• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1195-1200
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• 2009

Two important concepts in VE are "function" and "cost." Cost can be expressed quantitatively. Unlike cost, the function can only be expressed qualitatively. Thus, to accurately evaluate the performance in VE analysis, it is required that the functional aspect should be considered a qualitative one. This study suggests a procedure of function oriented evaluation which can evaluate function enhancement of a VE proposal more logically and objectively. To conduct this study, problems were induced via case analysis, and solutions were found. In addition, the existing simple evaluation procedures were corrected, and a function enhancement evaluation procedure via function classification was suggested. For function classification, the use of the concepts, which were "intended function" and "additionally obtained function," was suggested. Function oriented evaluation procedure to VE proposals which is suggested in this study is expected to be a great help in treating valuable functions through VE job plan.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.59-64
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• 2010

To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate nonlinear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.

• 대한물리의학회지
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• 제12권4호
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• pp.19-28
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• 2017

PURPOSE: The purpose of this study was to describe the Proprioceptive Neuromuscular Facilitation (PNF) Intervention strategy applied International Classification of Functioning, Disability and Health (ICF) Tool about strength, range of motion, scapular stability, pain and function of shoulder for patients with adhesive capsulitis. METHODS: The data was collected by patient with adhesive capsulitis. The patient was a 50-year-old male diagnosed with right shoulder with adhesive capsulitis. We applied the PNF Intervention strategy applied ICF Tool to patient with adhesive capsulitis. PNF interventions were consisting of such as combination of isotonic and stabilizing reversal technique and various positions. PNF interventions were applied, such as those aiming at decreasing pain and disability and increasing range of motion and function for the four weeks. Parameters of result were collected for strength, range of motion, scapular stability, pain and function of shoulder using the hand held dynamometer, goniometer, lateral scapula slide test, and shoulder pain and disability index, respectively. RESULTS: Clinical benefits were observed the patient with adhesive capsulitis for strength, range of motion, scapular stability, pain, and function of shoulder. The patient with adhesive capsulitis improved strength, range of motion, scapular stability, pain, and function of shoulder. CONCLUSION: Patient reported improved strength, range of motion, scapular stability, pain, and function of shoulder after intervention.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.66-72
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• 2023

Little attention appears to have been paid to the relevance of learning a good representation function in solving long tail tasks. Therefore, we propose a new loss function to ensure a good representation is learnt while learning to classify. We call this loss function Triplet Class-Wise Difficulty-Based (TriCDB-CE) Loss. It is a combination of the Triplet Loss and Class-wise Difficulty-Based Cross-Entropy (CDB-CE) Loss. We prove its effectiveness empirically by performing experiments on three benchmark datasets. We find improvement in accuracy after comparing with some baseline methods. For instance, in the CIFAR-10-LT, 7 percentage points (pp) increase relative to the CDB-CE Loss was recorded. There is more room for improvement on Places-LT.

• 한국콘텐츠학회논문지
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• 제9권5호
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• pp.182-188
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• 2009

본 연구는 국제 기능 장애 건강분류의 구성요소에 기반을 둔 자기관리 훈련이 경직성 뇌성마비 아동의 기능적 독립성에 미치는 영향을 알아보고자 하였다. 연구대상은 K광역시 소재 병원 두 곳의 재활의학과에 내원하는 35개월 이상 72개월 미만인 경직성 뇌성마비 아동(남아=25, 여아=18) 중 GMFCS(Gross Motor Function Classification System) level III-IV인 아동 43명으로 구성하였다. 연구기 간은 2008년 8월 1일부터 2008년 9월 31일까지였고, 자기관리 훈련은 2인의 작업치료사에 의해 회당 30분씩 주 4회 제공되었다. 자기관리 훈련은 먹기, 꾸미기, 목욕하기, 화장실 사용하기의 4개 영역으로 구성하였다. 훈련 후 경직성 뇌성마비 아동의 기능적 독립성 변화는 Wee-FIM(Functional Independence Measure for Children)을 이용하여 측정하였다. 연구결과 자기관리 훈련 후 뇌성마비 아동의 기능적 독립성은 유의한 향상을 보였다. 위의 결과에 근거하여 자기관리 훈련을 경직성 뇌성마비 아동의 기능적 독립성 향상을 위해 효과적인 방법으로서 충분히 활용 할 수 있을 것으로 기대한다.

• 한국임상보건과학회지
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• 제5권3호
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• pp.973-980
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• 2017

Purpose. To understand the effectiveness of training programs to improve the daily living activities and social adaption abilities of stroke patients. Methods. The participant included 1 stroke patient in Jeonbuk, from March to August 2016. The test tools used was the Canadian occupational performance measurement, International classification of function, disability and health co-resets. After applying the training program, I compared the daily life satisfaction and social adaption abilities. Results. According to this study, both daily life satisfaction and social adaption abilities improved. Conclusion. In conclusion, after an exercise program, self- help program, and underwater exercise programs, daily life satisfaction and social adaptation levels were improved. Therefore, the programs that apply to stroke patients could be found to be effective.

• 대한물리의학회지
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• 제16권3호
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• pp.65-80
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• 2021

PURPOSE: This study was conducted as a literature review to analyze the research trends related to the International Classification of Functioning, Disability and Health (ICF) in Korea from 2015 to 2020. METHODS: Precedent studies were searched with the search term "ICF" or "international classification of functioning, disability and health" from the databases of RISS, KISS, DBpia, and Pubmed. The inclusion criteria are that the studies have been carried out in Korea from 2015 to 2020 using ICF by researchers consisting of one or more Koreans and have been peer-reviewed. RESULTS: Of the total 269 studies, 107 that met the inclusion criteria were analyzed. It was found that these studies were published at a similar frequency each year. The most common area of expertise was identified as the clinical area (n = 67), followed by special education (n = 21) and social welfare (n = 13). The study subject groups were mostly patients (n = 39), disabled people (n = 25), and related experts (n = 13). The most common research topic was functioning evaluation (n = 49) and followed by a literature review (n = 29), and the most frequently used components in all the areas of expertise were activity and participation (n = 98), body function and structure (n = 73), and environmental factors (n = 61). CONCLUSION: For the past six years, domestic ICF-related research has been conducted in a wider range of expertise areas on more subdivised subject groups. Continuous research, development of standardized curricula and contents, and development of coding tools are considered to be important in vitalizing the use of the ICF.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.175-184
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• 2008

To construct user profiles automatically, an extraction method for representative keywords from a set of documents is needed. In our previous works, we suggested such a method and showed its usefulness. Here, we apply it to the classification problem and observe how much it contributes to performance improvement. The method can be used as a linear document classifier with few modifications. So, we first evaluate its performance for that case. The method is also applicable to some non-linear classification methods such as GIS (Generalized Instance Set). In GIS algorithm, generalized instances are built from training documents by a generalization function and then the K-NN algorithm is applied to them, where the method can be used as a generalization function. For comparative works, two famous linear classification methods, Rocchio and Widrow-Hoff algorithms, are also used. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.

• KSTLE International Journal
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• 제2권1호
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• pp.35-39
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• 2001

In this paper, statistical approach is undertaken to investigate the classification of wear debris which is the key function of objective assessment of wear debris morphology. Wear tests are run to produce various kinds of wear debris. The images of wear debris from wear tests are captured with image acquisition equipment. By thresholding, two-dimensional binary images of wear debris are made and, then, morphological parameters are used to quantify the images of debris. Parametric and nonparametric discriminant method are employed to classify wear debris into predefined wear conditions. It is demonstrated that classification accuracy of parametric and nonparametric discriminant method is similar. The selected use of morphological parameters by stepwise discriminant analysis can generally improve the classification accuracy of parametric and nonparametric discriminant method.

• International Journal of Contents
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• 제14권4호
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.