• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.5
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• pp.75-80
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• 2015

Nowadays, flat-shaped cover glass is widely used for mobile devices. However, for its good design and convenience of use, curved cover glass has been demanded. Thus, many companies have tried to produce curved cover glass through the shaving technique, but the production efficiency is very low. Therefore, the molding technique has been adopted to increase the efficiency for the curved-glass production system. For a glass-molding system, several heating blocks are installed, and the flat cover glass is sequentially heated and molded. The production time for the cover glass is very different depending on the heating conditions; thus, the prediction of the production time for different heating conditions should be needed. Therefore, in this study, the computations were performed with different heating conditions (uniform and non-uniform) in the present cover glass-molding machine. For uniform and non-uniform heating conditions, the simple correlation between the heating time and the heater capacity and the heating time to achieve higher durability can be suggested, respectively.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.569-579
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• 2021

Purpose: The purpose of this study is the time series analysis for predicting the yield of crops applicable to each farm using environmental variables measured by smart farms cultivating tomato. In addition, it is intended to confirm the influence of environmental variables using a deep learning model that can be explained to some extent. Methods: A time series analysis was performed to predict production using environmental variables measured at 75 smart farms cultivating tomato in two periods. An LSTM-based encoder-decoder model was used for cases of several farms with similar length. In particular, Dual Attention Mechanism was applied to use environmental variables as exogenous variables and to confirm their influence. Results: As a result of the analysis, Dual Attention LSTM with a window size of 12 weeks showed the best predictive power. It was verified that the environmental variables has a similar effect on prediction through wieghtss extracted from the prediction model, and it was also verified that the previous time point has a greater effect than the time point close to the prediction point. Conclusion: It is expected that it will be possible to attempt various crops as a model that can be explained by supplementing the shortcomings of general deep learning model.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.621-630
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• 2009

In recent years, there have been many attempts to connect the latest RFID (Radio Frequency Identification) technology with EIS (Enterprise Information System) and utilize them. However, in most cases the focus is only on the simultaneous multiple reading capability of the RFID technology neglecting the management of massive data created from the reader. As a result, it is difficult to obtain time-related information such as flow prediction and analysis in process control. In this paper, we suggest a new method called 'procedure tree', an enhanced and complementary version of PathTree which is one of RFID data mining techniques, to manage massive RFID data sets effectively and to perform a real-time process control efficiently. We will evaluate efficiency of the proposed system after applying real-time process management system connected with the RFID-based EIS. Through the suggested method, we are able to perform such tasks as prediction or tracking of process flow for real-time process control and inventory management efficiently which the existing RFID-based production system could not have done.

• Computers and Concrete
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• v.12 no.2
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• pp.109-129
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• 2013

This work presents an approach to model concrete shrinkage. The goal is to permit the concrete industry's experts to develop independent prediction models based on a reduced number of experimental data. The proposed approach combines fuzzy logic and genetic algorithm to optimize the fuzzy decision-making, thereby reducing data collection time. Such an approach was implemented for an experimental data set related to self-compacting concrete. The obtained prediction model was compared against published experimental data (not used in model development) and well-known shrinkage prediction models. The predicted results were verified by statistical analysis, which confirmed the reliability of the developed model. Although the range of application of the developed model is limited, the genetic-fuzzy approach introduced in this work proved suitable for adjusting the prediction model once additional training data are provided. This can be highly inviting for the concrete industry's experts, since they would be able to fine-tune their models depending on the boundary conditions of their production processes.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.11-21
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• 1993

In this paper, we survey the current status of measurement labor in Korean Industry. At the same time we try to predit the demand and supply of measurement labor to suggest policy measures for equilibrium in measurement labor market. We use a general production function for the prediction which include a set of general homethetic production function.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3130-3139
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• 2022

Direct containment heating (DCH) is one of the potential factors leading to early containment failure. DCH is closely related to safety analysis and containment performance evaluation of nuclear power plants. In this study, a DCH prediction program was developed to analyze the DCH loads of containment vessel. The phenomenological model of debris dispersal, metal oxidation reaction, debris-atmospheric heat transfer and hydrogen jet burn was established. Code assessment was performed by comparing with several separate effect tests and integral effect tests. The comparison between the predicted results and experimental data shows that the program can predict the key parameters such as peak pressure, temperature, and hydrogen production in containment well, and for most comparisons the relative errors can be maintained within 20%. Among them, the prediction uncertainty of hydrogen production is slightly larger. The analysis shows that the main sources of the error are the difference of time scale and the oxidation of cavity debris.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.29-37
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• 2023

Due to climate change, interest in crop production and distribution is increasing, and attempts are being made to use bigdata and AI to predict production volume and control shipments and distribution stages. Prediction of agricultural product imports not only affects prices, but also controls shipments of farms and distributions of distribution companies, so it is important information for establishing marketing strategies. In this paper, we create an artificial intelligence prediction model that predicts the future import volume based on the wholesale market melon import volume data disclosed by the agricultural statistics information system and evaluate its accuracy. We create prediction models using three models: the Neural Prophet technique, the Ensembled Neural Prophet model, and the GRU model. As a result of evaluating the performance of the model by comparing two major indicators, MAE and RMSE, the Ensembled Neural Prophet model predicted the most accurately, and the GRU model also showed similar performance to the ensemble model. The model developed in this study is published on the web and used in the field for 1 year and 6 months, and is used to predict melon production in the near future and to establish marketing and distribution strategies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.132-139
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• 2009

Yield is a very important measure that can expresses simply for productivity and performance of company. So, yield is used widely in many industries nowadays. With the development of the information technology and online based real-time process monitoring technology, many industries operate the production lines that are developed into automation system. In these production lines, the product structures are very complexity and variety. So, there are many multi-variate processes that need to be monitored with many quality characteristics and associated process variables at the same time. These situations have made it possible to obtain super-large manufacturing process data sets. However, there are many difficulties with finding the cause of process variation or useful information in the high capacity database. In order to solve this problem, neural networks technique is a favorite technique that predicts the yield of process for process control. This paper uses a neural networks technique for improvement and maintenance of yield in manufacturing process. The purpose of this paper is to model the prediction of a sub process that has much effect to improve yields in total manufacturing process and the prediction of adjustment values of this sub process. These informations feedback into the process and the process is adjusted. Also, we show that the proposed model is useful to the manufacturing process through the case study.