• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.470-472
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• 2006

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as environment and health, industry scene system monitoring, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modem learning techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes having the capability of simple processing and wireless communication. The proposed system is able to perform context classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to intelligent dust collecting system.

• 한국전문물리치료학회지
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• 제21권3호
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• pp.55-62
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• 2014

This study investigated the relationship between function classification systems and the Pediatric Evaluation of Disability Inventory (PEDI) functional skills in children with cerebral palsy (CP). Two hundred and eleven children with CP participated in this study. The Korean-Gross Motor Function Classification System (K-GMFCS), Korean-Manual Ability Classification System (K-MACS), Korean-Communication Function Classification System (K-CFCS), and self-care, mobility, and social function domains of the Korean-Pediatric Evaluation of Disability Inventory (K-PEDI) functional skills were measured by physical therapists or occupational therapists. All of the function classification systems were significantly correlated with PEDI functional skills ($r_s$=-.549 to -.826) (p<.05). Especially, K-GMFCS, K-MACS, and K-CFCS were correlated significantly with mobility, self-care, and social function, respectively. Using stepwise multiple regression analysis, we established that K-GMFCS, K-MACS, and K-CFCS were predictors of self-care skills (74.3%) and mobility skills (79.5%) of the K-PEDI (p<.05). In addition, K-CFCS and K-MACS were predictors of social function (65.9%) of the K-PEDI (p<.05). The information gathered in this study using the levels measured in the function classification systems may be useful to clinicians for estimating the PEDI functional skills in children with CP.

• 제어로봇시스템학회논문지
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• 제11권12호
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• pp.991-995
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• 2005

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as health, environment and habitat monitoring, military, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modern teaming techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes haying the capability of simple processing and wireless communication. The proposed system is able to perform classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to hand gesture recognition system.

• 대한물리의학회지
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• 제14권2호
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• pp.97-105
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• 2019

PURPOSE: This study examined the factors related to school-based activity performance in school-aged children with spastic cerebral palsy (CP). METHODS: The Gross Motor Function Systems (GMFCS), Manual Ability Classification System (MACS), Communication Function Classification System (CFCS) as functional classifications, and the physical activity performance of the School Function Assessment (SFA) were measured in 79 children with spastic CP to assess the student's performance of specific school-related functional activities. RESULTS: All the function classification systems were correlated significantly with the physical activity performance of the SFA ($r_s=-.47$ to -.80) (p<.05). The MACS (${\beta}=-.59$), GMFCS (${\beta}=-.23$), CFCS (${\beta}=-.21$), and age (${\beta}=-.15$) in order were predictors of the physical activity performance of the SFA (84.8%)(p<.05). CONCLUSION: These functional classification systems can be used to predict the school-based activity performance in school-aged children with CP. In addition, they can contribute to the selection of areas for intensive interventions to improve the school-based activity performance.

• 한국산학기술학회논문지
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• 제12권12호
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• pp.5709-5714
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• 2011

이 연구는 뇌성마비 아동의 기능적 수준 분류 체계의 유용성을 알아보기 위해 실시되었다. 이를 위해 2008년 9월부터 2010년 8월까지의 기간 동안 뇌성마비 아동 217명을 대상으로 대동작 기능 분류체계(GMFCS), 손 기능 분류 체계(MACS), 일상생활 활동을 측정하고 도구 간의 관계를 알아보았다. 그 결과, 대동작 기능 분류체계와 일상 생활 활동은 모든 하위 영역 및 총점과 유의한 상관이 있는 것으로 나타났다(p<.05). 손 기능 분류체계와 일상생활 활동은 이상운동형 아동에서 일상생활 하위 영역 중 의사소통과 상관이 유의하지 않은 것을 제외하고 모든 하위 영역 및 총점과 유의한 상관이 있는 것으로 나타났다(p<.05). GMFCS와 MACS의 관계를 알아본 결과, 가장 많은 분포를 나타낸 것은 GMFCS의 경우 1수준(20.3%)과 5수준(40.6%)이었고, MACS의 경우는 2수준(48.8%)과 5수준(16.6%)이었다. 결론적으로, 뇌성마비 아동의 기능적 수준 분류 체계인 GMFCS와 MACS는 임상적으로 유용한 평가 체계로 사용될 수 있을 것으로 생각된다.

• 센서학회지
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• 제24권6호
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• pp.368-372
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• 2015

In this paper, we proposed a post-processing technique for improving classification performance of electronic nose (E-Nose) system which may be occurred drift signals from sensor array. An adaptive radial basis function network using stochastic gradient (SG) and singular value decomposition (SVD) is applied to process signals from sensor array. Due to drift from sensor's aging and poisoning problems, the final classification results may be showed bias and fluctuations. The predicted classification results with drift are quantized to determine which identification level each class is on. To mitigate sharp fluctuations moving-averaging (MA) technique is applied to quantized identification results. Finally, quantization and some edge correction process are used to decide levels of the fluctuation-smoothed identification results. The proposed technique has been indicated that E-Nose system was shown correct odor identification results even if drift occurred in sensor array. It has been confirmed throughout the experimental works. The enhancements have produced a very robust odor identification capability which can compensate for decision errors induced from drift effects with sensor array in electronic nose system.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.11-18
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• 2019

One of the key objectives of control systems is to maintain a system in a specific stable state. To achieve this goal, a variety of control techniques can be used and it is often uses a feedback control method. As known this kind of control methods requires mathematical model of the system. This article presents seesaw unstable system with two propellers which are controlled without use of a mathematical model instead. The goal was to control it using training data. For system control we use a logistic regression technique which is one of machine learning method. We tested our controller on the real model created in our laboratory and the experimental results show that instability of the seesaw system can be fixed at a given angle using the decision boundary estimated from the classification method. The results show that this control method for structural equilibrium can be used with relatively more accuracy of the decision boundary.

• Journal of information and communication convergence engineering
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• 제5권3호
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• pp.245-248
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameter data base for the audio data to implement the audio data index and searching system. Audio data is classified to the primitive various auditory types. We described the analysis and feature extraction method for the feature parameters available to the audio data classification. And we compose the feature parameters data base in the index group unit, then compare and analyze the audio data centering the including level around and index criterion into the audio categories. Based on this result, we compose feature vectors of audio data according to the classification categories, and simulate to classify using discrimination function.

• 디자인학연구
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• 제16권3호
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• pp.351-360
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• 2003

현재의 복잡한 커뮤니케이션 환경에서 장르의 융합현상 등 장르체계의 현상에 대한 인식이 효율적 커뮤니케이션 디자인의 지표가 될 수 있다. 장르는 예술분야에서 그 실체나 특성의 구분을 위해 적용되었던 개념으로 오늘날에는 다양한 커뮤니케이션 체계의 구조나 유형을 분석하기 위해 활용될 수 있다. 장르를 구분하기 위한 기준요소의 정의는 형태와 내용의 체계를 파악함으로써 이루어졌다. 그동안 새로운 커뮤니케이션 툴인 웹의 유형별 분류를 위해서 웹이 추구하는 목적에 따라 그 기준을 적용해 왔다. 본고에서는 커뮤니케이션 매체로써 웹의 유형분류를 위해 장르의 개념을 적용하여 웹 장르의 구성요소를 형태와 내용, 그리고 기능의 체계를 파악하였다. 형태와 내용에 의한 웹 장르의 특성을 정의함으로써 커뮤니케이션 차원의 웹 유형을 보다 명확하게 구분되도록 논하였다. 형태와 내용, 기능의 장르요소가 커뮤니케이션과정에서 상호 작용하는 이론적 체계를 정립해 보기 위해 기호와 기호대상, 해석체로 이루어지는 기호작용이론인 퍼스의 3항 기호론을 적용하였다. 기호자체는 웹의 고유 목적인 커뮤니케이션의 기능에 대입되고, 기호대상은 웹의 형태에 대입되고, 기호의 해석체에 해당하는 것은 의미의 발생과 작용을 하는 웹의 내용에 대입된다. 또한 기호는 기호대상을 정의하고 해석체에 의해 정의될 수 있으므로 이를 웹 장르 요소의 관계구조에 적용하면, 웹의 형태는 기능을 따르고, 기능은 웹의 내용을 따른다는 논리의 확장이 가능하다. 이러한 이론적 대입을 통하여 웹의 커뮤니케이션과정에서 장르요소가 체계를 가지고 의미작용을 한가는 개념이 정립되었다. 현재의 복잡한 커뮤니케이션 환경에서 장르의 융합 등, 장르체계의 현상에 대한 개념인식이 효율적 커뮤니케이션 디자인의 지표가 될 수 있을 것이다.