• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.125-133
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• 2016

Data processing capacity and speed are rapidly increasing due to the development of hardware and software in recent time. As a result, data usage is geometrically increasing and the amount of data which computers have to process has already exceeded five-thousand transaction per second. That is, the importance of Big Data is due to its 'real-time' and this makes it possible to analyze all the data in order to obtain accurate data at right time under any circumstances. Moreover, there are many researches about this as construction of smart factory with the application of Big Data is expected to have reduction in development, production, and quality management cost. In this paper, system using In-Memory Data Grid for high speed processing is implemented in semiconductor process which numerous data occur and improved performance is proven with experiments. Implemented system is expected to be possible to apply on not only the semiconductor but also any fields using Big Data and further researches will be made for possible application on other fields.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.192-207
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• 2020

In industrial fields like big factories, efficient management of resources is critical in terms of time and expense. So, inefficient management of resources leads to additional costs. Nevertheless, in many cases, there is no proper system to manage resources. This study proposes a system to manage and track large-scale resources efficiently. We attached Bluetooth 5.0-based beacons to our target resources to track them in real time, and by saving their transportation data we can understand flows of resources. Also, we applied a diagonal survey method to estimate the location of beacons so we are able to build an efficient and accurate system. As a result, We achieve 47% more accurate results than traditional trilateration method.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.131-154
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• 2023

Purpose: The purpose of this paper is to design an integrated informatization system that can manage quality & KPI by integrating management systems in the aerospace and defense industry, and study the effect on KPI when applied to related companies. Methods: The 7 management systems required for integration in the AS&D industry were studied, and an empirical analysis was conducted for H company in South Korea for the application of e-QMS integrated informatization & KPI system based on security environment and open quality. Results: The results of this study were analyzed to have an effect on the improvement of customer satisfaction and the positive improvement of quality failure cost in the aerospace and defense industry. And it was analyzed that it works to continuously comply with ethical management and environmental laws and prevent safety accidents. Conclusion: The greatest significance of this study is that it attempted to build an e-QMS integrated system in the aerospace and defense industry. Considering that the case of integrated management system and integrated operation of KPI in related industries has not been introduced in the existing literature, the results of this study will be shared as a meaningful preceding study in the era of digital quality information. In addition, the fact that the open-quality quality innovation methodology emphasizing measurement(M), tracking(T), and connection(C) was actually applied in an AS&D company and its effectiveness was objectively proven. It is expected that it will be a good paper for follow-up research.

• 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 Internet Computing and Services
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• v.19 no.1
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• pp.87-95
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• 2018

Due to global warming and increased energy costs around the world, interests of energy saving and efficiency have been increased. In particular, forging factories need methods to save energy and increase productivity because of needing amounts of energy uses. To solve the problem, we propose a system, which includes collection, monitoring, and analysis process, to monitor energy uses each facility in realtime based on the IoT devices. This system insists of worksheets management, facility/energy management, realtime monitoring, history search, data analysis through connecting with existed ERP/MES Systems in manufacturing factories. The energy monitoring process is to present used energy collected from IoT devices connected with installed gasmeter and wattmeter each facility. This system provide the change of energy uses, usage fee, energy conversion, and green gas information in realtime on Web and mobile devices. This system will be enhanced with energy saving technology by analyzing constructed big data of energy uses. We can also propose a method to increase productivity by integrating this system with functions of digitalized worksheets and optimized models for production process.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.739-747
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• 2018

With a large feature space data, feature selection has become an extremely important procedure in the Data Mining process. But the traditional feature selection methods with single process may no longer fit for this procedure. In this paper, we proposed a hybrid efficient feature selection model for high dimensional data. We have applied our model on KNHNAES data set, the result shows that our model outperforms many existing methods in terms of accuracy over than at least 5%.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.109-120
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• 2020

The supply chain without digitization is just a series of discrete, siloed steps taken through marketing, product development, manufacturing, and logistics, and finally into the hands of the customer. Digitization brings down those walls, and the chain becomes a completely integrated network fully transparent to all the parties involved. The ulitimate goals of digitizatized supply chain management are velocity and visibility. This network will depend on a number of key technologies including integrated planning and execution systems, supply chain analytics, autonomous logistics, smart warehousing and factory, etc, enabling companies to react to disruptions in the supply chain, and even anticipate them, by fully modeling the network, creating "what-if" scenarios, and adjusting the supply chain in real time as conditions change. This paper presents a number of studies on digitalization of supply chains and provides a discussion on issues raised in the process of technology adoption. Implications of the study findings are also provided.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.147-155
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• 2016

Industry 4.0's goal is the 'Smart Factory' that integrates and controls production process, procurement, distribution and service based on the fundamental technology such as internet of the things, cyber physical system, sensor, etc. Basic requirement for successful promotion of this Industry 4.0 is the large supply of semiconductor. However, company I who produces dicing blades has difficulty to meet the increasing demand and has hard time to increase revenue because its raw material includes high price diamond, and requires very complex and sensitive process for production. Therefore, this study is focused on understanding the problems and presenting optimal plan to increase productivity of dicing blade manufacturing processes. We carried out a study as follows to accomplish the above purposes. First, previous researches were investigated. Second, the bottlenecks in manufacturing processes were identified using simulation tool (Arena 14.3). Third, we calculate investment amount according to added equipments purchase and perform economic analysis according to cost and sales increase. Finally, we derive optimum plan for productivity improvement and analyze its expected effect. To summarize these results as follows : First, daily average blade production volume can be increased two times from 60 ea. to 120 ea. by performing mixing job in the day before. Second, work flow can be smoother due to reduced waiting time if more machines are added to improve setting process. It was found that average waiting time of 23 minutes can be reduced to around 9 minutes from current process. Third, it was found through simulation that the whole processing line can compose smoother production line by performing mixing process in advance, and add setting and sintering machines. In the course of this study, it was found that adding more machines to reduce waiting time is not the best alternative.

• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.1-11
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• 2018

Discrete event system specification (DEVS) has been used in many simulations including hybrid systems featuring both discrete and continuous behavior that require a lot of time to get results. Therefore, in this study, we proposed the acceleration of a DEVS-based hybrid system simulation using multi-cores and GPUs tightly coupled computing. We analyzed the proposed heterogeneous computing of the simulation in terms of the configuration of the target device, changing simulation parameters, and power consumption for efficient simulation. The result revealed that the proposed architecture offers an advantage for high-performance simulation in terms of execution time, although more power consumption is required. With these results, we discovered that our approach is applicable in hybrid system simulation, and we demonstrated the possibility of optimized hardware distribution in terms of power consumption versus execution time via experiments in the proposed architecture.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.47-52
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• 2021

Recently, demand for artificial intelligence solutions for production process management has been increasing in the manufacturing industry. However, through the application of AI solutions in the manufacturing industry, there are limitations to legacy smart factory solutions such as POP and MES.Therefore, in order to overcome this, this paper aims to improve production management efficiency by applying guidance, an artificial intelligence concept, to image recognition systems. In the system flow, As_is To be separated and actual work flow was applied, and the process was improved for overall productivity efficiency. The pre-processing plan for AI guidance learning was established and the relevant AI model was designed, developed, and simulated, resulting in a 97% recognition rate.