• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.18-26
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• 2008

In this paper, dynamic GTS scheduling method based on IEEE 802.15.4 is proposed for wireless control system considering reliability and real-time property. The proposed methods can guarantee a transmission of real-time periodic and sporadic data within the limited time frame in factory environment. The superframe of IEEE 802.15.4 is used for the dynamic transmission method of real-time mixed traffic (periodic data, sporadic data, and non real-time message). By separating CFP and CAP properly, the periodic, sporadic, and non real-time messages are transmitted effectively and guarantee real-time transmission within a deadline. The simulation results show the improvement of real-time performance of periodic and sporadic data at the same time.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.427-434
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• 2015

The objective of this study is to establish an rational mixture-proportioning procedure for low-$CO_2$ concrete using supplementary cementitious materials (SCMs) achieving the targeted $CO_2$ reduction ratio as well as the conventional requirements such as initial slump, air content, and 28-day compressive strength of concrete. To evaluate the effect of SCM level on the $CO_2$ emission and compressive strength of concrete, a total of 12537 data sets were compiled from the available literature and ready-mixed concrete plants. The amount of $CO_2$ emission of concrete was assessed under the system boundary from cradle to concrete production stage at a ready-mixed concrete plant. Based on regression analysis using the established database, simple equations were proposed to determine the mixture proportions of concrete such as the type and level of SCMs, water-to-binder ratio, and fine aggregate-to-total aggregate ratio. Furthermore, the $CO_2$ emissions for a given concrete mixture can be straightforwardly calculated using the proposed equations. Overall, the developed mixture-proportioning procedure is practically useful for determining the initial mixture proportions of low-$CO_2$ concrete in the ready-mixed concrete field.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2003.11a
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• pp.217-229
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• 2003

Analysis of the elements affecting short circulation in highly integrated paper mills and identification of the interactions among these elements are very important tasks to prevent operational perturbations such as web breaks. In the present work a dynamic model for the short circulation is developed to analyze tuning methods for the outputs to follow set points during grade change operations. Steady state operation data are used to investigate dynamic characteristics of responses for input changes.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.137-142
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• 2006

제철소 후판공장에서는 두꺼운 슬라브(Slab)를 압연하여 사각형태의 철판인 날판(Mother Plate)을 생산하고, 이를 주문(Plate) 사이즈에 맞게 다시 절단을 하게 된다. 이때 동일 슬라브라 하더라도 압연방법에 따라 다양한 사이즈의 날판을 생산할 수 있다. 여기에서 다루고 있는 후판 날판설계 문제는 주어진 주문을 대상으로 최소 개수의 슬라브를 사용하여 생산하는 문제를 말한다. 이를 위해 최적의 날판 사이즈를 결정하고, 각 날판에 주문들을 배치하게 된다. 본 논문에서는 후판 날판설계문제를 two-stage guillotine cutting problem의 변이로 모델을 세우고, 이를 위한 효율적인 휴리스틱을 제시하였다. 그리고 실 데이터를 대상으로 컴퓨터 실험을 통해 휴리스틱을 효율성을 검정하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.620-623
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• 2002

To cope with the challenge of competitive market, manufacturing system needs to be agile in terms of its reconfigurability and scalability. For a system to be adapted to changed requirements, decision support tools such as configurator have to be provided. This paper introduces the basic framework of a configurator for assembly system Based on the factory data model(FDM) depicting the overall structure of a manufacturing system, functions of the configurator are described, i.e., requirements analysis, module selection and configuration optimization.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.17-18
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• 2017

본 논문은 스마트 팩토리의 중요 요소인 무인반송차(AGV)를 자율 주행시키기 위해 오픈 소스 자율 주행차 시뮬레이터인 udacity를 이용해 머신 러닝시키는 시스템을 개발하였다. 공장의 운행 루트를 자율주행 시뮬레이터의 전경으로 가공하고, 3개의 카메라를 부착시킨 AGV를 운행시키면서 머신 러닝시킨다. AGV를 주행하여 얻어진 여러 학습 데이터를 통해 도출된 결과들을 각각 비교하여 우수한 모델을 선정하고 운행시킨 결과 AGV가 정해진 운행 루트를 정확하게 주행하는 것을 확인하였다. 이를 통해, 가상 운행 환경에서 저비용으로 AGV 운행 학습이 가능하다는 것을 보였다.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.9
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• pp.644-655
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• 2011

VOCs (Volatile Organic Compounds) have adverse effects on human health and have caused serious global air pollution problems such as ozone depletion and cimate changes. The total of 56 target VOCs were selected to be monitored in this study for 4 years (2006~2009). The VOCs were measured every hour. The concentration of BTEX was higher than the other target compounds. Generally, the levels of VOCs measured in this study were higher than those measured by the other studies because Gamjeon and Jangrim monitering sites are located in industrial areas. The seasonal variations showed that the VOCs were the highest in winter. The temporal variations showed that the VOCs were high during commuting time on weekday. PMF model was used to resolve source types and source contributions of VOCs in this study. Identified sources and quantified contributions resolved by PMF were vehicle exhaust (15.22%), thinning solvent (29.83%), surface coating (17.13%), industries (13.95%), LPG vehicle (15.22%), combustion boiler (7.11%) and biogenic source (6.61%). Thinning solvent and Surface coating were the most contributed sources possibly due to manufactures and automobile garages in Gamjeon and solvent and paint manufactures in Sasang-Gu.