• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.69-77
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• 1999

One of the objectives of any task design is to provide a safe and helpful workplace for the employees. The safety and health module may include means for confronting the design with safety and health regulations and standards as well as tools for obstacles and collisions detection (such as error models and simulators), Virtual Reality is a leading edge technology which has only very recently become available on platforms and at prices accessible to the majority of simulation engineers. The design of an automated manufacturing system is a complicated, multidisciplinary task that requires involvement of several specialists. In this paper, a design procedure that facilitates the safety and ergonomic considerations of an automated manufacturing system are described. The procedure consists of the following major steps. Data collection and analysis of the data, creation of a three-dimensional simulation model of the work environment, simulation for safety analysis and risk assessment, development of safety solutions, selection of the preferred solutions, implementation of the selected solutions, reporting, and training. When improving the safety of an existing system the three-dimensional simulation model helps the designer to perceive the work from operators point of view objectively and safely without the exposure to hazards of the actual system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.101-104
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• 2021

본 논문에서는 시뮬레이션을 이용하여 대부분의 공정이 수작업으로 이루어지고 있는 수산가공 공장의 생산성 향상을 위한 제조데이터 활용 시스템을 제안한다. 제안된 내용은 플랜트 시뮬레이션을 이용하여 생산공정 모델링을 제작하고, 이로부터 가상의 제조데이터를 수집하여 생산량, 작업공정 시간 등 최적화된 공정 프로세스를 도출한다. 또한 제조데이터 수집 및 분석을 위하여 공장 내 수기로 작성되는 제조데이터를 정형화하여 제조데이터 플랫폼에 저장하고, 저장된 데이터의 시각화, 실시간 모니터링 등 데이터 시각화 및 시뮬레이션과 연동된 공정 프로세스 예측 등에 활용 가능할 것으로 기대된다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.143-146
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• 2005

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.219-226
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• 2018

IIoT(Industrial Internet of Thing) means that all manufacturing processes are informed beyond the conventional automation of process automation. The objective of the system is to build an information system based on the data collected from the sensors installed in each process and to maintain optimal productivity by managing and automating each process in real time. Data collected from sensors in each process is unstructured and many studies have been conducted to collect and process such unstructured data effectively. In this paper, we propose a system using Node-RED as middleware for effective big data collection and processing.

• IE interfaces
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• v.25 no.1
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• pp.13-20
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• 2012

Recently, the collaboration among small and medium-sized enterprises(SMEs) has been recognized as an effective competitive tool. As several systems have been developed to boost the collaboration, it is necessary to analyze the effectiveness of the systems in terms of their contribution to enhance operational performance of SMEs through objective and quantitative validation. In particular, the analysis for SMEs rather than large-scaled enterprises has not received much attention due to lack of relevant information and difficulty of collecting data. This paper presents a framework for analyzing the effectiveness of the collaboration support system, called i-manufacturing hub, which has been implemented by Korean government. Identification of influential factors to the effectiveness of collaboration hub, and constructing necessary hypotheses are proposed. To overcome the difficulty in data collection only by means of surveys through subjective questionnaires, we exploit system log data that are generated while SMEs use the system. As an initial phase to analyze the effectiveness through hypothesis validation, we discuss several interesting observations and challenges in the direction of enhancing collaboration among SMEs for better operational performance improvement and more participation in the collaboration hub.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Asia Pacific Journal of Business Review
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• v.6 no.2
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• pp.27-60
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• 2022

Previous studies stated that low carbon innovation performance could be influenced by government regulations and the green market, which is the new trend of consumer consumption in the present time, mainly focusing on external factors. Before study augured that low carbon innovation performance could be driven by internal and external factors of cooperation such as institutional pressure, stakeholder pressure, and innovation resources. However, the study of green transformational leadership and organizational environmental culture on low carbon innovation performance is rare, especially in Chinese manufacturing, as well as the effect of influencing factors of TPB model: environmental attitude, subjective norm, and perceived behavior capability on low carbon innovation performance. Previous studies mostly used the TPB model for predicting individual behavior. This study established a theoretical model combining the TPB model with green transformational leadership and organizational environmental culture of Chinese automobile manufacturing on low carbon innovation performance. This study consists of two sections of research methodology: section 1 related to questionnaire design and data collection. We established a questionnaire and distributed it online, targeting responses from the managerial level working in Chinese automobile manufacturing. Eventually, 155 valid questionnaires were used for analysis. Section 2 involved data analysis using statistical software. Reliability and data validity was examined by reliability analysis and factor analysis. Correlations and convergent validity analyses were applied, and structural equation modeling was conducted to test the proposed hypotheses. The findings indicated that green transformational leadership, organizational environmental culture, and essential factors of TPB model; environmental attitude, subjective norm and perceived behavior capability positively affect low carbon innovation performance. In addition, the indirect effect of green transformational leadership was tested and found that organizational environmental culture and TPB factors mediated the relationship between transformational leadership and low carbon innovation performance.

• Journal of the Korean Society of Costume
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• v.62 no.4
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• pp.149-163
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• 2012

This study reviewed current facts on overseas manufacturing factories of garment vendors that were launched in the countries that have a great influence of the Korean Wave, and investigated the influence of Korean Wave in its sourcing area. By doing so, this study aims to present basic data in order to help fabric and garment vendors to enter into the fashion markets of different countries through a local network. For data collection and analysis, Windows SPSS 19.0 was used for frequency analysis of the facts and figures of the local manufacturing factories. In-depth interviews regarding the current facts on local manufacturing factories and the influence of Korean Wave were conducted with 16 Korean garment manufacturing factories in Vietnam and 9 in Indonesia among the overseas garment companies that were registered in the Korean Apparel Industry Association. Through the interview, it was found that new companies should investigate custom tariffs, salary level of the local employees, and infrastructure prior to launching above all. Also, as a result of analyzing competitors and competitive advantages, good treatment of local employees and a good labor environment were noted the most. As for the influence of the Korean Wave, the image of Korea was positive and favorable, but it did not directly affect the preference for Korean companies. After investigating the obstacles that prevented the entrance into local markets, it was found that the rise in the salary level was the biggest hindrance.

• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.563-575
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• 2022

Many organizations seek statistical modelling facilitated by data analytics technologies for determining the prediction models associated with M&A (Merger and Acquisition). By combining these data analytics tool alongside with data collection approaches aids organizations towards M&A decision making, followed by achieving profitable insights as well. It promotes for better visibility, overall improvements and effective negotiation strategies for post-M&A integration. This paper explores on the impact of pre and post integration of M&A in a standard organizational setting via devising a suitable statistical model via employing techniques such as Naïve Bayes, K-nearest neighbour (KNN), and Decision Tree & Support Vector Machine (SVM).

• Journal of Information Technology Services
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• v.21 no.2
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• pp.97-107
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• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.