• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.2
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• pp.197-203
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• 2001

Micro-defects induced by design and production failure or working environments are known as the cause of SCC(Stress Corrosion Cracking) in aged structures. Therefore, the evaluation of structural integrity based on micro-cracks is required not only a manufacturing step but also in-service term. So we introduce a new nondestructive inspection method using the magneto-optical film to detect micro-cracks. The method has some advantage such as high testing speed, real time data acquistion and the possibility of remote sensing by using of a magneto-optical film that takes advantage of the change of magnetic domains and domain walls. This paper introduces the concept of the new nondestructive inspection method using the magneto-optical film, also proves the possibility of this method as a remote testing system under oscillating load considering application on real fields by applying the method to four types of specimens.

• Journal of the Korean Magnetics Society
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• v.6 no.6
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• pp.367-374
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• 1996

The developmement of electronic machine industries requires miniature of size as well as increasement of driving frequency in electronic parts, recently. To realize micro-struture of magnetic devices, in this study, we fabricated thin film inductors by using thin film manufacturing techniques such as photolithography and wet etching process, and these devices are measured at high frequency range of 1 MHz~1 GHz. The results are as follows. The accurate measuring technique by using network analyzer system having microstrip line was established. The manufactured inductors are fabricated with several ten micrometers by means of wet etching process known as easier and more economic than dry etching process. VVhen the device size of two types (spiral, meander) is the same, inductance value L and quality factor Q of spiral type devices are larger than those of meander type, but driving frequency of spiral type is lower than that of meander type due to increasement of inductance L. It is necessary to decrease resistance value R by increasing cross section of the conductor film coil. Thus high frequency measuring method would be a very useful for another measuring fields of the range over several hundreds MHz.

• Smart Media Journal
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• v.5 no.3
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• pp.42-48
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• 2016

Recently, most of the modern people used a second mobile device(two degree mobile device). Mobile devices are affecting all areas of human life, consumer electronics, transportation, manufacturing, and finance. On this paper, we propose a model-driven development based interaction model that can be used in the development of mobile payment system, which is the latest buzzword pins of the various application fields of mobile devices Tech (Fin-Tech) sector. Using a model-driven development based models do not depend on the Platforms (PIM), we propose a model for interaction between devices which can be reused when developing mobile billing app. A model-driven development based mobile applications use the reusable of interaction models development program analyzed the bus fees automatic payment application by a beacon.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.130-138
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• 2020

In this paper, a plan to expand the scale of the domestic MRO industry was proposed by finding the technical common points between the aviation MRO and naval vessel MRO industries. The aviation MRO industry is led by Europe, North America, and Singapore. Europe and North America have very large aviation industries. The reason for the development of the MRO industry in Singapore is that the aviation MRO and ship MRO industries gathered to expand the industrial scale. The MRO field is an industry that spans all fields from research & development, production, manufacturing, operation, disposal, and crew training. The MRO industry is divided into military and civilian use. However, most of them are only differences in the needs of users, and there are no significant technical differences. The weapon system used by the military is steadily developing. It is impossible for the military to maintain all equipment at a time when troops are reduced. For that reason, it is necessary to share roles in each field. There is a need for an MRO industry in which civil and military operations cooperate to maintain all weapon systems at optimal performance. And the MRO industry development should be based on the civil market. The scale of the MRO industry should be expanded by gathering equipment commonly used in aircraft and naval vessels. This can increase military availability and reduce maintenance budgets.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.155-166
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• 2021

Recently, digital health care technology is spreading and developing in various fields. Therefore, in this paper, we realized that the field to which digital healthcare technology is not applied is the field of optometry, and implemented a digital healthcare optometry system for precise lens manufacturing. A device called Phoroptor is used to manufacture the lens, and this device sets the lens by measuring the visual acuity of the person who requested the glasses. And when the person to be measured wears glasses, a device called a PD meter is used to align the pupil center and lens focus. However, there is a limit to the convenience of precise lens production and optometry due to the absence of a database and program that can accumulate and analyze the PD measurement error, inconvenience and error due to manual control of the Phoroptor, and optometric information. Therefore, in this paper, PD meter design for more accurate PD measurement, Phoroptor design and Phoroptor control application design for automatic Phoroptor control, and a database and analysis program that automatically set lenses using optometry information for each subject had been designed. Based on this, ultimately, a digital healthcare optometry system using an optometry database has been implemented.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.113-119
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• 2003

Since Lean Construction has been introduced as a new management approach to improve productivity in the construction industry, much research is in progress to develop lean concepts and principles for better implementation and to get results of the successful adaptation of lean ideas from manufacturing for application in the construction industry. Currently, several construction companies in the USA have applied the Last Planner System (LPS), a decentralized system developed by the Lean Construction Institute. Thus, there are demands to share information how other companies implement lean construction, to identify the benefits and barriers of lean implementation in the construction fields, and finally to improve their lean implementation. This study carried out case studies to assess current lean construction projects with the objective to find out how effectively and to what extent lean construction is being adapted by the construction industry. This study will only introduce the Last Planner which has four levels in the LPDS, findings based on interviews with project participants, and observations from the projects. This study will also provide empirically identified success factors associated with lean implementation on the construction site. Finally, the recommendations are offered to support the effort of adaptation of lean construction in the domestic construction industry. Even though lean construction still stood on the bridge crossing from current practice to lean practice, it is the researcher's conviction that lean construction would be successfully adapted to the construction industry in the near future and would be recognized as an effective management innovation.

• 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.

• Clean Technology
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• v.26 no.3
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• pp.228-236
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• 2020

Korea has an economy based on manufacturing industrial fields, which produce high amounts of hazardous wastes, in spite of few landfill candidates, and a significant concern for fine airborne particulates; therefore, traditional waste management is difficult to apply in this country. Moreover, waste collection and accumulation have recently been intensified by the waste import prohibitions or regulations in developing nations, the universalization of delivery services in Korea, and the global COVID-19 crisis. This study thus presents a domestic waste management strategy that aims to address the recent issues on waste. The contents of the strategy as the main results of the study include the (1) improvement of the compatibility of the classification codes between the domestic hazardous waste and the international ones such as those of the Basel Convention; (2) consideration of the mixed hazard indices to represent toxicity from low-content components such as rare earth metals often contained in electrical and electronic equipment waste; (3) management application based on risks throughout the life cycles of waste; (4) establishment of detailed material flow information of waste by integrating the Albaro system, Pollutant Release and Transfer Register (PRTR) system, and online trade databases; (5) real-time monitoring and prediction of the waste movement or discharge using positional sensors and geographic information systems, among others; and (6) selection and implementation of optimal treatment or recycling practices through Life Cycle Assessment (LCA) and clean technologies.