• Asia pacific journal of information systems
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• v.29 no.1
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• pp.65-82
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• 2019

Technological innovations over the years have accentuated the workings in corporate- connected organizations and different application platforms. Hence, a unified management information system (MIS) that can utilize the Web and propel programming developments is required. Software agents, the latest developments in computer software technology, can be utilized to rapidly and effortlessly build integrated information systems. Consequently, 59 research papers on the use of software agents in MIS were identified from top 40 MIS journals published between 2007 and 2017. Then, we reviewed and classified all the research papers according to two categories: application fields and application categories. The application fields consisted of eight sub-groups: manufacturing, telecommunication systems, traffic and transportation management, information filtering and gathering, electronic commerce, business process management, entertainment, and medical care; whereas the application categories consisted of three sub-groups: multi-agent systems, personal assistants, and multi-agent simulation. The research papers were further divided into journal and year of publication, and journal and application field. The objective of our research was to understand the trend of the use software agent technology in MIS by examining the published research paper beside to add knowledge and content to the information system academic discipline.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of the Korean Professional Engineers Association
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• v.12 no.1
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• pp.12-20
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• 1979

The early history of gun powder (black powder) and explosives was closely connected with the discovery of methods of preparing and purifing salpetre (potassium nitrate KNO$_3$). The Chineses apparently became acquainted with salpetre firstly on about 11th century, and they were possibly the original discoverers of salpetre for raw material of gun powder. The Egyptians called it “Chinese snow”, and it is significant that Chingis-Khan, the Mongol conqueror, took the Chinese eenginees with him in 1218 to use it for attacking the fortifications of the Persian cities. The black powder was invented by chance by Chinese alchemists during the Song dynasty (11th century) in the process of manufacturing medicine, and the powder was introduced to Europe by Mongol army. The manufacturing method of salpetre and gun powder was introduced to Korea from China in 1374, and the powder alld gunnery manufacturing project was developed by Mu Sun Choe(崔茂宣), the first Korean engineer late in Koryo dynasty. Coming in to Yi dynasty the explosive technic, extractive method of salpetre, and gunnery manufacturing process were developed greatly by Mu Sun Choe and Hai Sin Choe (崔海臣). However, confronting with the Japanes invasion at Imjin War (1597) with more powerful western style rifles which had been introduced from the Portuguese, on the contrary Korean army with the traditional guns couldn't compete with them. The Chochong(烏銃, the western rifle introduced in Japane) were much superior to the Chinese style traditional guns in the shooting power and striking efficiency. On the other hand, the Japanese battle ships armed only with the Chochong, when confronted with the Korean turtle shaped ships under the commanding of Admiral Yi Sun-Sin(李舞臣), were defeated by the Korean canons on the ships. The technical development of the modern powder industry in Korea. with the construction of four big explosive plants from 1930 to 1945, has resulted the mass-production of explosives. This study was purposed to investigate to the process with regard to the details of introduction to the explosive technology in Korea, and intended to give a help to the engineers who are engaged in study of the explosive technics by means of giving a spot light data on the early process of the designs, and making suggestion to the researchers for further study and invent a new and modern explosive.

• 재활복지
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• v.22 no.3
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• pp.23-39
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• 2018

The fourth industrial revolution and technological innovation will make the job factor of people with disabilities complicated and difficult. Thus, this study analyzed the technical factors influencing the job structure and tried to find a way to develop the job of the people with disabilities in response to the 4th Industrial Revolution by changing the labor market and changing the trend of the employment by industry. The methods for this study are literature research and FGI. First, technological factors affecting the job structure of the Fourth Industrial Revolution are artificial intelligence, Internet and networking of things, 3D printing, big data, Second, technological innovation due to the industrial revolution was a major factor in the job structure. As the industrial revolution and technological innovation progressed, the job structure shifted rapidly from the manufacturing industry to the service industry, Third, as the measures of the 4th Industrial Revolution and the change of the job structure, it is necessary to make preemptive investment for the development of competency to cope with technological innovation, Finally, in order to respond to the Fourth Industrial Revolution and the rapidly changing technological innovation, the basic data of people with disabilities should be able to be big data.

• Journal of dental hygiene science
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• v.24 no.1
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• pp.9-21
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• 2024

Background: The purpose of this study was to utilize text network analysis and topic modeling to identify interconnected relationships among keywords present in patent information related to oral health, and subsequently extract latent topics and visualize them. By examining key keywords and specific subjects, this study sought to comprehend the technological trends in oral health-related innovations. Furthermore, it aims to serve as foundational material, suggesting directions for technological advancement in dentistry and dental hygiene. Methods: The data utilized in this study consisted of information registered over a 20-year period until July 31st, 2023, obtained from the patent information retrieval service, KIPRIS. A total of 6,865 patent titles related to keywords, such as "dentistry," "teeth," and "oral health," were collected through the searches. The research tools included a custom-designed program coded specifically for the research objectives based on Python 3.10. This program was used for keyword frequency analysis, semantic network analysis, and implementation of Latent Dirichlet Allocation for topic modeling. Results: Upon analyzing the centrality of connections among the top 50 frequently occurring words, "method," "tooth," and "manufacturing" displayed the highest centrality, while "active ingredient" had the lowest. Regarding topic modeling outcomes, the "implant" topic constituted the largest share at 22.0%, while topics concerning "devices and materials for oral health" and "toothbrushes and oral care" exhibited the lowest proportions at 5.5% each. Conclusion: Technologies concerning methods and implants are continually being researched in patents related to oral health, while there is comparatively less technological development in devices and materials for oral health. This study is expected to be a valuable resource for uncovering potential themes from a large volume of patent titles and suggesting research directions.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.3
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• pp.197-212
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• 2022

This study was conducted to empirically analyze the effects and interactions among beneficial bacteria, commensal bacteria, and acne bacteria, which are factors in the skin microbiomes, on detrimental bacteria by 289 people, who are 20 to 49 years old among Koreans. As a result of multiple regression models using bio big data of skin microbiomes, when the difference in skin microbiomes according to the sex and age of the subjects was controlled, the beneficial bacteria showed a negative (-) effect on the detrimental bacteria, while the commensal and acne bacteria showed a positive (+) effect. Particularly, the negative (-) effect of beneficial bacteria on detrimental bacteria was different through interaction with acne bacteria according to the level of commensal bacteria. These results demonstrate that the activation of beneficial bacteria inhibits detrimental bacteria, and the effect of skin microbiomes on detrimental bacteria is balanced with skin microbiomes through interaction with independent influence. Therefore, it is suggested that when studying skin microbiomes products to help the proliferation of beneficial bacteria and to create a skin environment that inhibits detrimental bacteria in the personalized cosmetics manufacturing industry, it is necessary to consider the independent effects and interactions among skin microbiome factors together.

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

• Knowledge Management Research
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• v.17 no.2
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• pp.27-50
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• 2016

Korean manufacturing industry have recently faced the catch-up of China in the mass commodity product, such as automotive, display, and smart phone in terms of market as well as technology. Accordingly, discussion on the importance of achieving catch-up in complex product systems (CoPS) has been increasing as a new innovation engine for the industry. In order to achieve successful catch-up of CoPS, we explored emerging technologies of CoPS, which are featured by the characteristics of radical novelty, relatively fast growth and self-sustaining, through the study of emerging technologies of gas turbine for power generation. We found that emerging technologies of the gas turbine are technologies for combustion nozzle and composition of electrical machine for increasing power efficiency, washing technology for particulate matter, cast and material processing technology for enhancing durability from fatigue, cooling technologies from extremely high temperature, interconnection operation technology between renewable energy and the gas turbine for flexibility in power generation, and big data technology for remote monitoring and diagnosis of the gas turbine. We also found that those emerging technologies resulted in technological progress of the gas turbine by converging with other conventional technologies in the gas turbine. It indicates that emerging technologies in CoPS can be appeared on various technological knowledge fields and have complementary relationship with conventional technologies for technology progress of CoPS. It also implies that latecomers need to pursue integrated learning that includes emerging technologies as well as conventional technologies rather than independent learning related to emerging technologies for successful catch-up of CoPS. Our findings provide an important initial theoretical ground for investigating the emerging technologies and their characteristics in CoPS as well as recognizing knowledge management strategy for successful catch-up of latecomers. Our findings also contribute to the policy development of the CoPS from the perspective of innovation strategy and knowledge management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1144-1151
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• 2021

The pressing process is a compression process in which a product is made by applying force to a heated or unheated material to transform it into the desired shape. Due to the characteristics of press equipment that produces products through continuous compression for a short time, product defects occur continuously, and systems for solving these problems are being developed using various technologies. This paper proposes a real-time defect detection system based on an artificial intelligence algorithm that detects defects. By attaching various sensors to the press device, the relationship between equipment status and defects is defined and collected based on a big data platform. By developing an artificial intelligence algorithm based on the collected data and implementing the developed algorithm using an embedded board, we will show the practicality of the system by applying it to the actual field.

• Journal of Platform Technology
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• v.8 no.3
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• pp.22-29
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• 2020

The 4th industrial revolution is progressing by country as a mega trend that leads various technological convergence directions in the social and economic fields from the initial simple manufacturing innovation. The epidemic of infectious diseases such as COVID-19 is shifting digital-centered non-face-to-face business from economic operation, and the use of AI and big data technology for personalized services is essential to spread online. In this paper, we analyze cases focusing on the application of artificial intelligence technology, which is a key technology for the effective implementation of the digital new deal promoted by the government, as well as the major technological characteristics of the 4th industrial revolution and describe the use cases in the field of disaster response. As a disaster response use case, AI assistants suggest appropriate countermeasures according to the status of the reporter in an emergency call. To this end, AI assistants provide speech recognition data-based analysis and disaster classification of converted text for adaptive response.