• Journal of Digital Convergence
• /
• v.15 no.3
• /
• pp.221-228
• /
• 2017

With the recent development of IoT technology, medical services using IoT technology are increasing in many medical institutions providing health care services. However, as the number of IoT sensors attached to the user body increases, the healthcare information transmitted to the server becomes complicated, thereby increasing the time required for analyzing the user's healthcare information in the server. In this paper, we propose a deep learning based health care information management method to collect and process healthcare information in a server for a large amount of healthcare information delivered through a user - attached IoT device. The proposed scheme constructs a subnet according to the attribute value by assigning an attribute value to the healthcare information transmitted to the server, and extracts the association information between the subnets as a seed and groups them into a hierarchical structure. The server extracts optimized information that can improve the observation speed and accuracy of user's treatment and prescription by using deep running of grouped healthcare information. As a result of the performance evaluation, the proposed method shows that the processing speed of the medical service operated in the healthcare service model is improved by 14.1% on average and the server overhead is 6.7% lower than the conventional technique. The accuracy of healthcare information extraction was 10.1% higher than the conventional method.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.6
• /
• pp.183-190
• /
• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Journal of the Korean Society of Earth Science Education
• /
• v.14 no.1
• /
• pp.59-68
• /
• 2021

The purpose of this study is to provide basic data to derive the direction of future elementary science curriculum books through delphi study of science education experts. To this end, a panel of 18 experts was formed and two delphi investigations were conducted. By analyzing the mean, median, and CVR values for each item in the Delphi survey, the priorities of changes in science education for the future society and the validity of each item's implementation method were verified. In addition, by synthesizing this, the direction of future elementary science textbooks was derived. As a result, the future elementary science textbook can be 'fun and interesting science study', 'exploration performance-oriented learning' and 'science that enjoys and participates even as an adult'. It should be developed to prepare for culture. For this, it is necessary to use materials in real life, and it is necessary to present an experiment that stimulates curiosity and easy access using materials and preparations with high accessibility. In addition, it is necessary to develop a textbook for learning that science is a discipline that is highly connected with real life, and that it is also related to future career paths.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.645-661
• /
• 2024

Today, the development of new technologies due to the 4th industrial revolution requires work performance methods such as non-face-to-face collaboration. In response to this, various VR collaboration tools are emerging, but VR collaboration tools for brainstorming, which are used in collaboration or design development work, are not provided. Therefore, despite the advantages and possibilities of VR for non-face-to-face collaboration, there are limitations in practical use. Accordingly, the development of VR collaboration tools in a digitalized work environment is necessary, and research on UI design development for this is required. The purpose of this study is to propose a VR collaboration tool prototype by developing an interaction UI design that applies user hand behavior elements that appear during collaboration sessions through user research. This study was a qualitative study. The research method was to conduct user research through observation and in-depth interviews, and as a result of analyzing the data obtained from this, five types of user hand behavior elements were derived. In this study, an interaction UI design was developed that reflects hand gestures as behavioral elements. And using Unity and the Oculus Integration SDK Kit, we created a prototype VR collaboration tool that can be used without a controller. As a result of conducting a user evaluation of the prototype produced in this study, it was found that users had difficulty making hand gestures accurately, and it was possible to find areas for improvement in UI design. It is expected that this study will help develop interaction UI design for VR collaboration tools that can increase work efficiency.

• Science of Emotion and Sensibility
• /
• v.19 no.1
• /
• pp.39-48
• /
• 2016

This research aims to identify innovative companies' attention to the emotion and sensibility and its application by analyzing the contents of the corporate annual reports. Annual report is a good reference data because it describes not only various current products and services' annual activities and business performance but also corporate future direction. Sensibility is interpreted and used with various words internationally: various related terms such as sensibility, sense, emotion, feeling and affection are analyzed not only by the definition but also the interrelationship among them, and included for the contents analysis. To select the innovative companies, the researcher used 'Fast company' that is the economic journal and deducted the companies list via 'The world's 50 most innovative companies' in 2009 and 2014. Listed companies' 2009 and 2014 annual reports' contents were analyzed to identify the rate of the recognition and the application of sensibility to their business. Even though the quantitative result of the content analysis indicates not a strong relationship between corporate innovativeness and 'sensibility' qualitative result identifies companies are paying more attention to the 'sense' and 'feeling' during five years. In conclusion, the innovation that company pursues strategically is shifting from differentiation and the technological leadership to satisfying user experiences and the number of companies which express and measure user feeling and emotions are increasing.

• Journal of Digital Convergence
• /
• v.16 no.1
• /
• pp.85-92
• /
• 2018

In this study investigated the factors for the career preparation behaviors of social welfare major students based on Lent et al. (1994)'s Social-cognitive Career Theory so as to provide essential baseline data for establishing proper career support strategies that suit the distinctive nature of social welfare studies. The participants of this study are 132 social welfare major students from three colleges who have completed social welfare field education. This study analyzed the relationship between cognitive factor (outcome expectation), vocational interest factor (major selection satisfaction), goal factor (career decision level), and work performance factor (career preparation behavior). For analysis, SPSS 24.0 and AMOS 24.0 were used. The analysis results are as follows. First, the model's goodness of fit was found to be at a statistically ideal level with CFI=.904, TLI=.887, and RMSEA=.068. Second, the result of analyzing the correlation between the primary variables is as follows: as outcome expectation increased, major selection satisfaction grew, which then increased the career decision level and led to the improvement in career preparation behavior. These results indicate the importance of developing a customized route support program considering the perceived and interesting factors of individual students to improve their career preparation behavior for social welfare majors.

• Journal of Navigation and Port Research
• /
• v.45 no.4
• /
• pp.186-193
• /
• 2021

To enhance the competitiveness of the Busan Port in accordance with changes in global shipping and port industry trends, the Busan New Port is promoting step-by-step integration and developing a port resource-sharing platform. However, inefficient resource-sharing can cause unnecessary additional costs or impede port productivity, so accurate supply and demand matching of shared resources is required. In this study, the supply and demand of port resources were investigated for employees of Busan New Port and North Port, and port resources that could be ideally shared through IPA(Importance Performance Analysis) were analyzed. As a result of analyzing the equipment in the port, Yard Tractor, Reach Stacker, and Top Handler were the top considerations, and for facilities in the port, berths and aprons, empty container yards, and refrigerated container yards were the most important considerations. As for the data in the port, gate status, equipment specifications, and berth and apron conditions were the top considerations.

• Informatization Policy
• /
• v.30 no.4
• /
• pp.40-61
• /
• 2023

The adoption of smart factories and smart manufacturing as strategies to enhance competitiveness and stimulate growth in the manufacturing sector is vital for a country's future competitiveness and industrial transformation. The government has consistently pursued smart manufacturing innovation policies starting with the Manufacturing Innovation 3.0 strategy in the Ministry of Industry. This study aims to identify policy areas for smart factories and smart manufacturing based on technical standards. Analyzing policy areas at the current stage where the establishment and support of domestic standards aligning with international technical standards are required is crucial. By prioritizing smart manufacturing process areas within the industry, policymakers can make well-informed decisions to advance smart manufacturing without blindly following international standardization in already well-established areas. To achieve this, the study utilizes a hierarchical analysis method including expert interviews and importance-performance analysis for the five major process areas. The findings underscore the importance of proactive participation in standardization for emerging technologies, such as data and security, instead of solely focusing on areas with extensive international standardization. Additionally, policymakers need to consider carbon emissions, energy costs, and global environmental challenges to address international trends in export and digital trade effectively.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.93-111
• /
• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Korea Port Economic Association
• /
• v.36 no.1
• /
• pp.129-140
• /
• 2020

In the future, the global container handling market will be reorganized into larger ships and shipping alliances, and the bargaining power of shipping companies will be further strengthened. Therefore, the global terminal operator (GTO), which has a global network, vast experience, and operational know-how, is expected to strengthen its competitiveness. In Korea, the central government promoted the development of GTOs in the mid-2000s, but it failed, mainly due to disagreements between port stakeholders. In this study, the macroeconomic indicators that have the same effect in all regions were used to analyze GTO management performance. In the short term, it could be used to establish the business strategy of domestic terminal operators based on changes in macroeconomic indicators. In the long term, it would be used to establish a promotion strategy for GTOs in Korea. The results of analyzing the impact of macroeconomic indicators on the GTO's profit show that the GTO's profit is significantly affected by cargo handling capacity, the consumer price index of the United States, the Shanghai Composite Index, the Crude Oil Price, and the London Inter-bank Offered Rate (LIBOR). However, the scale of impact was not significantly different between public and private GTOs.