진행성위암의 (進行性胃癌) 내시경적 (內視鏡的) 관찰

방영근,김두경,송환규,권영걸,황영실,박노춘,양웅석 ( Young Keun Bang,Doo Kyung Kim,Hwan Kyu Song,Young Girl Kweon,Young Sil Hwang,Noh Choon Park ) 대한소화기학회 1981 대한소화기학회지 Vol.13 No.1

A clinical analysis was done among 211 cases of advanced stomach cancer which had been confirmed with gastrofiberscopy for 3 years from May, 1977 to September, 1980 at the Depart- ment of Internal Medicine, Yvallace Memorial Baptist Hospital, Busan, Korea The following results were obtained; 1. Among the 211 cases of advanced stomach cancer, 145 cases(68. 72%) were male and 66 cases(31.27%) were female. The ratio of male to female was 2. 2: l. 2. The peak age incidence was in the 5 th decade with 73 cases(34. 59%) and next came the 4 th decade with 54 cases(25, 59%), the 6 th decade with 40 cases(18. 95%) and the 3 rd decade with 27 cases(12. 79%). 3. Among the 173 cases of advanced stomach cancer, 62 cases(35. 83%) were confined to the body of stomach which was the most prevalent site and next came 55 cases(31. 79%) in antrum, 30 cases(17. 34po) in both antrum and body, and 7 cases(4. 04%) in angulus. On the other hand, among the 137 cases, 71 cases(51. 82%) were confined to lesser curvature which was the most prevalent site, and next came 28 cases(20.43%) in greater curvature, 25 cases (18. 24%) in posterior wall, and 13 cases(9. 48%) in anterior wall viewing the stomach laterally. 4. As for the Borrmanns classification in 187 cases, the most frequent was type g with 83 cases and next came type IV with 40 cases, type ]I with 29 cases and type [ with 25 cases in the order. 5 As for the UGI roentgenographic study in 139 cases, stomach cancer was found in 115 cases(82. 73%), benign gastric ulcer in 9 cases(6 47%), gastritis in 6 cases(4. 31%) and norm- al findings in 6 cases(4. 31%). 6. As for the gastrofiberscopic biopsy done in 144 cases, 104 cases(72. 2%) were positive for cancer cell and 40 cases were negative for cancer cell(27. 77%).

가스 식별 시스템 설계를 위한 유전알고리즘과 퍼지시스템 적용에 관한 연구

방영근,조해파,이철희,Bang, Young-Keun,Haibo, Zhao,Lee, Chul-Heui 강원대학교 산업기술연구소 2011 産業技術硏究 Vol.31 No.2

Recently, machine olfactory systems that have been proposed as an artificial substitute of the human olfactory system are being studied by many researchers because they can scent dangerous gases and identify the type of gases in contamination areas instead of the human. In this paper, we present an effective design method for the gas identification system. The design method adopted the sequential combination between genetic algorithms and TSK fuzzy logic system. First, the proposed method allowed the designed gas identification system effectively performing the pattern analysis because it was able to avoid the curse of dimensionality caused by use of a large number of sensors. Secondly, the method led the gas identification system to good performance because it was able to deal with drift characteristics of the sensor data by using description ability of the fuzzy system for nonlinear data. In simulation, we demonstrated the effectiveness of the designed gas identification system by using the simulation results of five types of gases.

HCBKA 기반 오차 보정형 TSK 퍼지 예측시스템 설계

방영근(Young-Keun Bang),이철희(Chul-Heui Lee) 대한전기학회 2010 전기학회논문지 Vol.59 No.6

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

HCBKA 기반 IT2TSK 퍼지 예측시스템 설계

방영근(Young-Keun Bang),이철희(Chul-Heui Lee) 대한전기학회 2011 전기학회논문지 Vol.60 No.7

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

강화된 유전알고리즘을 이용한 이중 동조 기반 퍼지 예측시스템 설계 및 응용

방영근(Young-Keun Bang),이철희(Chul-Heui Lee) 대한전기학회 2010 전기학회논문지 Vol.59 No.1

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

러프 집합 기반 적응 모델 선택을 갖는 다중 모델 퍼지 예측 시스템 구현과 시계열 예측 응용

방영근(Young-Keun Bang),이철희(Chul-Heui Lee) 한국지능시스템학회 2009 한국지능시스템학회논문지 Vol.19 No.1

최근 시계열 예측에 결론부에 선형식을 갖는 TS 퍼지 모델이 많이 이용되고 있는데, 이의 예측 성능은 정상성과 같은 데이터의 특성과 밀접한 관련이 있다. 그러므로 본 논문에서는 특히 비정상 시계열 예측에 매우 효과적인 새로운 예측 기법 을 제안하였다. 시계열의 패턴이나 규칙성을 잘 끌어내기 위한 데이터 전처리 과정을 도입하고 다중 모델 TS 퍼지 예측기를 구성한 뒤, 러프집합을 이용한 적응 모델 선택 기법에 의해 입력 데이터의 특성에 따라 가변적으로 적합한 예측 모델을 선택하여 시계열 예측이 수행되도록 하였다. 마지막으로 예측 오차를 감소시키기 위하여 오차 보정 메커니즘을 추가함으로써 예측 성능을 더욱 향상시켰다. 시뮬레이션을 통해 제안된 기법의 성능을 검증하였다, 제안된 기법은 예측 모델 구현과 예측 수행 과정에서 시계열 데이터의 특성들을 잘 반영할 수 있으므로 불확실성과 비정상성을 갖는 시계열의 예측에 매우 효과적으로 이용될 수 있을 것이다. Recently, the TS fuzzy models that include the linear equations in the consequent part are widely used for time series forecasting, and the prediction performance of them is somewhat dependent on the characteristics of time series such as stationariness. Thus, a new prediction method is suggested in this paper which is especially effective to nonstationary time series prediction. First, data preprocessing is introduced to extract the patterns and regularities of time series well, and then multiple model TS fuzzy predictors are constructed. Next, an appropriate model is chosen for each input data by an adaptive model selection mechanism based on rough sets, and the prediction is going. Finally, the error compensation procedure is added to improve the performance by decreasing the prediction error. Computer simulations are performed on typical cases to verify the effectiveness of the proposed method. It may be very useful for the prediction of time series with uncertainty and/or nonstationariness because it handles and reflects better the characteristics of data.

계층적 분류구조의 퍼지시스템 설계 및 시계열 예측 응용

방영근(Young-Keun Bang),이철희(Chul-Heui Lee) 한국지능시스템학회 2009 한국지능시스템학회논문지 Vol.19 No.5

시스템의 동작특성을 표현하는 퍼지 규칙들은 퍼지 클러스터링 기법에 매우 의존적이다. 만약, 클러스터링 기법의 분류 능력이 개선된다면, 그들에 의해 생성되는 퍼지 규칙과 식별되는 파라미터들이 보다 정밀해 질 수 있으므로 시스템의 성능이 개선될 수 있다. 따라서 본 논문에서는 분류능력이 강화된 새로운 계층 구조 클러스터링 알고리즘을 제안한다. 제안된 클러스터링 기법은 데이터 사이의 통계적 특성과 상관성을 고려하여 보다 정확하게 데이터들을 분류할 수 있도록 2개의 클러스터의 구조를 갖는다. 또한, 본 논문은 차분 데이터를 이용하여 원형 데이터의 패턴이나 규칙들이 명확하게 반영될 수 있도록 하며, 각각의 차분 데이터들의 다양한 특성을 고려할 수 있도록 다중 퍼지 시스템을 구현한다. 마지막으로, 제안된 기법들의 유효성을 다양한 비선형 시계열 데이터들의 예측을 통해 검증한다. Fuzzy rules, which represent the behavior of their system, are sensitive to fuzzy clustering techniques. If the classification abilities of such clustering techniques are improved, their systems can work for the purpose more accurately because the capabilities of the fuzzy rules and parameters are enhanced by the clustering techniques. Thus, this paper proposes a new hierarchically structured clustering algorithm that can enhance the classification abilities. The proposed clustering technique consists of two clusters based on correlationship and statistical characteristics between data, which can perform classification more accurately. In addition, this paper uses difference data sets to reflect the patterns and regularities of the original data clearly, and constructs multiple fuzzy systems to consider various characteristics of the differences suitably. To verify effectiveness of the proposed techniques, this paper applies the constructed fuzzy systems to the field of time series prediction, and performs prediction for nonlinear time series examples.

퍼지 예측 시스템을 이용한 전력 부하 예측

방영근(Young-Keun Bang),심재선(Jae-Sun Shim) 대한전기학회 2013 전기학회논문지 Vol.62 No.11

Electric power is an important part in economic development. Moreover, an accurate load forecast can make a financing planning, power supply strategy and market research planned effectively. This paper used the fuzzy logic system to predict the regional electric power load. To design the fuzzy prediction system, the correlation-based clustering algorithm and TSK fuzzy model were used. Also, to improve the prediction system’s capability, the moving average technique and relative increasing rate were used in the preprocessing procedure. Finally, using four regional electric power load in Taiwan, this paper verified the performance of the proposed system and demonstrated its effectiveness and usefulness.

DBKM을 이용한 퍼지 시계열 예측

방영근(Young-Keun Bang),심재선(Jae-Sun Shim),이철희(Chul-Heui Lee) 대한전기학회 2013 대한전기학회 학술대회 논문집 Vol.2013 No.7

데이터 전처리와 퍼지 논리 시스템을 이용한 전력 부하 예측

방영근(Young-Keun Bang),이철희(Chul-Heui Lee) 대한전기학회 2017 전기학회논문지 Vol.66 No.12

This paper presents a fuzzy logic system with data preprocessing to make the accurate electric power load prediction system. The fuzzy logic system acceptably treats the hidden characteristic of the nonlinear data. The data preprocessing processes the original data to provide more information of its characteristics. Thus the combination of two methods can predict the given data more accurately. The former uses TSK fuzzy logic system to apply the linguistic rule base and the linear regression model while the latter uses the linear interpolation method. Finally, four regional electric power load data in taiwan are used to evaluate the performance of the proposed prediction system.