• ETRI Journal
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• v.43 no.1
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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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• v.28 no.1_2
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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.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.9
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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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• v.20 no.1
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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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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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.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.6
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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.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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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.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.3
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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.

• The Korean Journal of Archival Studies
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• no.37
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• pp.189-237
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• 2013

Since central governments began to establish and use the Standard Records Management System(RMS) in 2007, more and more local governments and other public organizations have constructed RMS. RMS is the essential tool for records management in electronic environments, but it is not known how well the functions of RMS reflect standards and practice related records management or how many records managers use RMS in performing their works. This paper deals with analyzing the evaluation of 'classification & disposal schedule management' function in RMS. 'Classification & disposal schedule management' function has 4 subfunctions of review of classification & preservation period, management of the schedule items, assignment of classification scheme and reclassification. Classification and disposal schedule is at the heart of intellectual control of records and core area of records management. So it is important to analyze whether this function plays well a role in RMS or not. This research carried out evaluation of function and use about classification & disposal schedule management in RMS. Functional evaluation is to compare and analyze how well RMS meets the functional requirements which home and foreign standards give. Use evaluation is to investigate how records managers use RMS in accomplishing their task of managing classification & disposal schedule and to look into what is the problem with the use. This paper could get the implications through the survey of records managers who are working at central governments, regional local governments and basic local governments. And these implications are considered in institutional, functional, use and administrative aspect. It is important to communicate with stakeholders so that 'classification & disposal schedule management' function, further, all functions of the RMS in practice of records management could be used smoothly. Users of RMS have to raise demands or call for technical solutions of the problems which come up in use, while RMS developers and administrators must make more of an effort to satisfy their demands, reflect them on the RMS and enhance the system.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.1
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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.