• 한국통신학회논문지
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• 제32권10C호
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• pp.965-974
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• 2007

본 논문에서는 내용 기반 음악 범주 분류 시스템에서 다중 범주를 위한 특징벡터 선택 알고리즘을 제안한다. 제안된 특징벡터 선택 알고리즘은 분리 성능을 측정할 때 가우시안 혼합 모델(Gaussian Mixture Model: GMM)을 기반으로 GMM separation score을 측정함으로써 확률분포 및 분리 성능 추정의 정확도를 높였고, sequential forward selection 방법을 개선하여 이전까지 선택된 특징벡터들이 분리를 잘 하지 못하는 범주들을 기준으로 다음 특징벡터를 선택하는 알고리즘을 제안하여 다중 범주 분류의 성능을 높였다. 제안된 알고리즘의 성능 검증을 위해 음색, 리듬, 피치 등 오디오 신호의 특징을 나타내는 다양한 파라미터를 오디오 신호로부터 추출하여 제안된 특징벡터 선택 알고리즘과 기존의 알고리즘으로 특징벡터를 선택한 후 GMM classifier와 k-NN classifier를 이용하여 분류 성능을 평가하였다. 제안된 특징벡터 선택 알고리즘은 기존 알고리즘에 비하여 3%에서 8% 정도의 분류 성능이 향상된 것을 확인할 수 있었고 특히 낮은 차원의 특징벡터의 분류 실험에서는 분류 정확도 측면에서 5%에서 10% 향상된 좋은 성능을 보였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (B)
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• pp.673-675
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• 2005

The art psychotherapy expert system is a computer system which helps to analyse one's psychology through pictures. However we need a standard criterion because the psychology, the target of the art psychotherapy, does not only have a ambiguous criterion but also a vast range. We're going to suggest a criterion in the field of the art psychotherapy by constructing systematic database through knowledge acquirement of the art psychotherapy expert system. In this study we introduce a system which enables systematic classification and confirmation of symptoms according to mental analyses. The suggested system enables confirmation of a classical structure and systematic classification of knowledges through conversation by extracting nouns through sentence analysis from the knowledge of descriptive form based on the clinical purpose of sentence analysis.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1998년도 Proceedings of International Workshop on Advanced Image Technology
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• pp.165-170
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• 1998

This paper presents a new prediction method RBP region-based prediction model where the context used for prediction contains regions instead of individual pixels. There is a meaningful property that RBP can partition a cartoon image into two distinctive types of regions, one containing full-color backgrounds and the other containing boundaries, edges and home-chromatic areas. With the development of computer techniques, synthetic images created with CG (computer graphics) becomes attactive. Like the demand on data compression, it is imperative to efficiently compress synthetic images such as cartoon animation generated with CG for storage of finite capacity and transmission of narrow bandwidth. This paper a lossy compression method to full-color regions and a lossless compression method to homo-chromatic and boundaries regions. Two criteria for partitioning are described, constant criterion and variable criterion. The latter criterion, in form of a linear function, gives the different threshold for classification in terms of contents of the image of interest. We carry out experiments by applying our method to a sequence of cartoon animation. We carry out experiments by applying our method to a sequence of cartoon animation. Compared with the available image compression standard MPEG-1, our method gives the superior results in both compression ratio and complexity.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.192-197
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• 2020

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

• 무역상무연구
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• 제41권
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• pp.267-292
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• 2009

It is a right time to improve the Korea Foreign Trade Act(KFTA) as a fundamental law on Rules of Origin(RoO) in the global trade circumstances which are summarized FTA and WTO. The KFTA's RoO constitutes the labelling system of the Country of Origin, the criterion of it, the issuing of certificate of origin and the punishing offender mainly around the importing goods. This study has focused on the problems of KFTA's RoO at the macro and practical level, and proposed the programs to improve the KFTA's RoO about importing, exporting and domestic production goods. KFTA need to create a purpose clause to protect consumers and industries also, and has to be located a general and top position in the RoO of Korea. In the concrete, the labelling system of the Country of Origin has to set limited in the point of minimum necessity view. The criterion of the Country of Origin also has to improve the wholly obtained criterion, the changing in tariff classification criterion, value added criterion and processing operation criterion to harmonize WTO Rules of Origin and FTA Rules of Origin. The punishment ceiling against offender has to raise to guarantee the effectiveness of RoO.

• 논리연구
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• 제23권1호
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• pp.25-53
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• 2020

홍지호·여영서는 "'부당한 연역 논증'은 형용모순인가?"라는 논문에서 연역 논증과 귀납 논증을 구분하는 기준으로 실현 기준이 아닌 의도 기준을 지지한다. 이 논문은 그들의 주장을 비판하는 것이 목표이다. 나는 그들의 주장이 논증 재구성과 논증 분류[평가]를 헷갈리고 있으며, 의도 기준의 난점을 해명하면서 실현 기준을 들여오고 있다고 주장한다. 그들을 비롯한 대부분의 논리학자들은 논증을 연역, 귀납, 그리고 나쁜 논증으로 나눈다. 나는 연역과 귀납으로 나누어야 한다고 주장한다. 마지막으로 논리 교육에서는 연역과 귀납의 구분을 굳이 가르칠 필요가 없다고 주장한다.

• 정보기술과데이타베이스저널
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• 제8권1호
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• pp.95-104
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• 2001

In the text classification domain, labeling the training documents is an expensive process because it requires human expertise and is a tedious, time-consuming task. Therefore, it is important to reduce the manual labeling of training documents while improving the text classifier. Selective sampling, a form of active learning, reduces the number of training documents that needs to be labeled by examining the unlabeled documents and selecting the most informative ones for manual labeling. We apply this methodology to Naive Bayes, a text classifier renowned as a successful method in text classification. One of the most important issues in selective sampling is to determine the criterion when selecting the training documents from the large pool of unlabeled documents. In this paper, we propose two measures that would determine this criterion : the Mean Absolute Deviation (MAD) and the entropy measure. The experimental results, using Renters 21578 corpus, show that this proposed learning method improves Naive Bayes text classifier more than the existing ones.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.514-519
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• 1992

To make an accurate retrieval of the proportion of each category among mixed pixels (Mixel's) of a remotely sensed imagery, a maximum likelihood estimation method of category proportion is proposed. In this method, the observed multispectral vector is considered as probability variables along with the approximation that the supervised data of each category can be characterized by normal distribution. The results show that this method can retrieve accurate proportion of each category among Mixel's. And a index that can estimate the degree of error in each category is proposed. AS one of the application of the proportion estimation, a method for image classification based on category proportion estimation is proposed. In this method all pixel in a remotely sensed imagery are assumed to be Mixel's, and are classified to most dominant category. Among the Mixel's, there exists unconfidential pixels which should be categorized as unclassified pixels. In order to discriminate them, two types of criteria, Chi square and AIC, are proposed for fitness test on pure pixel hypothesis. Experimental result with a simulated dataset show an usefulness of proposed classification criterion compared to the conventional maximum likelihood criterion and applicability of the fitness tests based on Chi square and AIC,

• Korean journal of medical education
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• 제30권4호
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• pp.347-357
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• 2018

Purpose: The purpose of this study was to apply alternative standard setting methods for the Korean Medical Licensing Examination (KMLE), a criterion-referenced written examination, and to compare them to the conventional cut score used on the KMLE. Methods: The process and results of criterion-referenced standard settings (i.e., the modified-Angoff and bookmark methods) were evaluated. The ratio of passing and failing examinees determined using these alternative standard setting methods was compared to the results of the conventional criteria. Additionally, the external, internal and procedural evaluation of these methods were reviewed. Results: The modified-Angoff method yielded the highest cut score, followed sequentially by the conventional method and the bookmark method. The classification agreement between the modified-Angoff and bookmark methods was 0.720 measured by Cohen's ${\kappa}$ coefficient. The intra-panelist classification consistency of modified-Angoff method was higher than bookmark method. However, the inter-panelist classification consistency was vice versa. The standard setting panelists' survey results showed that the procedures of both methods were satisfactory, but panelists had more confidence in the results of the modified-Angoff method. Conclusion: The modified-Angoff method showed results that were more similar to those of the conventional method. Both new methods showed very high concordance with the conventional method, as well as with each other. The modified-Angoff method was considered feasible for adoption on the KMLE. The standard setting panelists responded positively to the modified-Angoff method in terms of its practical applicability, despite certain advantages of the bookmark method.

• 성인간호학회지
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• 제21권5호
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• pp.489-503
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• 2009

Purpose: This study was conducted to test criterion-related validity of the Critical Patients' Severity Classification System (CPSCS) developed by the Hospital Nurses' Association by examining relationships with brain injury severity measured by Glasgow Coma Scale (GCS), recovery state measured by Glasgow Outcome Scale (GOS), and days of stay in ICU of brain injury patients. Methods: Prospective correlational research design was adopted by including 194 brain injury patients admitted to ICU of one university hospital. Results: The score of CPSCS appeared to significantly discriminate the severity of brain injury. Among nursing activities in CPSCS, Respiratory therapy, IV Infusion and Medication, Monitoring, Activities of Daily Living (ADL), Treatment and Procedure were significant to discriminate the severity of brain injury. Respiratory therapy, Vital Signs, and Monitoring appeared to significantly discriminate the recovery states of 1- and 3-months. Nursing activities significantly contributed to predict the days of ICU stay were Respiratory therapy, ADL, and Teaching and Emotional Support. Conclusion: CPSCS developed by the Hospital Nurses Association appeared to be valid to discriminate or predict brain injury severity, recovery states, and days of stay in ICU for brain injury patients.