• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.33-46
• /
• 2017

Service part has the attribute causing a difficulty of the systematic management like a kind of diversity, uncertainty of demand, high request for quick response against general complete product. Especially, order picking is recognized as the most important work in the warehouse of the parts since inbound cycle of the service part long but outbound cycle is relatively short. But, increasing work efficiency in the warehouse has a limitation that cycle, frequency and quantity for the outbound request depend on the inherent features of the part. Through this research, not only are the types of the parts classified with the various and specified data but also the method is presented that it minimizes (that) the whole distances of the order picking and store location about both inbound and outbound by developing the model of the demand prediction. Based on this study, I expect that all of the work efficiency and the space utilization will be improved without a change of the inbound and outbound quantity in the warehouse.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.2
• /
• pp.397-404
• /
• 2018

As the usage of internet is increasing, a lot of user behavior are written in a log file and the researches and industries using the log files are getting activated recently. This paper uses the Hadoop based on open source distributed computing platform and proposes a customized tourism system by analyzing user behaviors in the log files. The proposed system uses Google Analytics to get user's log files from the website that users visit, and stores search terms extracted by MapReduce to HDFS. Also it gathers features about the sight-seeing places or cities which travelers want to tour from travel guide websites by Octopus application. It suggests the customized cities by matching the search terms and city features. NBP(next bit permutation) algorithm to rearrange the search terms and city features is used to increase the probability of matching. Some customized cities are suggested by analyzing log files for 39 users to show the performance of the proposed system.

• Health Policy and Management
• /
• v.30 no.1
• /
• pp.82-91
• /
• 2020

Background: This study investigates the influence factors of medical service variations using medical charge and the length of stay (LOS) for urinary incontinence surgery and uterine polypectomy. Methods: The National Health Insurance claims data and Medical Resource Report by the Health Insurance Review & Assessment Service in 2016 were used. Frequency analysis, one-way analysis of variance, and Bonferroni post-hoc tests were executed for each surgery. A multilevel analysis was executed to assess the factors to the medical charge and LOS for each surgery in patient, doctor, and hospital level. Results: Fifty-two point eight percent of urinary incontinence surgery and 87.1% of uterine polypectomy were distributed in general and tertiary hospitals. Among three levels, the patient level variation was 61.5% or 77.2% in medical charge and 93.9% or 96.3% in LOS, respectively. The doctor level variation was 29.6% or 22.6% in medical charge and 0.6% or 0.0% in LOS, respectively. The institution level variation was 8.9% or 0.2% in medical charge and 5.5% or 3.7% in LOS, respectively. Number of other disease and organizational type were main factors that affected the charge and LOS for urinary incontinence surgery and uterine polypectomy. Conclusion: Medical service variations of the urinary incontinence surgery and uterine polypectomy were the largest for the patient level, followed by doctor level for the medical charge, and the institution level for the LOS.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.11
• /
• pp.3384-3389
• /
• 2009

There are two major problems with current National GAP system. One was false in traceability record because it was written or inputted by farmers or distributers and no other measures to check the accuracy was valid. The other was incapability of tracking back and recalling the contaminated agricultural products. For solving these matters, IT convergence model which combined information technology with agricultural experience is elaborated. In IT convergence model, video analytic system classifies every activity depending on the pre-programmed farming process and create the traceability data automatically. Also real time trace system based on USN would solve the problem of tracking back. This system transmits the present location and monitors data of agricultural products from farm to table at all times.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.11
• /
• pp.3916-3936
• /
• 2014

Smartphone applications like games, image processing, e-commerce and social networking are gaining exponential growth, with the ubiquity of cellular services. This demands increased computational power and storage from mobile devices with a sufficiently high bandwidth for mobile internet service. But mobile nodes are highly constrained in the processing and storage, along with the battery power, which further restrains their dependability. Adopting the unlimited storage and computing power offered by cloud servers, it is possible to overcome and turn these issues into a favorable opportunity for the growth of mobile cloud computing. As the mobile internet data traffic is predicted to grow at the rate of around 65 percent yearly, even advanced services like 3G and 4G for mobile communication will fail to accommodate such exponential growth of data. On the other hand, developers extend popular applications with high end graphics leading to smart phones, manufactured with multicore processors and graphics processing units making them unaffordable. Therefore, to address the need of resource constrained mobile nodes and bandwidth constrained cellular networks, the computations can be migrated to resourceful servers connected to cloud. The server now acts as a bridge that should enable the participating mobile nodes to offload their computations through Wi-Fi directly to the virtualized server. Our proposed model enables an on-demand service offloading with a decision support system that identifies the capabilities of the client's hardware and software resources in judging the requirements for offloading. Further, the node's location, context and security capabilities are estimated to facilitate adaptive migration.

• The Journal of Korean Association of Computer Education
• /
• v.23 no.1
• /
• pp.77-90
• /
• 2020

The purpose of the study is to provide educational implications for more effective Problem-based learning(PBL) by investigating students' learning types based on their online learning behaviors. A total of 1,341 students participated in the study, and they engaged in a six-week-long PBL program run by K University. For the study, participants' online activity data were collected. From the data, a total of 48 variables that represent their various online learning behaviors were extracted. Based on the variables, hierarchical cluster analysis was conducted to analyze learning types. Also, the differences in learning characteristics and achievements were investigated by considering types of learning. As a result, the learning types in online PBL were classified as 'high-level participation (cluster 1)', 'medium-level participation (cluster 2)', and 'low-level participation (cluster 3)'. In addition, the achievement level was found to be highest in 'high-level participation (cluster 1)' and lowest in 'low-level participation (cluster 3)'. Based on the results, the implications for improving online PBL were suggested.

• The Journal of the Korea Contents Association
• /
• v.18 no.11
• /
• pp.123-134
• /
• 2018

Recently, due to the rising incidence of disasters in the nation, there has been a growing interest in the relevance and role of science and technology in solving disaster and safety related issues. In addition, the necessities of securing the human rights of all citizens in disaster risk reduction, identifying fields of technology development for effective disaster response, and improving the efficiency of R&D investment for disaster and safety are becoming more important as the different types of disasters and stages of disaster and safety management process have been considered. In this study, we analyzed bipartite or two-mode networks constructed from an expert survey dataset of technology development for disaster and safety management. The results reveal that earthquake and fire are the two disasters affecting an individual and society at large and demonstrate that AI and big data analytics are effective supports in managing disaster and safety. We believe that such a network analytic approach can be used to explore some important implications exist for the national science and technology effort and successful disaster and safety management practices in Korea.

• Journal of Advanced Navigation Technology
• /
• v.23 no.4
• /
• pp.296-301
• /
• 2019

Artificial intelligence is closely linked to our real lives, leading innovation in various fields. Especially, as a means of transportation possessing artificial intelligence, autonomous unmanned vehicles are actively researched and are expected to be put into practical use soon. Autonomous cars and autonomous unmanned aerial vehicles are required to equip accurate navigation system so that they can find out their present position and move to their destination. At present, the navigation of transportation that we operate is mostly dependent on GPS. However, GPS is vulnerable to external intereference. In fact, since 2010, North Korea has jammed GPS several times, causing serious disruptions to mobile communications and aircraft operations. Therefore, in order to ensure safety in the operation of the autonomous unmanned vehicles and to prevent serious accidents caused by the intereference, rapid situation judgment and countermeasure are required. In this paper, based on big data and machine learning technology, we propose a countermeasure system for GPS interference that supports decision making by applying John Boyd's OODA loop cycle (detection - direction setting - determination - action).

• Smart Media Journal
• /
• v.12 no.6
• /
• pp.15-26
• /
• 2023

As face-to-face communication has become difficult due to the COVID-19 pandemic, studies have been conducted to understand the impact of non-face-to-face communication, but there is a lack of research that examines this through messenger application reviews. This study aims to identify the impact of the pandemic through Latent Dirichlet Allocation (LDA) topic modeling by collecting review data of 메신저 applications in the Google Play Store and suggest service strategies accordingly. The study categorized the data based on when the pandemic started and the ratings given by users. The analysis showed that messenger is mainly used by middle-aged and older people, and that family communication increased after the pandemic. Users expressed frustration with the application's updates and found it difficult to adapt to the changes. This calls for a development approach that adjusts the frequency of updates and actively listens to user feedback. Also, providing an intuitive and simple user interface (UI) is expected to improve user satisfaction.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.712-720
• /
• 2022

The use of social media to disseminate news and interact with project stakeholders is increasing over time in the construction industry. Such social media data can be analyzed to get useful insights of the industry such as demands of new housing construction and satisfaction of construction workers. However, there has been a limited attempts to analyze social media data related to the construction industry. The objective of this study is to collect and analyze construction related tweets to understand the overall sentiments of individuals and organizations about the construction industry. The study collected 87,244 tweets from April 6, 2020, to April 13, 2020, which had hashtags relevant to the construction industry. The tweets were then analyzed to evaluate its sentiments polarity (positive or negative) and sentiment intensity or scores (-1 to +1). Descriptive statistics were produced for the tweets and the sentiment scores were visualized in a scatterplot to show the trend of the sentiment scores over time. The results shows that the overall sentiment score of all the tweets was slightly positive (0.0365). Negative tweets were retweeted and marked as favorite by more users on average than the positive ones. More specifically, the tweets with negative sentiments were retweeted by 2,802 users on average compared to the tweets with positive sentiments (247 average retweet count). This study can potentially be expanded in the future to produce a real time indicator of the construction market industry such as the increased availability of construction jobs, improved wage rates, and recession.