• 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.8 no.5
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• pp.496-510
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• 2016

Simulation technology is a type of shipbuilding product lifecycle management solution used to support production planning or decision-making. Normally, most shipbuilding processes are consisted of job shop production, and the modeling and simulation require professional skills and experience on shipbuilding. For these reasons, many shipbuilding companies have difficulties adapting simulation systems, regardless of the necessity for the technology. In this paper, the data model for shipyard production simulation model generation was defined by analyzing the iterative simulation modeling procedure. The shipyard production simulation data model defined in this study contains the information necessary for the conventional simulation modeling procedure and can serve as a basis for simulation model generation. The efficacy of the developed system was validated by applying it to the simulation model generation of the panel block production line. By implementing the initial simulation model generation process, which was performed in the past with a simulation modeler, the proposed system substantially reduced the modeling time. In addition, by reducing the difficulties posed by different modeler-dependent generation methods, the proposed system makes the standardization of the simulation model quality possible.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.615-616
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• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.453-456
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• 2022

The role of the power grid is to ensure stable power supply. It is necessary to take various measures to prepare for unstable situations without notice. After identifying the relationship between features through exploratory data analysis using weather data, a machine learning based energy production prediction model is modeled. In this study, the prediction reliability was increased by extracting the features that affect energy production prediction using principal component analysis and then applying it to the machine learning model. By using the proposed model to predict the production energy for a specific period and compare it with the actual production value at that time, the performance of the energy production prediction applying the principal component analysis was confirmed.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.63-76
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• 2006

Marine production forecasting in fisheries is a crucial factor for managing and maintaining fishery resources. Thus this paper aims to generate a forecasting model of total marine production. The most generally method of time series model is to generate the most optimal single forecasting model. But the method could induce a different forecasting results when it does not properly infer a model To overcome the defect, I am trying to propose a single forecasting through multiple time series model. In other word, by comparing and integrating the output resulted from ARIMA and VAR model (which are typical method in a forecasting methodology), I tried to draw a forecasting. It is expected to produce more stable and delicate forecasting prospect than a single model. Through this, I generated 3 models on a yearly and monthly data basis and then here I present a forecasting from 2006 to 2010 through comparing and integrating 3 models. In conclusion, marine production is expected to show a decreasing tendency for the coming years.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.375-380
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• 2003

This study is concerned with the machine part grouping m cellular manufacturing. To group machines into the set of machine cells and parts into the set of part families, new p-median model considering the production data such as the operation sequences and production volumes for parts is proposed. Unlike existing p-median models relying on the classical binary part-machine incidence matrix which does not reflect the real production factors which seriously impact on machine-part grouping, the proposed p-median model reflects the production factors by adopting the new similarity coefficient based on the production data-based part-machine incidence matrix of which each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. Computation test compares the proposed p median model favorably.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.8
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• pp.1205-1215
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• 2002

The purpose of this study was to develop a production line using simulation method, which could improve work allocation, labor utility and productivity. Using simulation software AIM, a simulation model of functional sports wear assembly line was developed. A functional sports wear production factory were analysed to gather data necessary for this research. Factory layouts, production facilities, work time of each unit jobs were investigated. The data obtained were used as to build a base simulation model. Then, the base simulation model was verified using the obtained data, such as daily productivity. Using simulation method, low alternative production plans were suggested, which were to enhance productivity, and work efficiency and to reduce queue length and throughput time.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.1
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• pp.304-320
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• 1996

A multiple production .epsilon. equation model was developed in the low Reynolds number $\kappa$-$\varepsilon$ model with the aids of DNS data. We derived the model theoretically and avoided the use of empirical correlations as much as possible in order for the model to have generality in the prediction of complex turbulent flow. Unavoidable model constants were, however, optimized with the aids of DNS data. All the production and dissipation models in the $\varepsilon$ equation were modified with damping functions to satisfy the wall limiting behavior. A new $f_{\mu}$ function, turbulent diffusion and pressure diffusion model for the k and .epsilon. equations were also proposed to satisfy the wall limiting behavior. By, computational investigation on the plane channel flows, we found that the multiple production model for .epsilon. equation could improve the near wall turbulence behavior compared with the standard production model without the complicated empirical modification. Satisfication of the wall limiting conditions for each turbulence model term was found to be most important for the accurate prediction of near wall turbulence behaviors.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.166-167
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• 2016

Unlike the past, supported by the development of digital technologies, free-form buildings are frequently designed with creative thoughts of architectural designers. However, there are some difficulties preventing successfully completion of projects, like reduced productivity and increased construction duration and cost upon the process of producing and installing concrete panels for free-form structures. In particular, there are active studies on the CNC machine for production of free-form concrete panels. Yet, it is difficult to effectively and easily come up with information on production and installation of free-form, curve-surfaced panels which are difficult to be mathematically defined. This requires a lot of manpower and time to implement the curved surfaces of free-form buildings as intended by architects. Accordingly, it needs a model that can effectively create production-installation data of free-form concrete panels for successful free-form building projects. Thus, the purpose of the study is to suggest data estimation model of production-installation using mathematical algorithm in free-form concrete panels. The study results will realize effective production and installation of free-form concrete members, allowing improved productivity of projects, reduced cost and shortened construction duration.