• Journal of Internet of Things and Convergence
• /
• v.9 no.2
• /
• pp.61-69
• /
• 2023

In recent years, due to heightened environmental awareness, Electric Power Steering (EPS) has been increasingly adopted as the steering control unit in manufactured vehicles. This has had numerous benefits, such as improved steering power, elimination of hydraulic hose leaks and reduced fuel consumption. However, for EPS systems to respond to actions, sensors must be employed; this means that the consistency of the sensor's linear variation is integral to the stability of the steering response. To ensure quality control, a reliable method for detecting defects and assessing linearity is required to assess the sensitivity of the EPS sensor to changes in the internal design characters. This paper proposes a data-driven defect and linearity assessment monitoring system, which can be used to analyze EPS component defects and linearity based on vehicle speed interval division. The approach is validated experimentally using data collected from an EPS test jig and is further enhanced by the inclusion of a Graphical User Interface (GUI). Based on the design, the developed system effectively performs defect detection with an accuracy of 0.99 percent and obtains a linearity assessment score at varying vehicle speeds.

• Journal of the Korea Convergence Society
• /
• v.13 no.3
• /
• pp.67-75
• /
• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• Journal of Korea Multimedia Society
• /
• v.22 no.11
• /
• pp.1324-1337
• /
• 2019

This study set out to investigate an energy harvesting device to ensure stable energy supply to batteries and data collection devices and a monitoring system for acer pictum sap to check collected data. Acer pictum sap farmers have written down weather information and yield of acer pictum sap manually for data storage. Since the job is done manually, there are many missing values in their data. In addition, it is not easy to manage batteries due to the characteristics of the areas where acer pictum sap is collected. The present study thus decided to build an energy harvesting device based on new renewable energy to ensure stable energy supply by taking into consideration power load, daily power consumption, and number of days with no sunshine for various devices. For a monitoring system, the investigator proposed a JSP-based web page to monitor temperature, humidity, volume of collected water, and battery state in real time. The proposed energy harvesting device was applied to reduce missing values in data. It promoted stable energy supply to the batteries and data collection devices, reducing the percentage of missing values in data from 30.55% to 0%.

• Journal of Digital Convergence
• /
• v.18 no.11
• /
• pp.59-71
• /
• 2020

The paper reports on a systematic literature review results concerning the smart factory research in Korea. 144 papers were identified from the articles published in Korean journals listed in the Korean citation index by keyword search related to smart factory. Bibliometric analyses were conducted by way of co-occurrence and network analysis using the VOSViewer. Automation, intelligence, and bigdata were identifed as three critical clusters of research while, operating systems, international policy and cases, concept analysis as other three clusters of research. Internet of Things turned out to be a key technology of smart factory linking all of these areas. Servitization studies were small in numbers but seemed to have a lot of potential. Security researches seemed to be lacking connections with other areas of studies. Results of this study can be used as a milestone for identifying future research issues in smart factories.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.5
• /
• pp.1035-1045
• /
• 2015

Recent studies in Big Data Analysis are showing promising results, utilizing the main memory for rapid data processing. In-memory computing technology can be highly advantageous when used with high-performing servers having tens of gigabytes of RAM with multi-core processors. The constraint in network in these infrastructure can be lessen by combining in-memory technology with distributed parallel processing. This paper discusses the research in the aforementioned concept applying to a test taxi hailing application without disregard to its underlying RDBMS structure. The application of IMDG technology in the application's backend API without restructuring the database schema yields 6 to 9 times increase in performance in data processing and throughput. Specifically, the change in throughput is very small even with increase in data load processing.

• The Journal of Industrial Distribution & Business
• /
• v.11 no.5
• /
• pp.45-52
• /
• 2020

Purpose: In the 4th industrial revolution, services that incorporate various smart technologies in the tourism sector have begun to gain popularity. Accordingly, academic discussions on smart tourism have also started to become active in various fields. Despite recent research, the definition of smart tourism is still ambiguous, and it is not easy to differentiate its scope or characteristics from traditional tourism concepts. Thus, this study aims to analyze the perception of smart tourism exposed online to identify the current point of smart tourism in Korea and present the research direction for conceptualizing smart tourism suitable for the domestic situation. Research design, data, and methodology: This study analyzes the perception of smart tourism exposed online based on 20,198 news data from portal sites over the past six years. Data on words used with smart tourism were collected from the leading portal sites Naver, Daum, and Google. Text mining techniques were applied to identify the social awareness status of smart tourism. Network analysis was used to visualize the results between words related to smart tourism, and CONCOR analysis was conducted to derive clusters formed by words having similarity. Results: As a result of keyword analysis, the frequency of words related to the development and construction of smart tourism areas was high. The analysis of the centrality of the connection between words showed that the frequency of keywords was similar, and that the words "smartphones" and "China" had relatively high connection centrality. The results of network analysis and CONCOR indicated that words were formed into eight groups including related technologies, promotion, globalization, service introduction, innovation, regional society, activation, and utilization guide. The overall results of data analysis showed that the development of smart tourism cities was a noticeable issue. Conclusions: This study is meaningful in that it clearly reflects the differences in the perception of smart tourism between online and research trends despite various efforts to develop smart tourism in Korea. In addition, this study highlights the need to understand smart tourism concepts and enhance academic discussions. It is expected that such academic discussions will contribute to improving the competitiveness of smart tourism research in Korea.

• Journal of Digital Convergence
• /
• v.15 no.5
• /
• pp.61-69
• /
• 2017

This study aims to investigate the public big data standard analysis model developed by Ministry of the Interior and examine its accuracy and reliability of prediction. To do this, big data standard analysis index were calculated to apply them to the real world case of CCTV monitoring system prior installation in K city. The result of this case study revealed that the areas to be installed CCTV consisted with the area where residences requested and complained to install CCTV monitoring systems, which indicated that the result of big data standard analysis model provided accurate and reliable outcomes. The result of this study suggested implications on effective exploitation of big data analysis.

• Smart Media Journal
• /
• v.4 no.4
• /
• pp.86-92
• /
• 2015

Recently, it is entering the hyper connectivity age due to the development of sensor and communication technology. In particular, it is emerging new industries such as the IoT, bigdata, cloud by convergence with the ICT and other industries. Because these industries are high the gravity of the software, the demand for software manpower is increasing rapidly. But university curriculum don't deviate from the traditional curriculum, and lack of positive response to these changes is occurring a mismatch with the industry demand. In this paper, investigate a software curriculums of the four-year university, and will attempt to investigate the perception about the university software course of the corporate perspective. Also, we draw a on-site fitness of universities training course by analysis of importance on software training courses between universities and businesses. Finally, we propose a strategy model for software training course design appropriate for the field.

• Journal of Digital Convergence
• /
• v.15 no.12
• /
• pp.333-340
• /
• 2017

Since realtime search words are centered on the fact that the search growth rate of an issue is rapidly increasing in a short period of time, it is not possible to express an issue that maintains interest for a certain period of time. In order to overcome these limitations, this paper evaluates the daily and hourly persistence of the realtime words that belong to the top 10 for a certain period of time and extracts the search word that are constantly interested. Then, we present the method of using the time series analysis and the neural network to know how the interest of the upper search word changes, and show the result of forecasting the near future change through the actual example derived through the method. It can be seen that forecasting through time series analysis by date and artificial neural networks learning by time shows good results.

• Journal of Digital Convergence
• /
• v.14 no.12
• /
• pp.237-243
• /
• 2016

Since realtime search keywords of portal sites are arranged in descending order by instant increasing rates of search numbers, they easily show issues increasing in interests for a short time. But they have the limits extracted different results by portal sites and not shown issues by a period. Thus, to find key issues from the whole realtime search keywords for certain period, and to show results of summarizing them and analyzing differences, is significant in providing the basis of understanding issues more practically and in maintaining consistency of them. This paper analyzes differences of week key issues extracted from week analysis of realtime search keywords provided by two typical portal sites. The results of experiments show that the portal group means of realtime search keywords by the independent t-test and the survival functions of realtime search keywords by the survival analysis are statistically significant differences.