using KOMPSAT-1 imagery and other simulated imagery was performed. The tests resulted that, for KOMPSAT-1 (6.6m) stereoscopic images, the possibility of interpretation is 44.79% and possibility of stereoscopic plotting is 43.75%. In the other hand, for simulated imagery (1m), the possibility of interpretation is 60.92% and possibility of stereoscopic plotting is 55.18%.

• Korean Journal of Radiology
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• 제22권8호
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• pp.1225-1239
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• 2021

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.