• Archives of design research
• /
• v.13 no.3
• /
• pp.81-90
• /
• 2000

There are a great number of difficulties without Interface Guideline, even though the utility of the web in the educational field has been increased. In spite of having a guideline there still remains problems, when the researcher develops a practical web design, because of uniformity and universality. The purpose of this research will give a good model and a guideline, developing a way of web-site assessment through psychological language. First, the researcher has to induce psychological language and recognize the relevance of the principle of device system. Second, they should build an assessment model based on an established system of classification. As a result, they recognized that an assessment model based on the system of psychological language can help in working out authentic design problems. The designer faces many difficulties when using Interface Guideline for the sake of the existing software developer because of specific terminology. On the contrary, these days, the guideline of psychological language system provides the designer with easy comprehension of language and also able to perceive problems in advance. In addition, the researcher can realize that it can be used, as a good source and data.

• Proceedings of the Korea Society of Design Studies Conference
• /
• 2000.11a
• /
• pp.136-137
• /
• 2000

교육 분야에서 이미 웹을 기반으로 하는 교육 프로그램들이 개발되어, 많은 대학에서 사이버 강의를 시행하는 용도로 사용하고 있으며, 이를 토대로 진보된 교과과정인 멀티미디어식 교육을 실현화하기 위한 노력이 진행되고 있다. (중략)

• Information Systems Review
• /
• v.21 no.1
• /
• pp.23-38
• /
• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.4
• /
• pp.35-41
• /
• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.