• ETRI Journal
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• 제43권1호
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• pp.82-94
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• 2021

In this work, six voiced/unvoiced speech classifiers based on the autocorrelation function (ACF), average magnitude difference function (AMDF), cepstrum, weighted ACF (WACF), zero crossing rate and energy of the signal (ZCR-E), and neural networks (NNs) have been simulated and implemented in real time using the TMS320C6713 DSP starter kit. These speech classifiers have been integrated into a linear-predictive-coding-based speech analysis-synthesis system and their performance has been compared in terms of the percentage of the voiced/unvoiced classification accuracy, speech quality, and computation time. The results of the percentage of the voiced/unvoiced classification accuracy and speech quality show that the NN-based speech classifier performs better than the ACF-, AMDF-, cepstrum-, WACF- and ZCR-E-based speech classifiers for both clean and noisy environments. The computation time results show that the AMDF-based speech classifier is computationally simple, and thus its computation time is less than that of other speech classifiers, while that of the NN-based speech classifier is greater compared with other classifiers.

• Journal of applied mathematics & informatics
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• 제28권1_2호
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• 한국정밀공학회지
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• 제33권9호
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• pp.715-721
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• 2016

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

• The Journal of Korean Physical Therapy
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• 제20권1호
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• pp.49-56
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• 2008

Purpose: Lack of a valid prognosis of gross motor development in children with cerebral palsy (CP) and the absence of longitudinal data on which to base an opinion in Korea have made it difficult to plan treatment and counsel prognosis issues accurately. The purposes of this study were to examine whether the Gross Motor Function Classification System (GMFCS) is valuable to prognostication about gross motor progress in children with CP in Korea. Methods: Medical records of 61 patients were retrospectively reviewed that visited outpatient department and were diagnosed as CP. Various information was surveyed including CP type, visual acuity, cognitive function, motor acquisition age, ambulatory status, development curves of Gross Motor Function Measure (GMFM) according to each of the 5 level of GMFCS. All of them were compared with other studies. Also the gross motor development curves and the maximum GMFM score derived from this study were compared with the Palisano's report and the Rosenbaum's report. Results: Based on a total of 494 GMFM assessments provided by this study, the 5 distinct motor development curves and the maximum GMFM score were created. These observations is corresponding with the Palisano's and the Rosenbaum`s Development curves. Conclusion: The 5 distinct motor development curves (GMFCS) that were created by Palisano's and Rosenbaum's study is useful in Korea, providing parents and clinicians with a means to plan interventions and to judge progress over time.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• 한국환경복원기술학회지
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• 제13권6호
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• pp.117-131
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• 2010

This study was conducted to utilize as basic information for the construction of conservation and estimation system for Palustrine wetland, which was badly managed and imprudently reclaimed, through the analysis of distribution characteristics and the estimation of conservation value for sample sites (eight wetlands) in rural area. As the result of wetland type classification, these wetlands was classified by 4 types (Permanent freshwater marshes/pools, ponds, Aquaculture ponds, and Seasonally flooded agricultural land) by Ramsar system, 3 types (Emergent Wetland, Aquatic Bed, and Scrub-Shrub Wetland) by NWI (Cowardin) System, 5 types (Farm Pond Depression, Under-flow wetland, Man-made Pond Depression, Abandoned Paddy Fields Wetland, and Reservoir Shore) by National Wetland's Categorical System, and 3 types (Aquatic Bed Wetland, Emergent Wetland, and Forested Wetland) by Lee (2000) System. These results suggest us developing the new type classification system for small Palustrine wetland in Korean rural areas. The score of function assessment (The Modified RAM) for small Palustrine wetlands was high at the wetlands nearby hills and rice paddy fields, and low at those nearby upper fields, which was mainly affected by land-use and vegetation. The functions as 'Flood/Storm Water Storage', 'Runoff Attenuation', 'Water Quality Protection' were resulted by the structural difference of inflow and outlet. Some functions as 'Wetland size', 'Wetland to immediate watershed ratio', 'Presence of boat traffic', 'Maximum water depth', 'Fetch of water's body' of RAM were not appropriate in evaluation of small wetlands in rural area. Which suggest us developing the new function assessment system for small Palustirne wetland in Korean rural areas.

• 한국정보처리학회논문지
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• 제6권11호
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• pp.2863-2874
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• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.

• 한국정밀공학회지
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• 제6권3호
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• pp.109-119
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• 1989

This paper discusses the system layout of flexible manufacturing system. A definition of flexible manufacturing system has not been necessarily classified yet. An understanding, and an objective of its application are different in a variety of industries. It could be treated as the system adopting flexble-Automation and FMS has been improving as a form of parts maching system. It was thought that the problems of machining function and transfer function were important. This paper introduces parts family and machine groups to increase machining function and transfer function. Parts family and machine tool groups were made up by means of multidimensional dizitizing analysis. A new software algorithm for forming parts family and machine groups has been proposed. Flexible manufacturing system was layout according to the FMS transfer function classification.

• 기록학연구
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• 제37호
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• pp.189-237
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• 2013

표준 기록관리시스템(이하 RMS)이 2007년 중앙행정기관의 도입을 시작으로 현재 지방자치단체, 그 밖의 공공기관에도 도입되어 사용되고 있다. RMS는 전자기록환경에서 기록을 관리하기 위한 필수도구이나, 실제 RMS의 기능들이 표준이나 실무를 잘 반영하고 있는지, 얼마만큼 업무에 활용되고 있는지는 잘 알려져 있지 않다. 본 연구는 이러한 문제의식에서 출발하여, 기록관리시스템에 구현된 기능 중 '기준관리'에 대한 평가와 그 의미를 분석하고자 하였다. '기준관리'는 RMS상 기록관리기준표 기준정보관리 분류체계지정 재분류로 구성되어 있는데, 이것은 기록관리기준표 관리와 관련된, 다시 말해 기록의 분류와 처분일정의 영역이다. 분류와 처분일정은 기록에 대한 지적 통제의 중심이자 기록관리의 핵심영역이므로, 이것이 시스템에서 그 역할을 제대로 하고 있는지 분석하는 것은 중요한 일이다. 본 연구에서는 RMS의 기준관리에 대하여 기능 평가와 이용 평가를 함께 실시하였다. 기능 평가는 국내외 표준에서 제시하는 기능요건을 RMS가 얼마나 구현하고 있는지를 비교분석한 것이다. 그리고 이용 평가는 그러한 기능들을 얼마만큼 실제로 사용하고 있는지, 문제점은 무엇인지 진단한 것이다. 중앙행정기관과 광역 및 기초 지자체를 대상으로 실시한 이러한 평가를 통해 얻은 시사점들을 제도적, 기능 이용적, 행정적 측면으로 구분하여 정리하였다. RMS 기준관리 기능이, 더 나아가 RMS 시스템 전체가 기록관리 실무에서 원활하게 사용되기 위해 중요한 것은 사용자를 비롯한 이해당사자들의 소통임이 연구과정에서 드러났다. 사용자들은 RMS를 이용하면서 발생하는 요구사항을 지속적으로 제기하여야 하며, 중앙기록물관리기관은 그들의 요구사항을 분석 파악하고 이를 시스템에 반영하여, 시스템을 고도화 시키고 개선하는데 많은 노력을 하여야 한다.

• 간호행정학회지
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• 제7권1호
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• pp.15-23
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• 2001

This study was a methodological research to develop computerized patient classification system. The subjects of this investigation were 435 inpatients except redundant data and outliers in P University Hospital from January 18, 2000 to January 24, 2000. The data was analyzed by discrimination analysis and adopted discriminant variables were 1) sum of frequency for the nursing activities, 2) the number of nursing activities that do not need to consider intensity of the activities, and 3) total hours of nursing activities that need to consider their intensities. Discriminant function developed by this study classified the patients into 4 groups; class I, 251 ; class II, 125 ; class III, 39 ; class IV, 20. The Hit ratio was 89.23. Based on this study, following suggestions can be made for the future research 1. Inclusive patient classification system, which includes more expanded direct nursing care factors, need to be developed and examined. 2. This developed classification system can be utilized to evaluate patient distribution and to estimate adequate numbers of nursing staffs in each nursing unit.