• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.160-165
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• 2015

Traditional speech emotion recognition techniques recognize emotions using a general training model based on the voices of various people. These techniques can not consider personalized speech character exactly. Therefore, the recognized results are very different to each person. This paper proposes an adaptive speech emotion recognition framework made from user's' immediate feedback data using a prompted labeling technique for building a personal adaptive recognition model and applying it to each user in a mobile device environment. The proposed framework can recognize emotions from the building of a personalized recognition model. The proposed framework was evaluated to be better than the traditional research techniques from three comparative experiment. The proposed framework can be applied to healthcare, emotion monitoring and personalized service.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.173-180
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• 2015

The advent of personal healthcare record(PHR) technology has been changing the uses as well as the paradigm of internet services, and emphasizing the importance of services being personalization. But the problem of user's privacy infringement and leaking user's sensitive medical information is increasing with the fusion of PHR technology and healthcare. In this paper, we propose a security labeling scheme for privacy protection in PHR system. In the proposed scheme, PHR data can be labeled also manually based on patient's request or the security labelling rules. The proposed scheme can be used to control access, specify protective measures, and determine additional handling restrictions required by a communications security policy.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.22 no.1
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• pp.82-90
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• 2012

Objectives: This study was performed to provide workplaces with political guidelines that apply international CMRs (Carcinogens, Mutagens, Reproductive toxins) information to Public Notice of TLVs (Threshold Limit Values). We analyzed information supply status about CMRs of international agencies and compared substances for which TLVs are set in KMoEL (Ministry of Employment and Labor in Korea). Methods: We referred to the reliable literature about classification criteria of CMRs corresponding to UN GHS (Globally Harmonized System of classification and Labeling of chemicals) and Public Notice No. 2009-68 'Standard for Classification, Labeling of Chemical Substance and Material Safety Data Sheet' in KMoEL. The classification system of CMRs in professional organizations (IARC, NTP, ACGIH, EU ECHA, KMoEL, etc.) was investigated through the internet and literature. Conclusions: 191 chemical substances among total 650 substances with TLVs are classified as carcinogens. Also, 43 substances classified as mutagens, and 44 as reproductive toxicants. These results suggest that the information of CMRs in Public Notice of TLV will be reorganized to 191 carcinogens, 43 mutagens, and 44 reproductive toxicants.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.565-572
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• 2021

The role of the garment and textile industry is particularly important in the economies of many countries in the context of international goods trade. There is no denying that the garment and textile industry contributes significantly to the economic growth in the global economy. The study seeks to investigate the relationship between control variables and Vietnamese consumers' intention to buy Chinese garment products. While previous research has found some control variables influencing consumers' intention to buy products, little research has been done about the influence of control variables on consumers' intention to buy Chinese garment products, in developing countries like Vietnam. In particular, the textile industry plays an important role in export, but outsourcing is accounting for a high proportion of trade, hence, it is necessary to increase innovation to increase consumers' intention to buy domestic garment products. The data is collected from a survey of 406 Vietnamese consumers' in Hanoi city and Ho Chi Minh City. The methodology includes a mixed-method, i.e. qualitative method and quantitative method. The quantitative method applies SPSS analysis to measure the control variables' influence on Vietnamese consumers' intention to buy Chinese garment products. The results identify 1 control variable that impacts Vietnamese consumers' intention to buy Chinese garment products, which is domicile.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.485-490
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• 2022

데이터 증강기법은 추가적인 데이터 구축 혹은 수집 행위 없이 원본 데이터셋의 양과 다양성을 증가시키는 방법이다. 데이터 증강기법은 규칙 기반부터 모델 기반 방법으로 발전하였으며, 최근에는 Masked Language Modeling (MLM)을 응용한 모델 기반 데이터 증강 연구가 활발히 진행되고 있다. 그러나 기존의 MLM 기반 데이터 증강 방법은 임의 대체 방식을 사용하여 문장 내 의미 변화 가능성이 큰 주요 토큰을 고려하지 않았으며 증강에 따른 레이블 교정방법이 제시되지 않았다는 한계점이 존재한다. 이러한 문제를 완화하기 위하여, 본 논문은 레이블을 고려할 수 있는 Re-labeling module이 추가된 MLM 기반 한국어 데이터 증강 방법론을 제안한다. 제안하는 방법론을 KLUE-STS 및 KLUE-NLI 평가셋을 활용하여 검증한 결과, 기존 MLM 방법론 대비 약 89% 적은 데이터 양으로도 baseline 성능을 1.22% 향상시킬 수 있었다. 또한 Gate Function 적용 여부 실험으로 제안 방법 Re-labeling module의 구조적 타당성을 검증하였다.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.8
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• pp.301-307
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• 1985

The first four generations of computers are all based on a single basic design: the Von Neuman Processor, which is sequential and does one operation at a time. Efforts to develop concurrent or parallel computers have been carried on for many years. Data flow approach is significant in these efforts to make high speed parallel machines and expected a great deal of parallelism. In this paper we propose a preliminary data Flow Machine Model operating asynchronously on the base of Node Labelling. We introduce a concept of Node Labeling for this purpose which is relevant to the Data dependency and Parallelism. And we explain how the Node Tokens are fired in the proposed system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.83-93
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• 1998

Three dimensional data acquisition system based on the structured light is developed in this work. The system is composed of a CCD camera, slide projector, and various image processing programs. Calibration procedures and several image processing steps which are necessary to get the rnage data are described. A new grid labeling technique and a grid pattern are devised to improve the accuracy of th eobtained data. Preliminary experimental result shows that the developed system may be used as a simple and cheap 3D data acquisition system. Severla suggestions are included for further research.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.1-5
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• 2021

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

• The Korean Journal of Community Living Science
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• v.22 no.3
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• pp.379-393
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• 2011

This study compared the college students' dietary behavior for processed foods, who will be the main consumers in the future and looked for the measures to understand and establish the right food labeling system by surveying the level of understanding and utilization of food labeling. The data was analysed by SPSS win 17.0 program, and the results are as follows. For the standard of selecting processed foods, the group with high nutrition knowledge considered the reliability of foods as important and the group with low nutrition knowledge considered the products introduced in TV commercial as important. When purchasing processed foods, the group with high nutrition knowledge considered nutrition, taste, price, appearance(shape), and the consumable period more than the group with low nutrition knowledge. For trans fat, the group with high nutrition knowledge learned more about it than the group with low nutrition knowledge. The ratio of confirming food nutrition label was higher in the group with high nutrition knowledge. Regarding the level of confirming individual food labels, the highest level was for milk and dairy products. And there was significant difference for the processed products of meat, cookies, bread and noodles. It was found that the level of confirmation was higher in the group with high nutrition knowledge. And the most important indication for individual food product was the consumable period. To preserve the purchased foods, the group with high nutrition knowledge preserve the foods in line with the description written on the food cover sheet, and this group used to return or exchange the products when they found them spoiled or purchased by mistake. The group with high nutrition knowledge knew more about the nutrition indication than the group with low nutrition knowledge. The necessity of nutrition indication for processed foods and the need of education and PR(Public Relation) were acknowledged higher in the group with high nutrition knowledge. For the effect of nutrition indication, it showed that the group with high nutrition knowledge thought it would improve the quality and the group with low nutrition knowledge thought it would be helpful when comparing the product with others. The group with high nutrition knowledge showed higher understanding level about nutrition indication than the group with low nutrition knowledge.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.366-369
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• 2021

본 논문에서는 동영상에서 일련의 과정을 거쳐 얻었던 학습데이터를 보다 간편하고 빠른 속도로 획득하는 방법을 제안한다. 음성과 영상 스트림을 처리하는 ffmpeg을 이용해 영상을 프레임화하고, 딥 러닝 기반의 YOLO 알고리즘을 사용하여 객체를 검출한다.