• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.46-46
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• 2017

We developed a rice simulation model for remote-sensing (SIMRIW-RS, Homma et al., 2007) to evaluate rice production and management on a regional scale. Here, we reports its application trial to estimate rice production in farmers' fields in Sendai, Japan. The remote-sensing data for the application was periodically obtained by multispectral camera (RGB + NIR and RedEdge) attached with unmanned air vehicle (UAV). The airborne images was 8 cm in resolution which was attained by the flight at an altitude of 115 m. The remote-sensing data was relatively corresponded with leaf area index (LAI) of rice and its spatial and temporal variation, although the correspondences had some errors due to locational inaccuracy. Calibration of the simulation model depended on the first two remote-sensing data (obtained around one month after transplanting and panicle initiation) well predicted rice growth evaluated by the third remote-sensing data. The parameters obtained through the calibration may reflect soil fertility, and will be utilized for nutritional management. Although estimation accuracy has still needed to be improved, the rice yield was also well estimated. These results recommended further data accumulation and more accurate locational identification to improve the estimation accuracy.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.382-395
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• 2021

In this study, character recognition using deep learning is performed among the various defects in the PCB, the purpose of which is to check whether the printed characters are printed correctly on top of components, or the incorrect parts are attached. Generally, character recognition may be perceived as not a difficult problem when considering MNIST, but the printed letters on the PCB component data are difficult to collect, and have very high redundancy. So if a deep learning model is trained with original data without any preprocessing, it can lead to over fitting problems. Therefore, this study aims to reduce the redundancy to the smallest dataset that can represent large amounts of data collected in limited production sites, and to create datasets through data enhancement to train a flexible deep learning model can be used in various production sites. Moreover, ResNet model verifies to determine which combination of datasets is the most effective. This study discusses how to reduce and augment data that is constantly occurring in real PCB production lines, and discusses how to select coresets to learn and apply deep learning models in real sites.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.1 s.145
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• pp.103-118
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• 2006

At the initial design stage, the generation process of the production material information of a building block and the simulation process of the block erection, which are required to perform the production planning and scheduling, have been manually performed using 2D drawings, based on the data of parent ships, and subjective intuition from past experience. To make these processes automatic, the accurate generation method of the production material information and the convenient simulation method of the block erection based on the initial hull structural model(3D CAD model), were developed in this study. Here, the initial hull' structural model was generated from the initial hull structural CAD system early developed by us. To evaluate the developed methods. these methods were applied to corresponding processes of a deadweight 300,OOOton VLCC. As a result. it was shown that the production material information of a building block can be accurately generated and the block erection can be conveniently simulated in the initial design stage.

• Korean Journal of Organic Agriculture
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• v.25 no.4
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• pp.805-819
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• 2017

The study was carried out to develop a mathematical model for evaluating the effect of organic fertilizers in organic rice production systems. A function to simulate the nitrogen mineralization process in the paddy soil has been developed and integrated into ORYZA2000 crop growth model. Inorganic nitrogen in the soil was estimated by single exponential models, given temperature and C:N ratio of organic amendments. Data collected from the two-year field experiment were used to evaluate the performance of the model. The revised version of ORYZA2000 provided reasonable estimates of key variables for nitrogen dynamics and crop growth in the organic rice production systems. Coefficient of determination between the measured value and simulated value were 0.6613, 0.8938, and 0.8092, respectively for soil inorganic nitrogen, total dry matter production, and rice yield. This means that the model could be used to quantify nitrogen supplying capacity of organic fertilizers relative to chemical fertilizer. Nitrogen dynamics and rice growth simulated by the model would be useful information to make decision for organic fertilization in organic rice production systems.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.19-34
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• 2000

Impact of business process re-engineering requires the fundamental rethinking of how information systems are analyzed and designed. It is no longer sufficient to establish a monolithic system for fixed business environments. Information systems must be adaptive in nature. This demand is also applied in production domain. Enabling concept for the adaptive information system is reusability. This paper presents a new object-oriented analysis process for creating such reusable software components in production domain, especially for production planning and scheduling. Our process called MeCOMA is based on three meta-models: physical object meta-model, data object meta-model, and activity object meta-model. After the three meta-models are extended independently for a given production system, they are collaboratively integrated on the basis of integration pattern. The main advantages of MeCOMA are (1) to reduce software development time and (2) to consistently build reusable production software components.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.1
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• pp.18-28
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• 2013

It was compared the estimated parameters by the surplus production from three different models, i.e., three types (Schaefer, Gulland, and Schnute) of the traditional surplus production models, a stock production model incorporating covariates (ASPIC) model and a maximum entropy (ME) model. We also evaluated the performance of models in the estimation of their parameters. The maximum sustainable yield (MSY) of small yellow croaker (Pseudosciaena polyactis) in Korean waters ranged from 35,061 metric tons (mt) by Gulland model to 44,844mt by ME model, and fishing effort at MSY ($f_{MSY}$) ranged from 262,188hauls by Schnute model to 355,200hauls by ME model. The lowest root mean square error (RMSE) for small yellow croaker was obtained from the Gulland surplus production model, while the highest RMSE was from Schnute model. However, the highest coefficient of determination ($R^2$) was from the ME model, but the ASPIC model yielded the lowest coefficient. On the other hand, the MSY of Kapenta (Limnothrissa miodon) ranged from 16,880 mt by ASPIC model to 25,373mt by ME model, and $f_{MSY}$, from 94,580hauls by ASPIC model to 225,490hauls by Schnute model. In this case, both the lowest root mean square error (RMSE) and the highest coefficient of determination ($R^2$) were obtained from the ME model, which showed relatively better fits of data to the model, indicating that the ME model is statistically more stable and robust than other models. Moreover, the ME model could provide additional ecologically useful parameters such as, biomass at MSY ($B_{MSY}$), carrying capacity of the population (K), catchability coefficient (q) and the intrinsic rate of population growth (r).

• Geomechanics and Engineering
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• v.15 no.1
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• pp.677-686
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• 2018

Aimed at serious casing damage problem during the process of oilfield development by injecting water, based on seepage mechanics, fluid mechanics and the theory of rock mechanics, the multi-physics coupling theory was also taken into account, the mathematical model for production of petroleum with water flooding was established, and the method to solve the coupling model was presented by combination of Abaqus and Eclipse software. The Q9G3 block in Jibei oilfield was taken for instance, the well log data and geological survey data were employed to build the numerical model of Q9G3 block, the method established above was applied to simulate the evolution of seepage and stress. The production data was imported into the model to conduct the history match work of the model, and the fitting accuracy of the model was quite good. The main mechanism of casing damage of the block was analyzed, and some wells with probable casing damage problem were pointed out, the displacement of the well wall matched very well with testing data of the filed. Finally, according to the simulation results, some useful measures for preventing casing damage in Jibei oilfield was proposed.

• Journal of the Korean GEO-environmental Society
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• v.22 no.3
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• pp.15-29
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• 2021

The purpose of this study is to establish an ecosystem model that can predict ecosystem fluctuations in the Nakdong estuary, and use this model to calculate total primary production and respiration. AQUATOX model was used as the ecosystem model, and the model was calibrated and verified using the measured data. For the calibration of the model, chlorophyll-a data measured at the Nakdong estuary were used, and the model verification was performed using DO, TN, and TP data. In general, the total primary production and respiration volume vary greatly depending on the season, but the total primary production and respiration in the Nakdong estuary were greatly influenced by the amount of water discharged from Nakdong estuary bank. When the amount of effluent increased, photosynthesis could not be performed due to the loss of phytoplankton living in the lower area, and the total primary production amounted to zero, whereas the respiration increased sharply due to the inflow of organic substances contained in the effluent. The increase in the inflow water means the inflow of organic substances contained in the inflow water, and the organic substances are decomposed by oxidation, reducing dissolved oxygen. Compared with other countries' estuaries, the Nakdong estuary shows the lowest total primary production and because the respiration is larger than the total primary production, the dissolved oxygen is depleted by the oxidation of organic matter.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.265-275
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• 2024

Smart factory is a remarkable development from traditional manufacturing systems to data-based smart manufacturing systems that can connect and process data continuously, collected from machines, production equipment to production and business processes, capable of supporting workers in making decisions or performing work automatically. Smart factory is the key and center of the fourth industrial revolution, combining improvements in traditional manufacturing activities with digital technology to help factories achieve greater efficiency, contributing to increased revenue and reduce operating costs for businesses. Besides, the importance of smart factories is to make production more quality, efficient, competitive and sustainable. Businesses in Vietnam are in the process of learning and applying smart factory models. However, the number of businesses applying the pine factory model is still limited due to many barriers and difficulties. Therefore, in this paper we conduct a survey to assess the needs and current situation of businesses in applying smart factories and propose some specific solutions to develop and promote application of smart factory model in Vietnamese businesses.