• Journal of the Korea Institute of Building Construction
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• v.5 no.4 s.18
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• pp.153-164
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• 2005

This study aims at establishing JIT production management system to enable manage the resources input into from procurement through construction based on correct identification of the process, an analysis on the amount of input materials and information sharing. This study has focused on the process control and working process of rebar work in domestic apartment house construction where the overall scope of Process from the planning phase to the construction phase has been analyzed in this study. Also construction phase was selected for the application of a sample case. A basic model for JIT production was generated with these processes. Furthermore A questionnaire and the on-site survey with process, checklist and control data were prepared and performed for the application of JIT production management model into rebar work. The governing scopes of JIT production management system include process management, material management, yard loading and moving management and inventory control, and the operation of each control item

• Fisheries and Aquatic Sciences
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• v.24 no.4
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• pp.139-152
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• 2021

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.224-228
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• 2000

The piercing and blanking of thin sheet metal working is specified division in press die design and making. In order to prevent the detects, the optimum design of the production part, strip process layout, die design, die making and try out etc. re necessary the analysis of effective factors. For example, theory and practice of metal shearing process and its phenomena, die structure, machine tool working for die making, die materials and its heat treatment, metal working in industrial and its know how etc. In this study, we analyzed whole of data base, theoretical back ground of metal working process, and then performed the progressive die tryout with the screw press. This study regards to the aim of small quantity of production part's press working. Part 1 of this study reveals with production part and strip process layout design.

• The Journal of Information Systems
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• v.26 no.2
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• pp.105-121
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• 2017

Purpose It is a major goal to improve the product yields during production operations in the manufacturing industry. Therefore, factory is trying to keep the good quality materials and proper production resources, also find the proper condition of facilities and manufacturing environment for yields improvement. Design/methodology/approach We propose the hybrid framework to analyze to dataset extracted from MES. Those data is about the alarm information generated from equipment, both measurement and equipment process value from production and cycle/pitch time measured from production data these covered products during production. We adapt a data warehousing techniques for organizing dataset, a logistic regression for finding out the significant factors, and a association analysis for drawing the rules which affect the product yields. And then we validate the framework by applying the real data generated from the discrete process in secondary cell battery manufacturing. Findings This paper deals with challenges to apply the full potential of modeling and simulation within CPPS(Cyber-Physical Production System) and Smart Factory implementation. The framework is being applied in one of the most advanced and complex industrial sectors like semiconductor, display, and automotive industry.

• Journal of Korean Society of Rural Planning
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• v.25 no.3
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• pp.67-75
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• 2019

Agricultural production facilities that have been established to support improving food production, farm income, and reduction of farming time have remarkable achievements as value-neutral devices or infrastructures, but recently they are pointed out as a factor that hinders landscape by changing the contextual values of rural area. Despite this timelessness, research on the landscape design of agricultural production facilities has not been conducted until now. Based on these research necessities, this study aims to improve the process of reviewing the landscape of agricultural production facilities by analyzing the impact of activities, policies, plans. The results of this study are as follows. First, the analysis of the literature and the related data were carried out. This presents the structural limitations of why landscape review is difficult in the process of reviewing plans and the limitations of current landscape laws, deliberations, and plans. The process of reviewing the plan has formed a functionally oriented closed network, and the government policy does not properly control the landscape design of agricultural production facilities. From the viewpoint of the study, results can be used as basic data for the study of the lack of agricultural production facilities and landscape.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• Environmental Engineering Research
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• v.24 no.4
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• pp.559-565
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• 2019

In order to alleviate the environmental pressure caused by production process of thermal power plants, the application of cleaner production is imperative. To estimate the implementation effects of cleaner production in thermal plants and optimize the strategy duly, it is of great significance to take a comprehensive evaluation for sustainable development. In this paper, a hybrid model that integrated the analytic hierarchy process (AHP) with least squares support vector machine (LSSVM) algorithm optimized by grid search (GS) algorithm is proposed. Based on the establishment of the evaluation index system, AHP is employed to pre-process the data and GS is introduced to optimize the parameters in LSSVM, which can avoid the randomness and inaccuracy of parameters' setting. The results demonstrate that the combined model is able to be employed in the comprehensive evaluation of the cleaner production in the thermal power plants.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.150-156
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• 2022

Recently, the use of Unreal Engine for animation production is increasing. In this situation, Unreal Engine's 'MetaHuman Creator' helps make it easier to apply realistic characters to animation. In this regard, we tried to produce animations using 'MetaHuman' and verify the effectiveness and differences from the animation production process using only Maya software. To increase the efficiency of the production process, the animation process was made with Maya software. We tried to import animation data from Unreal Engine and go through the process of making animations, and try to find out if there are any problems. And we tried to compare animations made with realistic 'MetaHuman' characters and animation works using cartoon-type characters. The use of the same camera lens in realistic character animations and cartoon character animations produced based on the same scenario was judged to be the cause of the lack of realistic animation screen composition. The analysis revealed that a different approach from the existing animation camera lens selection is required for the selection of the camera lens in the production of realistic animation.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.148-154
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• 2021

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.