• Fashion & Textile Research Journal
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• v.25 no.3
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• pp.304-314
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• 2023

The rapid development of digital technology is diversifying the fashion industry by influencing both the manufacturing processes and the characteristics of fashion products. Although various smart clothing technologies are being developed as part of the government's technology development policy, the number of smart clothing products available to consumers in stores remains very limited. To address this issue, this study analyzes the key attributes of smart clothing as expressed in consumer language. The CIT (Critical Incident Technique) research method was used, and data were collected through an online survey. The study focuses on identifying potential factors that may influence the development direction or strategy of smart clothing. By classifying past experiences and attitudes towards smart clothing into positive and negative categories, it was found that positive responses to smart clothing were heavily influenced by expectations from technology and convenience. Participants' experience with smart technology has had a positive impact on their evaluation of smart clothing. Consumers with negative attitudes towards smart clothing expressed expectations for new benefits resulting from technological development, and indicated that they would consider purchasing such clothing in the future when design and technology improve. Ultimately, this study provides a valuable reference for the development of smart clothing products in Korea by analyzing consumer experiences and acceptance conditions towards smart clothing.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.527-532
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• 2001

In this paper, we will propose the new method that estimates the sensing ability of HH smart sensor. We have developed a new signal processing method that can distinguish among different materials relatively. The HH smart sensor was developed for recognition of materials. We made the HH smart sensor in our experiment. Then, we estimated the ability to recognize objects according to acceleration value. We estimated the sensing ability of HH smart sensor with the $R_{SAI}$ method. Experiments and analysis were executed to estimate the ability to recognize objects according to acceleration value changing. Dynamic characteristics of HH smart sensor were evaluated relatively through a new $R_{SAI}$ method that uses the power spectrum density. Applications of this method are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.11-26
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• 2018

Purpose: This study is to help the construction of smart factories of other manufacturing enterprises through IDIS 's case of smart factory construction. Methods: We introduce the four phases of implementing smart factory building by IDIS company, which produces a small quantity of multi-odd units. Results: Through the smart factory construction, the cost of product is reduced due to the improvement of total productivity such as office work, production work, and energy saving, and sales are enhanced by customized production, quality / delivery reliability improvement. Conclusion: We present the actual examples needed to build the manufacturer's smart factory.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.922-926
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• 2001

This paper deals with sensing ability of smart sensor that has a sensing ability to distinguish materials according to frequency changing. We have developed a new signal processing method that can distinguish among different materials. The smart sensor was developed for recognition of materials. We estimated the sensing ability of smart sensor with the $R_{SAI}$ method according to frequency changing. Experiments and analysis were executed to estimate the ability to recognize objects according to frequency changing. Sensing ability of smart sensors was evaluated relatively through a new $R_{SAI}$ method. Applications of smart sensors are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.etc.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1335-1348
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• 2016

Smart factory of Manufacturing sector is rapidly spreading, globally. In case of domestic, it is on going based on KOSF. It is neither lack of invest nor security of information due to it has been spread from manufacturing sector. Hence, that's very difficult to efficiency prevent from new type of intimidation and security accident happened sometimes from this situation. According to research information security condition with recognized new menace, there is a most efficient way is provide education of information security without any extra budget to safely spread smart factory. Thus, this study of research has developed security awareness training curriculum from international standard, requirement of the industry, and curriculum of educational institution based on NCS (National Competency Standard). It is be very helpful to spread smart factory safely due to expert group has been test of validity.

• Korean small business review
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• v.41 no.4
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• pp.1-36
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• 2019

The purpose of this study is to identify the relationships among purposes and contents of smart factory building and continuous utilization of smart factory. Specifically, this study identifies two types of purposes of smart factory building as follows: (1) improving productivity, (2) increasing flexibility. In this study, three aspects of smart factory building contents were suggested like this: (1) automation area (facility automation vs. work automation), (2) big data system focus (radical transformation vs. incremental improvement), and (3) value chain integration area (internal value chain integration vs. external value chain integration). In addition, we looked at how firm size moderates the purposes - contents - continuous utilization of smart factory relationship. A questionnaire survey was conducted on 151 manufacturing companies. More specifically, out of 151 companies, 100 are small-and-medium-sized enterprises and 51 large-sized enterprises. All questionnaires were targeted at companies with Smart Factory level above level 2. The analysis results of this study using Smart PLS statistical programs are as follows. First, the purposes of smart factory building including increasing productivity and flexibility had positive impacts on all of the contents of smart factory building. Second, all of smart factory building contents had positive impacts on the continuous use of smart factory except big data system for incremental improvement of manufacturing process. Third, the impacts of smart factory building purposes implementation on smart factory building contents varied depending on whether the purpose is productivity improvement or flexibility. Fourth, it was founded that firm size moderated the relationships of purposes - contents - continuous utilization of smart factory in such a way that large-sized firms tend to empathize the link between flexibility and smart factory building contents for continuous use of smart factory, while small-and-medium-sized-firms emphasizing the link between productivity and smart factory building contents. Most of the previous studies have focused on presenting current smart factory deployment cases. However, it is believed that this research has made a theoretical contribution in this field in that it established and verified a research model for the smart factory building strategy. Based on the findings from a working-level perspective, corporate practitioners also need to have a different approach to smart factory building, which should be emphasized depending on whether their purpose of building smart factory is to increase productivity or flexibility. In particular, since the results of this study identify the moderating effect of firm size, it is deemed necessary for firms to implement a smart factory building strategy suitable for their firm size.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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• pp.99-106
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• 2006

In this paper, the new method that estimates a sensing ability of smart sensor will be proposed. A study is estimation method that evaluates sensing ability about smart sensor respectively. According to acceleration(g) and displacement changing, we estimated sensing ability of smart sensor using SAI(Sensing Ability Index) method respectively. Smart sensors was made fer experiment. The types of smart sensor are two types(hard and soft smart sensor). Smart sensors developed for recognition of material. Experiment and analysis are executed for estimate the SAI method. In develop a smart sensor, the SAI method will be useful for finding optical design condition of smart sensor that can sense a material. And then dynamic characteristics of smart sensors(frequency changing, acceleration changing, critical point, etc.) are evaluated respectively through new method(SAI) that use the power spectrum density. Dynamic characteristic of sensor is evaluated with SAI method relatively. We can use the SAI for finding critical point of smart sensor, too.

• Electronics and Telecommunications Trends
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• v.35 no.2
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• pp.28-37
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• 2020

Recently, the focus of research interest in artificial intelligence technology has shifted from algorithm development to application domains. Industrial sectors such as smart manufacturing, transportation, and logistics venture beyond automation to pursue digitalization of sites for intelligence. For example, smart manufacturing is realized by connecting manufacturing sites, autonomous reconfiguration, and optimization of manufacturing systems according to customer requirements to respond promptly to market needs. Currently, KSB Convergence Research Department is developing BeeAI-an on-site end-to-end intelligence platform. BeeAI offers end-to-end service pipeline configuration and DevOps technologies that can produce and provide intelligence services needed on-site. We are hopeful that in future, the BeeAI technology will become the base technology at various sites that require automation and intelligence.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.89-92
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• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.

• Advances in nano research
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• v.17 no.2
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• pp.109-124
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• 2024

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.