• Journal of Acupuncture Research
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• 제26권6호
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• pp.225-239
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• 2009

Objectives : To establish a well organized and systematic oriental medicine classification of headache, the western and oriental medicine diagnosis and treatment systems of headache were reviewed. Methods : The history and development process of western medicine classification of headache were studied. A literature review of oriental medicine classification of headache was done. The characters of each classification systems were assessed. Results : In western medicine, many international societies concerning headache have been established. Through these societies, a classification of headache which can be used by both researchers and practitioners has been suggested. And the suggested classification system is highly recommended to be used in studies in order to increase utilization. As data is accumulated, new versions of the classification system were updated. But in the case of oriental medicine, various classification systems of headache are presented in numerous literatures. But the effort to unify and systemize the oriental medicine headache classification has been in lack. Conclusions : Establishment and utilization of a standardized oriental medicine headache classification system, based on various classifications and detailed descriptions is needed.

• 한국도서관정보학회지
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• 제38권2호
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• pp.315-335
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• 2007

이 연구는 최근 어린이독서에 대한 관심과 함께 어린이도서관이 급증하고 있지만 여기에 대한 실태분석은 물론 어린이도서관을 위한 표준분류표가 제정되지 않아 도서관 실무자들과 이용자들이 많은 어려움과 불편을 감수하고 있는 현실을 생각하여, 향후 어린이도서관 전용 분류표 개발 시에 고려해야 할 기본원칙과 요건을 제안하고자 시도되었다. 이를 위해 국내 어린이도서관의 실태와 어린이도서의 특성을 분석한 후, 공립 인표어린이도서관, 기적의 도서관, 사립어린이도서관으로 대별하여 분류체계 사용현황을 살펴보았으며, 대표적인 어린이도서관 분류표인 느티나무도서관 분류표와 파랑새도서관 분류표에 대한 분석을 바탕으로 어린이도서관 전용 분류표 개발을 위한 기본사항을 제안하였다.

• 한국멀티미디어학회논문지
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• 제25권1호
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• 기술혁신학회지
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• 제10권3호
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• pp.531-554
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• 2007

본고는 그간 독립적으로 존재해 왔던 학문분류 연구분류 기술분류 산업분류 전공분류 및 취업 분류와 같은 지식활동과 관련된 분류체계를 상호 연계시켜 종합적으로 보는 새로운 모형을 제시하고 그를 구체적으로 구현하는 방법을 다룬 것이다. 중 분야 이상의 의미를 갖는 학문분류와 소 분야 이하의 의미를 갖는 연구분류를 통합시킨 학문/연구분류는, 자체가 연구분야와 적용분야로 구성되는 2차원형이지만, 한편으로는 다양한 기술분류와 산업분류로 연계되고, 다른 한편으로는 전공(교육)분류와 취업분류로 연계된다. 연계시키는 방법은 두 개 이상의 분류체계를 동시에 기재하도록 하고, 그러한 기재를 허용하는 정보시스템과 데이터베이스를 갖추고, 필요에 따라 몇 개의 분류체계를 선택하여 동시에 사용하면 된다. 본고는 새로운 분류체계를 보이고자 한 것이지만 기본적인 의도는 분류체계를 넘어선다. 지식사회의 기본적인 활동인 지식활동을 종합적으로 파악하기 위한 수단을 강구하고자 한 것이다.

• 무역학회지
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• 제46권5호
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• pp.49-63
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• 2021

The HS Convention is an agreement on the harmonized commodity description and coding system enacted by the World Customs Organization in January 1988 to promote international trade and unify the commodity classification systems internationally, and the seventh revision will take effect in January 2022. This study's main purpose is to consider criteria for classifying unmanned autonomous transport systems(UATS) in accordance with Section 17 of the HS nomenclature and to present recommendations for improvement of laws related to tariff classification which may be used to amend related laws in Korea. Currently, there are no provisions within the HS Nomenclature that classify unmanned autonomous transportation systems and equipments. Although such technologies have yet to be commercially deployed, they are being actively developed globally. Thus, this study aims to classify UATS and suggest appropriate amendments to the new edition of the HS Nomenclature and Korean law. This paper examines advance ruling cases from domestic and foreign HS classification under the revision of the HS Convention and the criteria for the classification of UATS and Domestic Korean and foreign classification case studies were investigated, along with a survey of the literature on UATS, in order to derive reasonable tariff classification criteria and present legislative implications. In conclusion, this study aims to provide legislative recommendations for how to improve the system to apply the revisions to the HS Convention to the domestic Korean statutes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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• pp.1-5
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• 2008

Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.

• 한국도서관정보학회지
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• 제39권3호
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• pp.321-344
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• 2008

이 연구는 어린이자료의 분류시스템 구축을 위한 한 방안으로 어린이들이 즐겨 찾는 국내 인터넷 포털과 어린이전문 인터넷 사이트의 디렉토리 구분 및 계층성과 인터넷서점의 어린이도서에 대한 항목구분과 계층성을 비교분석하였다. 이를 토대로 인터넷 포털에서 체계적이고 효율적인 어린이자료의 분류체계를 구성하기 위한 몇 가지 지침을 제시한 바, 그 내용은 다음과 같다. (1) 어린이네티즌들의 정보요구와 이용행태를 반영해야 한다. (2) 어린이들의 관점과 표현에 따른 용어를 선정하고 연령별 기준을 제시할 필요가 있다. (3) 이용자의 접근성과 편의성을 위해 명확한 계층성과 군집성을 유지해야 한다. (4) 주제나 개념중심의 카테고리에 어린이들의 활동과 대상을 보완하는 절충방식의 카테고리를 설정하는 것이 바람직할 것이다. (5) 교과목중심의 카테고리에 상상력과 호기심을 지속적으로 충족시켜 줄 수 있는 카테고리를 설정하고 상세한 주제별 세분을 추가할 필요가 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.