• Progress in Superconductivity and Cryogenics
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• v.23 no.3
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• pp.26-31
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• 2021

Advanced classification of Cs contaminated soil by using a magnetic force-assisted selection pipe was investigated. A selection pipe is a device that sort particles depending on their particle size, based on the relationship between buoyancy, drag, and gravity force acting on the particles. Radioactive cesium is concentrated in small-particle size soil components with a large specific surface area. Hence, the volume of the Cs contaminated soil can be reduced by recycling the large-particle size soil components with low radioactive concentration. One of the problems of the selection pipe was that the radioactive concentration of the stayed soil in the selection pipe exceeds 8000 Bq/kg, which is the standard value of recycling of Cs contaminated soil, due to low classification accuracy. In this study, magnetic fields were applied to the lab-scale selection pipe from upper side to improve the classification accuracy and to reduce the radioactive concentration of the stayed soil.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.179-189
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• 2023

Hybrid sufficient dimension reduction (SDR) methods to a weighted mean of kernel matrices of two different SDR methods by Ye and Weiss (2003) require heavy computation and time consumption due to bootstrapping. To avoid this, Park et al. (2022) recently develop the so-called cross-distance selection (CDS) algorithm. In this paper, two variations of the original CDS algorithm are proposed depending on how well and equally the covk-SAVE is treated in the selection procedure. In one variation, which is called the larger CDS algorithm, the covk-SAVE is equally and fairly utilized with the other two candiates of SIR-SAVE and covk-DR. But, for the final selection, a random selection should be necessary. On the other hand, SIR-SAVE and covk-DR are utilized with completely ruling covk-SAVE out, which is called the smaller CDS algorithm. Numerical studies confirm that the original CDS algorithm is better than or compete quite well to the two proposed variations. A real data example is presented to compare and interpret the decisions by the three CDS algorithms in practice.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.56
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• pp.3-27
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• 2012

The purpose of this thesis is to develop a relational model which can explain consumer selection for exporting goods and analyze the effect of corporate image on the relations between country image and consumer selection under global trade environment. The specific objectives are as follows: 1) to suggest a concept of consumer selection and measurement criteria, 2) to analyze correlations among country image, corporate image and consumer selection and 3) to find out the effect of corporate image on the relations between country image and consumer selection. The SPSS program for window and LISREL program were used to analyze the data for this study. The statistical method used in this study was the covariance structure analysis estimating parameters by maximum likelihood method. Path coefficients were tested for t-tests with a statistical significance level of .05. The conclusions of this study are as follows. First, significant correlations were observed among all sub-variables proposed in this study. In addition, significant correlations were detected among country image, consumer selection and corporate image. Second, a hypothetical model proposed in this study was mostly appropriate. Country image had a positive direct effect on consumer selection and corporate image with statistical significance. In addition, it has an indirect impact on consumer selection with statistical significance with corporate image as an intervening variable. Third, corporate image had a significant moderation effect in country image-consumer selection relations. As corporate image levels increased, the effect of country image on consumer selection increased as well. In other words, it has been confirmed that if corporate image levels are high, country image could end up with consumer selection.

• Journal of Distribution Science
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• v.11 no.12
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• pp.71-81
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• 2013

Purpose - This study investigated the effects of consumers' selection criteria for environment-friendly agricultural products on purchase intention and the effects of consumers' attitudes and the reliability of environment-friendly agricultural products on purchase intention by using the theory of planned behavior. Subjective norms of variables of behavioral intention, attitudes toward behavior and control of the behavior were used to create selection criteria, consumers' attitudes and reliability of environment-friendly agricultural products. The study investigated the effects of consumers' selection criteria, attitudes, and reliability of environment-friendly agricultural products on purchase intention constructing models and hypotheses of mediation and moderation between selection criteria for agricultural products and purchase intention by consumers' attitudes and reliability. Research design, data, and methodology - The findings were as follows: first, consumers' selection criteria for environment-friendly agricultural products had a significantly affirmative influence upon purchase intention. Health was the most important factor of selection criteria convenience was more important than quality and familiarity was next. Consumers' attitudes and trust had a significant influence on purchase intention. Second, testing showed that consumers' attitude and trust partially mediated selection criteria: sub-factors and purchase intention were important in selection criteria. Third, testing showed that consumers' attitude and trust had a significant moderation effect between selection criteria and purchase intention. In the test of the moderation effect between sub-factors of selection criteria and purchase intention, consumers' attitude had a significantly positive influence upon health, convenience, and familiarity, and had no significant influence upon quality and purchase intention. Consumers' trust had no significant influence upon health, convenience, and quality. Results - The study provided several theoretical implications: first, an empirical analysis was undertaken with selection criteria for environmental-friendly agricultural products, consumers' attitude, and trust to investigate subjective norms, attitude toward behavior and control of behavior based on the theory of planned behavior. Second, this study investigated both the mediation effect and moderation effect of consumers' subjective norms on attitudes toward behavior, the mediating effects of perceived behavior control and changes of behavioral intention depending upon size and direction of the variables. This study also provided several practical implications. Conclusions - First, consumption of environment-friendly agricultural products did not increase despite rapid increase of production therefore, promotion of consumption and distribution was needed considering the supply and demand of the products. Second, definite standards for selection criteria were suggested to build up consumers' attitude and trust. Consumers' attitude could be improved by factors including the brand of environment-friendly agricultural products, consistent quality, solving physiological problems caused by adverse effects of environmental problems, supplementary approaches, treatment of adverse effects by eating food, and the development and supply of products in accordance with changes of lifestyle. Finally, consumers' demand for sub-factors of selection criteria could be much higher than health, convenience, and quality of the products. Therefore, a process was needed that could continuously check consumers' needs for the products. Limitations were described at the end of the study.

• ETRI Journal
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• v.34 no.6
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• pp.847-857
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• 2012

Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.187-194
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• 2014

We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.105-111
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• 2008

A typical AP (Access Point) selection policy is to select an AP based on the received signal strength indicator (RSSI), ignoring its load. If multiple stations are deployed densely at a particular area, a typical AP selection policy will bring about the overall network throughput degradation. This paper proposes a novel AP selection policy taking into consideration not only signal strength of the APs but also AP loads to avoid Hotspot congestion. An experiment on Alinker implementing proposed AP selection policy, demonstrates that the proposed policy achieves close to optimal load balancing and grants the maximum transmission rate to stations in comparison with SSF (Strongest-Signal-First) and LLF (Least-Loaded-First).

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• Journal of KIBIM
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• v.5 no.2
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• pp.34-45
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• 2015

As buildings become larger and more complicated, construction costs have increased with a considerable effect on buildings' Life Cycle Cost (LCC). However, there has been little consideration on economic aspects in the selection of construction materials due to limited information on the materials and dependency in architects' experience and inefficiency in cost estimation, causing design changes, increase in maintenance cost, difficulty in budgeting, and decrease in building performance. To solve these problems, this study proposed a BIM-based material selection model which reflects the comprehensive economic efficiency of building materials. Our cost prediction model can estimates the material-related cost during the entire building life cycle. Furthermore, we implemented the proposed model in connection with BIM, which can analyze and compare LCC by material. Through the validation of the model, we could confirm the necessity of LCC-based material selection in comparison with the conventional cost-centered material selection.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.1
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• pp.118-125
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• 2003

As consumer's desire becomes various, agility of mold manufacturing is the most important factor for competitive mold manufacturer. Decision making process is required to produce optimal result of CAM systems in using commercial CAM system to generate tool path. The paper proposes a methodology fur computer-assisted tool selection procedures for various cutting type of rough, semi-rough and finish cuts. The procedure provides assistance for machining tool selection by analyzing sliced CAD model section of die cavity and core. Information about machining time for the generated NC-code is used to aid the tool selection. The module is developed with commercial CAM API. This module will be used fur the optimization of tool selection and planning process.