• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.7C
• /
• pp.719-725
• /
• 2003

The content-based image retrieval system extracts features of color, shape and texture from raw images, and builds the database with those features in the indexing process. The search in the whole retrieval system is defined as a process which finds images that have large similarity to query image using the feature database. This paper proposes a new two-stage search method in the content-based image retrieval system. The method is that the features are condensed and stored by the property of Cauchy-Schwartz inequality in order to reduce the similarity computation time which takes a mostly response time from entering a query to getting retrieval results. By the extensive computer simulations, we have observed that the proposed two-stage search method successfully reduces the similarity computation time while maintaining the same retrieval relevance as the conventional exhaustive search method. We also have observed that the method is more effective as the number of images and dimensions of the feature space increase.

• Journal of the Korean Society for Library and Information Science
• /
• v.17
• /
• pp.299-337
• /
• 1989

In this study, an Intermediary System for Searches has been designed and implemented. This System has some key functions as follows: First, in each stage of a search process, the information related to a definite term could be provided. Second, through displaying semantic categories for terms this system 'helps a searcher in modifying search terms when he fails to retrieve. Third, being consulted, this system could assist a searcher in making a Boolean search statement in terms of search specificity Fourth, to make a final search statement, a searcher could have several rounds of search cycles through displaying related terms, semantic categories, and frames.

• Journal of Wellbeing Management and Applied Psychology
• /
• v.6 no.1
• /
• pp.23-31
• /
• 2023

Purpose: The purpose of this study is a preliminary study to develop a comprehensive information avoidance scale that includes various search contexts. Research design, data and methodology: This study is a part of exploratory sequential design of mixed method for the development of information avoidance scale. Based on the themes derived from the analysis of the in-depth interview data collected in the qualitative research of the first stage of the study, 45 preliminary items on information search and avoidance were constructed. The factors related to information searching included information recognition, information seeking purpose, and information search expectations. Individual, information, time, and system factors were related to information avoidance. Pearson's correlation analysis was performed for the correlation between factor items, and Cronbach's alpha analysis was performed for the reliability analysis of the items. Exploratory factor analysis was applied to examine the construct validity of 35 items of information avoidance. Results: Among the information avoidance items, one of the less relevant among information purpose items, two information factor items, and one time factor item were excluded. Conclusions: A secondary survey should be conducted to confirm the validity and reliability of the scale composed of adjusted items (35) based on the results of exploratory factor analysis. The strength of this preliminary scale is that it was developed based on vivid qualitative data of ordinary people who had experiences of search and avoidance in various search contexts.

• The Research Journal of the Costume Culture
• /
• v.26 no.1
• /
• pp.1-18
• /
• 2018

In this study, the effects of anticipated regret and perceived uncertainty on price sensitivity or purchase hesitation in overseas purchasing are analyzed along with the effects of price sensitivity on purchase hesitation. The survey was conducted among internet fashion consumers with experience in overseas purchasing and 480 responses were used in the data analysis. The results showed the psychosocial anticipated regret positively influenced the price importance, and the service, product and psychosocial anticipated regret positively influenced the price search. The preference and psychology uncertainty positively influenced the price importance, and the information and psychology uncertainty positively influenced the price search. The price importance positively influenced payment stage hesitation and shopping cart abandonment, and the price search positively influenced purchase hesitation in overseas purchasing. The functional, service and psychosocial anticipated regret positively influenced payment stage hesitation, and the service and psychosocial anticipated regret positively influenced shopping cart abandonment and overall purchase hesitation. In addition, the perceived uncertainty positively influenced the payment stage hesitation, and the information and psychology uncertainty positively influenced the shopping cart abandonment and overall purchase hesitation. The results of this study will be helpful for developing the marketing strategy for customer relationship management for overseas internet shopping web-sites.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.21 no.3
• /
• pp.487-501
• /
• 1997

Prepurchase decision making process was investigated for the female high school students grouped by fashion leadership. Differences in the fashion leadership were also investigated among the groups determined by demographic characteristics. Data were obtained from 600 female students attending at 4 different high schools in Seoul by self-administered questionnaires, and 430 were used for the data analysis. Respondents were divided into 5 groups by fashion leadership: innovators(6.3%), early adopters(29.8%) , early majority(43.7%) , late majority(16.9%) , laggards(3.3%) , The groups with higher monthly allowances and monthly clothing expenditures showed higher fashion leadership. At the problem recognition stage, students with higher fashion leadership felt buying needs more often than those with lower fashion leadership. At the information search stage, students with higher fashion leadership tended to use higher number of information sources and mass media, visited stores more often, spent more time and collected new information more often, and tended to show higher satisfaction levels with searched information than students with lower fashion leadership. Leaders tended to search information at bonded goods stores and small shops in the area, and laggards prefered to visit small stores in the market. At the alternative evaluation stage, students with higher fashion leadership reported to use higher number of evaluative criteria and consider brand name, acknowledgment of others, becomingness with wardrobe as important criteria for evaluating apparel products; those with lower fashion leadership thought utility, comfort, size, sewing quality an6 fit as key criteria.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.103-128
• /
• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.37 no.3
• /
• pp.41-56
• /
• 2000

In this paper, we propose a two-stage fast block matching algorithm using the mean absolute error (MAE) of neighbor search point that can reduce the computational complexity to estimate motion vector while the motion estimation error performance is nearly the same as full search algorithm (FSA) In the proposed method, the lower bound of MAE 6at current search point IS calculated using the MAE of neighbor search point And we reduce the computational complexity by performing the block matching process only at the search point that has to be block matched using the lower bound of MAE The proposed algorithm is composed of two stages The experimental results show that the proposed method drastically reduces the computational complexity while the motion compensated error performance is nearly kept same as that of FSA.

• The Journal of Fisheries Business Administration
• /
• v.35 no.1
• /
• pp.57-85
• /
• 2004

To develop an blue tourism website(BTW) for electronic commerce(EC), information requirements of BTW are defined firstly. We defined information requirements of BTW from two aspects, i.e., front office and back office. Information requirements for front office were derived by consumer purchasing decision process. And information requirements for back office were derived by tourism value chain. Total 29 functions are identified as critical EC related functions of BTW. Among them, 25 functions were investigated into BTW. BTWs were searched by search engines - Yahoo and Empas - to Korean websites. There are 12 specialized BTWs, except one cyber museum website. For 12 websites, 25 functions were probed. By the results, in need recognition stage of blue tourism, only weather information was provided in most websites. In information search stage of blue tourism, package recommendation and various contents were provided in most websites. In consumption stage of blue tourism, traffic information were provided in most websites. And in after - sales service stage of blue tourism, bulletin board function was implemented in most websites. The rest of the functions were scarcely implemented. On the whole, it was concluded that most EC related functions of BTW in Korea were not implemented properly. To improve the status quo, it is expected in the dimension of individual website, that marketing planning, customized service, intelligent service, reinforcing purchasing assistance functions, customer relationship management, and escrow service etc. need to be implemented. And it is expected in the dimension of blue tourism industry, that standardizing product catalog, security assistance policy, information sharing by industrial database, finding referral model of BTW, elevating information mind, revising related laws etc. are needed.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.10 no.2
• /
• pp.53-61
• /
• 2011

This research presents an adaptive fast motion estimation (ME) computation on the stage of uneven multi-hexagon grid search (UMHGS) algorithm included in an unsymmetrical-cross multi-hexagon-grid search (UMHexagonS) in H.264 standard. The proposed adaptive method is based on statistical analysis and previously obtained motion vectors to reduce the computational complexity of ME. For this purpose, the algorithm is decomposed into three processes: skipping, terminating, and reducing search areas. Skipping and terminating are determined by the statistical analysis of the collected minimum SAD (sum of absolute difference) and the search area is constrained by the slope of previously obtained motion vectors. Simulation results show that 13%-23% of ME time can be reduced compared with UMHexagonS, while still maintaining a reasonable PSNR (peak signal-to-noise ratio) and average bitrates.

• Journal of Digital Convergence
• /
• v.10 no.7
• /
• pp.123-128
• /
• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.