• Honam Mathematical Journal
• /
• v.35 no.4
• /
• pp.757-764
• /
• 2013

In this paper, we give sufficient conditions for a map with nonconvex values to have a continuous selection and the selection extension property in LC-metric spaces under the one-point extension property. And we apply it to weakly lower semicontinuous maps and generalize previous results. We also get a continuous selection theorem for almost lower semicontinuous maps with closed sub-admissible values in $\mathbb{R}$-trees.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2027-2032
• /
• 2003

For action selection as well as learning, simple associations between stimulus and response have been employed in most of literatures. But, for a successful task accomplishment, it is required that an animat can learn and express behavioral sequences. In this paper, we propose a novel action-selection-mechanism to deal with sequential behaviors. For this, we define behavioral motivation as a primitive node for action selection, and then hierarchically construct a network with behavioral motivations. The vertical path of the network represents behavioral sequences. Here, such a tree for our proposed ASM can be newly generated and/or updated, whenever a new sequential behaviors is learned. To show the validity of our proposed ASM, three 2-D grid world simulations will be illustrated.

• Journal of Construction Engineering and Project Management
• /
• v.7 no.2
• /
• pp.1-10
• /
• 2017

The Clonal Selection Algorithm (CSA) is an algorithm inspired by the human immune system mechanism. In CSA, several parameters needs to be optimized by large amount of sensitivity analysis for the optimal results. They limit the accuracy of the results due to the uncertainty and subjectivity. Adaptive Clonal Selection (ACS), a modified version of CSA, is developed as an algorithm without controls by pre-defined parameters in terms of selection process and mutation strength. In this paper, we discuss the ACS in detail and present its implementation in construction site utilization planning (CSUP). When applied to a developed model published in research literature, it proves that the ACS are capable of searching the optimal layout of temporary facilities on construction site based on the result of objective function, especially when the parameterization process is considered. Although the ACS still needs some improvements, obtaining a promising result when working on a same case study computed by Genetic Algorithm and Electimze algorithm prove its potential in solving more complex construction optimization problems in the future.

• International Journal of Internet, Broadcasting and Communication
• /
• v.9 no.4
• /
• pp.51-58
• /
• 2017

This paper deals with the improvement of cluster head selection method in L-SEP for heterogeneous nodes among hierarchical routing protocols of wireless sensor network. Wireless sensor networks are classified into homogeneous and heterogeneous network. In heterogeneous network, SEP, L-SEP are mainly used because cluster head selection probability is different depending on node type. But, since protocol based on SEP has different cluster head selection probabilities depending on the node type, clusters that transmit data inefficiently can be formed. to improve this, it is necessary to select the cluster head that minimizes the transmission distance of member node and the cluster head. Therefore, we propose a protocol that improve the cluster head selection method.

• Journal of Communications and Networks
• /
• v.14 no.4
• /
• pp.364-373
• /
• 2012

In this paper, we propose combined relay selection and cooperative beamforming schemes for physical layer security. Generally, high operational complexity is required for cooperative beamforming withmultiple relays because of the required information exchange and synchronization among the relays. On the other hand, while it is desirable to reduce the number of relays participating in cooperative beamforming because of the associated complexity problem, doing so may degrade the coding gain of cooperative beamforming. Hence, we propose combined relay selection and cooperative beamforming schemes, where only two of the available relays are selected for beamforming and data transmission. The proposed schemes introduce a selection gain which partially compensates for the decrease in coding gain due to limiting the number of participating relays to two. Both the cases where full and only partial channel state information are available for relay selection and cooperative beamforming are considered. Analytical and simulation results for the proposed schemes show improved secrecy capacities compared to existing physical layer security schemes employing cooperative relays.

• Animal cells and systems
• /
• v.1 no.4
• /
• pp.619-627
• /
• 1997

Maintenance of polymorphism in a two-locus system with two alleles under stabilizing selection has been tested by Monte-Carlo simulation. The effect of each allele was additive. Only gene x environment interactions and degree of genetic linkage between loci were considered. There were no other evolutionary forces acting except stabilizing selection. Fixation rates were influenced by the extent of environmental change and the degree of genetic linkage. In most cases, stabilizing selection depleted genetic variability when two loci have a lower degree of linkage (10 cM). When two loci are closely linked (0.1 cM), however, stabilizing selection promoted balanced heterozygotes in changing environments. Thus, environment-dependent selection and recombination rate are important parameters which should be incorporated into mechanisms of maintenance of genetic variability.

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

• Asian-Australasian Journal of Animal Sciences
• /
• v.29 no.1
• /
• pp.36-42
• /
• 2016

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to $2^*SNP$ effect.

• Smart Media Journal
• /
• v.12 no.3
• /
• pp.30-37
• /
• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2022.10a
• /
• pp.233-233
• /
• 2022

As the world's population grows and food needs diversify, the demand for horticultural crops for beneficial traits is increasing. In order to meet this demand, it is necessary to develop suitable cultivars and breeding methods accordingly. Breeding methods have changed over time. With the recent development of sequencing technology, the concept of genomic selection (GS) has emerged as large-scale genome information can be used. GS shows good predictive ability even for quantitative traits by using various markers, breaking away from the limitations of Marker Assisted Selection (MAS). Moreover, GS using machine learning (ML) and deep learning (DL) has been studied recently. In this study, we aim to build a system that selects phenotype-related markers using the genomic information of the pepper population and trains a genomic selection model to select individuals from the validation population. We plan to establish an optimal genome wide association analysis model by comparing and analyzing five models. Validation of molecular markers by applying linkage markers discovered through genome wide association analysis to breeding populations. Finally, we plan to establish an optimal genome selection model by comparing and analyzing 12 genome selection models. Then We will use the genome selection model of the learning group in the breeding group to verify the prediction accuracy and discover a prediction model.