• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.71-78
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• 2011

This paper contains the selection of operation parameters for protection and control of steam turbine in a Korea Standard Type Nuclear Power Plant. The safety of nuclear reactor must be ensured which generates nuclear energy and produces steam. Also, the safety of turbine, which consume the nuclear energy as a core machine, must be ensured. For the purpose of this, we describe how the operating parameters were selected, reviewed, implemented into the operator console and finally put into actual operation of the system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.8-13
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• 2001

In the turning process, the feed is usually selected by a machining operator considering workpiece, cutting tool and depth of cut. Even if this selection can avoid power saturation or tool breakage, it is usually conservative compared to the capacity of the machine tools and can reduce the productivity significantly. This paper proposes a selection method of the feed and the reference cutting force based on MRR(material removal rate), maximum spindle power and specific energy. In order to estimate and control cutting force accurately in transient and steady state, this study utilizes a synthesized cutting force estimation method and a Fuzzy controller. The experimental results present that these systems can be useful for the FMS(flexible manufacturing system) and unmanned automation system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.7
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• pp.1446-1452
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• 2002

In the turning process, the feed is usually selected by a machining operator considering workpiece, cutting tool and depth of cut. Even if this selection can avoid power saturation or tool breakage, it is usually conservative compared to the capacity of the machine tools and can reduce the productivity significantly. This paper proposes a selection method of the feed and the reference cutting force based on MRR(material removal rate), maximum spindle power and specific energy. In order to estimate and control cutting force accurately in transient and steady state, this study utilizes a synthesized cutting force estimation method and a Fuzzy controller. The experimental results show that these systems can be useful for the unmanned turning process.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.226-235
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• 1999

The purpose of this study is to present models for evaluation and selection of Electronic Commerce systems suppliers. The Major concern of management is that almost all decision problems have multiple, usually conflicting, criteria. The fuzzified multi-objective programming models are given to accommodate the aspiration level and satisfaction level of decision makers. The proposed models are classified into three types, that is, min-operator. additive and pre-emptive priority. Numerical Examples illustrating each type of model are presented and the implications of these models are discussed.

• IE interfaces
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• v.9 no.1
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• pp.1-10
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• 1996

In this study, expert system for the selection of the optimal cutting speed and feed rate was developed using NEXPERT system shell. The NC system has been usually used inefficiently because the input command, which contains cutting speed, feed-rate and the depth of cut, is fixed value which depends on principally operator's experience and machining handbooks providing a guideline for applicable ranges. On the other hand, the optimal cutting conditions vary with time, and depend on tool and machine characteristics, work materials, and cost factor and so on. In this study, if cutting factors, such as, cutting method, material type, cutting depth, and tool nose radius are specified, our expert system gets the information about the standard cutting speed form the cutting speed database, and provides optimum feed rate for these cutting conditions. This cutting speed database can be updated by inputting valid cutting speed which is obtained form the practices.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.445-458
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• 2020

The least absolute shrinkage and selection operator (LASSO) is a popular method for a high-dimensional regression model. LASSO has high prediction accuracy; however, it also selects many irrelevant variables. In this paper, we consider the moderately clipped LASSO (MCL) for the high-dimensional generalized linear model which is a hybrid method of the LASSO and minimax concave penalty (MCP). The MCL preserves advantages of the LASSO and MCP since it shows high prediction accuracy and successfully selects relevant variables. We prove that the MCL achieves the oracle property under some regularity conditions, even when the number of parameters is larger than the sample size. An efficient algorithm is also provided. Various numerical studies confirm that the MCL can be a better alternative to other competitors.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1540-1549
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• 2017

Objective: Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP) markers strictly selected through least absolute shrinkage and selection operator (LASSO) feature selection. Methods: We performed farm tracing of domesticated pig (Sus scrofa) from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers. Results: We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs. Conclusion: The study represents a successful application of LASSO feature selection on multi-breed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability.

• Journal of Biosystems Engineering
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• v.23 no.2
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• pp.157-166
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• 1998

In this study. a traveling control system was developed to transfer a machine without an operator in the working zone. The dimension of the system was modelized to design and construct smaller than that of real configuration of a greenhouse. For this system, the fixed path type was used to detect exact position during operating a manless machine. and the X-Y table actuator type to escape a unique path, which had the disadvantage in a fixed path type environment. Based on the results of this research the following conclusions were made : 1. This system used two screws to move toward horizontal direction, and a Plate to reach at any points in the working zone. 2. The software combined the functions of path selection and motor operation to control into one program. The path selection program was a menu driven program written in Visual Basic, and the motor operation program was written in Borland C++ for actuating motors. 3. The path-select mode of the program was used by selecting the desired paths, and the user path-create mode by selecting a random path in the path-selection program. 4. The system proved to be a reliable system for operating a manless machine, since accuracy and precision to reach the positions were less than 1%.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.909-922
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• 2019

In this paper, we study the adaptive least absolute shrinkage and selection operator (LASSO) for the unstable autoregressive (AR) model. To identify the existence of the unit root, we apply the adaptive LASSO to the augmented Dickey-Fuller regression model, not the original AR model. We illustrate our method with simulations and a real data analysis. Simulation results show that the adaptive LASSO obtained by minimizing the Bayesian information criterion selects the order of the autoregressive model as well as the degree of differencing with high accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.47-47
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• 2023

Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R²=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.