• 대한전기학회논문지:전력기술부문A
• /
• 제53권12호
• /
• pp.637-644
• /
• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• 전기학회논문지
• /
• 제64권7호
• /
• pp.1114-1124
• /
• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
• /
• pp.356-361
• /
• 1998

Neural Networks, modeled succinctly from the real nervous system of a living body, can be categorized into two folds; artificial neural network(ANN) and biological neural network(BNN). While the former has been developed to solve practical problems using function approximation capability, pattern classification) clustering algorithm, etc, the latter has been focused on verifying the information processing capability to which brain research gives an impetus, by mimicking real biological systems. However, BNN suffers Iron severe nonlinearities dealt with. A bridge between two neural networks is chaotic neural network(CNN), which simply delineate the real nor-vous system and comprises almost all the ANN structures by selecting parameters. Main research theme of this area is to develop an explanation tool to clarify the information processing mechanism in biological systems and its extension to engineering applications. The CNN has a Gaussian-shaped refractory function with hysteresis effect and the chaotic responses of it have been observed fur a wide range of parameter space. Through the examination of the coupling effects of excitatory and inhibitory connections, the secrets of information processing and memory structure will appear.

• 대한물리의학회지
• /
• 제12권3호
• /
• pp.79-84
• /
• 2017

PURPOSE: The purpose of this study is to examine how task-oriented training focused on lower extremity strengthening can affect mobility function and postural stability. METHODS: The study's subjects included 10 children with cerebral palsy: 7 girls and 3 boys between the ages of 4 and 9 whose Gross Motor Functional Classification System (GMFCS) level was I or II. Their functional mobility was gauged using the Gross Motor Function Measurement (GMFM), and their postural stability was evaluated using a force platform. Participants received task-oriented training focused on lower extremity strengthening for 5 weeks. The study used a paired t-test to investigate the difference in mobility function and postural stability of children with cerebral palsy before and after the lower extremity strengthening exercise. RESULTS: The GMFM dimensions D (standing) (p<.02) and E (walking) (p<.001) improved significantly between the pre-test and post-test. A significant increase in the posturographic center of pressure (CoP) shift and surface area of the CoP were found overall between the pre-test and post-test (p<.001). CONCLUSION: The present study provides evidence that an 8-week task-oriented training focused on strengthening the lower extremities is an effective and feasible strategy for improving the mobility function and postural stability of children with cerebral palsy.

• 동의신경정신과학회지
• /
• 제21권1호
• /
• pp.13-42
• /
• 2010

Objectives: This study is to investigate the method for assesment of cerebral palsy(CP), especislly focusing on function assesment Methods: We searched the recent date of the publication and paper in Cerebral Palsy Results: Measuring the function of children with cerebral palsy is mobility, self-care and social ability. Early adequate evaluation of motor development and prognosis of degree of long-term motor disability is very important not only for parents, but also for correct management of goal oriented rehabilitation treatment and for planning of preventive measures. 1. Gross Motor Function Classification System(GMFCS) is valuable to prognostication about gross motor progress in children with CP, using longitudinal observation. 2. Gross Motor Function Measure(GMFM) is the instrument most commonly used to measure gross motor function in children with cerebral palsy(CP). 3. Pediatric Evaluation of Disability Inventory(PEDI) is one of the most commonly used assessments for children with a disability. Conclusions: The functional Assesment of children with CP are used GMFCS, GMFM and PEDI.

• 가정간호학회지
• /
• 제3권
• /
• pp.75-85
• /
• 1996

Nursing Intervention Classification(NIC) includes the 433 intervention lists to standardize the nursing language. Efforts to standardize and classify nursing care are important because they make explicit what has previously been implicit, assumed and unknown. NIC is a standardized language of both nurse-initiated and physician-initiated nursing treatments. Each of the 433 interventions has a label, definition and set of activities that a nurse does to carry it out. It defines the interventions performed by all nurses no matter what their setting or specialty. Principles of label, definition and activity construction were established so there is consistency across the classification. NIC was developed for following reasons; 1. Standandization of the nomen clature of nursing treatments. 2. Expansion of nursing knowledge about the links between diagnoses, treatments and outcomes. 3. Devlopment of nursing and health care information systems. 4. Teaching decision making to nursing students. 5. Determination of the costs of service provided by nurses. 6. Planning for resources needed in nursing practice settings. 7. Language to communicate the unigue function of nursing. 8. Articulation with the classification systems of other health care providers. The process of NIC development ; 1. Develop implement and evaluate an expert review process to evaluate feedback on specific interventions in NIC and to refine the interventions and classification as feedback indicates. 2. Define and validate indirect care interventions. 3. Refine, validate and publish the taxonomic grouping for the interventions. 4. Translate the classification into a coding system that can be used for computerization for articulation with other classifications and for reimbursement. 5. Construct an electronic version of NIC to help agencies in corporate the classifiaction into nursing information systems. 6. Implement and evaluate the use of the classification in a nursing information system in five different agencies. 7. Establish mechanisms to build nursing knowledge through the analysis of electronically retrievable clinical data. 8. Publish a second edition of the nursing interventions classification with taxonomic groupings and results of field testing. It is suggested that the following researches are needed to develp NIC in Korea. 1. To idenilfy the intervention lists in Korea. 2. Nursing resources to perform the nursing interventions. 3. Comparative study between Korea and U.S.A. on NIC. 4. Linkage among nursing diagnosis, nursing interventions and nursing outcomes. 5. Linkage between NIC and other health care information systems. 6. determine nursing costs on NIC.

• 전기학회논문지
• /
• 제66권9호
• /
• pp.1392-1401
• /
• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• 한국주거학회논문집
• /
• 제19권3호
• /
• pp.31-38
• /
• 2008

Recently, Seoul residential real estate market showed a big change, especially in 2007. The residential property price in Seoul had been mainly affected by 5 provideces: Kangnam-gu, Seocho-gu, Songpa-gu, Gangdong-gu and Yangchun-gu, but these providences started to show the decrease in price while the other providences ironically showed the opposite direction. Therefore, this project was derived from this phenomenon recognition and the necessity as the new market trend requires. The pre-research was carried out with the point of social-population academic view, but this project provides the analysis on the new market trend by simplifying the complex valuation indexes, originated from the pre-research. In result, the aspects of the change could be categorized into time-manner classification and territorial-manner classification, in cope with the change in the satisfaction degree on the residential environment and the selection tendency of the residential property. Based on the the moving-preferred area criteria, the territorial classification was categorized into 3 areas: 5 providences, which showed the initial decrease in real estate price (area 1), the other Kangnam area (area 2), and Kangbuk area (area 3). The result illustrated the reasonable change in the satisfaction degree on the residential environment and the selection tendency of the residential property. This project was able to reach the following conclusion : Firstly, the housing development planning should be devised by the residential environment, including the view and the natural environment, not by the area. Secondly, the housing development planning in the other Kangnam area (area 2) and Kangbuk area (area 3) should embrace the business function, not the housing development only. Last, the housing development planning in Kangbuk area (area 3) should be able to enhance education and culture function and be connected by various transportation system. This project analyzes the change in the satisfaction degree on the residential environment and the selection tendency of the residential property. Thereafter, this project has the purpose of providing the aid in understanding of the basis of housing development information.

• 한국정보통신학회논문지
• /
• 제24권2호
• /
• pp.225-230
• /
• 2020

기계학습은 데이터를 기반으로 한 컴퓨터를 학습시켜 컴퓨터 스스로 데이터의 경향성을 파악하게 하여 새로운 입력 데이터의 출력을 예측하도록 하는 알고리즘이다. 기계학습은 크게 지도학습, 비지도학습, 강화학습으로 나눌 수 있다. 지도학습은 데이터에 대한 레이블이 주어진 상태로 기계를 학습시키는 방법이다. 즉, 데이터 및 레이블의 쌍을 통해 해당 시스템의 함수를 추론하는 방법으로 새로운 입력 데이터에 대해서 추론한 함수를 이용하여 결과를 예측한다. 그리고 예측하는 결과 값이 연속 값이면 회귀분석, 예측하는 결과 값이 이산 값이면 분류로 사용된다. 새로운 붓꽃 데이터 Sepal length(5.01)과 Sepal width(3.43)을 이용하여 기초 데이터와 유클리드 거리를 분석하였다. 분석결과, 테이블 3의 8번(5, 3.4, setosa), 27번(5, 3.4, setosa), 41번(5, 3.5, setosa), 44번(5, 3.5, setosa) 그리고 40번(5.1, 3.4, setosa)의 데이터 순으로 유사도가 높은 붓꽃으로 분류되었다. 따라서 이론적 실무적 시사점을 제시하였다.

• 전기학회논문지
• /
• 제63권4호
• /
• pp.534-540
• /
• 2014

In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.