• The KIPS Transactions:PartB
• /
• v.12B no.4 s.100
• /
• pp.473-478
• /
• 2005

The purpose of this study was to investigate the interaction effect of Web site menu designs and information structures on two information searching performances (searching and browsing) and three measures of users' attitudes (appeal, usability, and disorientation). Pronounced interaction effects occurred across four dependent variables except searching when decreasing and increasing information structure were combined with a simple selection menu and a pull-down menu. Further studies are needed to investigate additional interactions among factors of interface and information structure of Web sites.

• Journal of Advanced Marine Engineering and Technology
• /
• v.25 no.5
• /
• pp.1115-1121
• /
• 2001

This paper presents the development of a tracking algorithm for shipborad satellite antenna systems which can enhance the tracking performance. In order to overcome some drawbacks of the conventional step tracking algorithm a new tracking algorithm is proposed. The proposed algorithm searches for the best tracking angles using gradient-based formulae and signal intensities measured according to a search pattern. The effectiveness of the proposed algorithm is demonstrated through simulation using real data.

• Journal of the Korean Geotechnical Society
• /
• v.40 no.4
• /
• pp.69-79
• /
• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.