• Journal of Animal Science and Technology
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• v.62 no.6
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• pp.790-800
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• 2020

The Yeonsan Ogye (YO) chicken is a natural heritage of Korea, characterized by black feathers, skin, bones, eyes, and comb. The purebred of YO population has been reared under the natural mating system with no systematic selection and breeding plan. The purpose of this study was to identify the genetic diversity and find the optimal number of population sub-division using 12 polymorphic microsatellite (MS) markers to construct a pedigree-based breeding plan for the YO population. A total of 509 YO birds were used for this study. Genetic diversity and population structure analysis were conducted based on the MS marker genotype information. The overall average polymorphic information content value and expected heterozygosity of the population were 0.586, and 0.642, respectively. The K-mean cluster analysis based on the genetic distance result confirmed that the current YO population can be divided into three ancestry groups. Individuals in each group were evaluated based on their genetic distance to identify the potential candidates for a future breeding plan. This study concludes that a future breeding plan with known pedigree information of selected founder animals, which holds high genetic diversity, could be the best strategy to ensure the conservation of the Korean YO chicken population.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Journal of Hydrogen and New Energy
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• v.32 no.6
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• pp.524-533
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• 2021

In this study, a series of anion conductive organic/inorganic composite membranes with excellent ionic conductivity and chemical stability were prepared by introducing graphene oxide (GO) inorganic nanofiller into the quaternized poly(phenylen oxide (Q-PPO) polymer matrix. The fabricated organic/inorganic composite membranes showed higher ionic conductivity than the pristine membrane. In particular, Q-PPO/GO 0.7 showed the highest ionic conductivity value of 143.2 mS/cm at 90℃, which was 1.56 times higher than the pristine membrane Q-PPO (91.5 mS/cm). In addition, the organic/inorganic composite membrane showed superior dimensional stability and alkaline stability compared to the pristine membrane, and the physicochemical stability was improved as the content of inorganic fillers increased. Therefore, we suggest that the as-prepared organic/inorganic composite membranes are very promising materials for anion exchange membrane applications with high conductivity and alkaline stability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.294-300
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• 2022

The article highlights the European experience of innovative educational technologies of training management specialists. Based on existing strategies, relevant in the European educational space, the introduction of regulatory elements to maintain a balance between the traditional and innovative format of the educational process, which is typical for the Ukrainian education system is proposed. The article aims to single out educational and technological innovations into a separate cluster of managerial training at different levels in the context of the principles of the modern synergetic sociocultural paradigm. The main objectives of the work are to develop settings to ensure the effective functioning of innovative educational technologies. Among the synergetic principles of educational technologies, providing the formation of necessary competencies of future managers, are: self-organization, interdisciplinarity, nonlinearity, individuality, and technologization. The methods used in the scientific study can be attributed to the group of scientific synergetic methodology. So, the training of specialists in management, implemented in the European practice assumes the use of new educational strategies. These technologies provide both the necessary skills of different levels (hard-soft-digital skills) and the observance of value components (solidarity, ethics, inclusiveness, openness).

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.45-49
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• 2022

The fourth industrial revolution, internet of things, and the expansion of online web services have increased an exponential growth and deployment in the number of cloud data centers (CDC). The cloud is emerging as new paradigm for delivering the Internet-based computing services. Due to the dynamic and non-linear workload and availability of the resources is a critical problem for efficient workload and resource management. In this paper, we propose the particle swarm optimization (PSO) based gated recurrent unit (GRU) neural network for efficient prediction the future value of the CPU and memory usage in the cloud data centers. We investigate the hyper-parameters of the GRU for better model to effectively predict the cloud resources. We use the Google Cluster traces to evaluate the aforementioned PSO-GRU prediction. The experimental shows the effectiveness of the proposed algorithm.

• Journal of Korea Trade
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• v.24 no.5
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• pp.55-70
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• 2020

Purpose - This paper aims to examines the various factors influencing the purchase decisions of Malaysian consumers for halal food and based on this analysis, to present some measures by which Korea's food industry could expand exports to the Malaysian market. Design/methodology - This research used SPSS 12.0 for descriptive analysis, ANOVA, t-tests, factor analysis, cluster analysis, and reliability analysis based on a total of 571 responses were included as the final data in the 600 surveys administered. Findings - Malaysian consumers had high trust and confidence in products that had obtained halal certifications. This reflects the cultural situation where 61% of the Malaysian population consist of Muslims. In terms of the consumption of Korean foods, items such as ramyeon, confectionery, and kimchi were found to enjoy high awareness and strong preference among local consumers, thus suggesting their competitiveness. Originality/value - This paper attempts to examine consumer characteristics - an aspect that had received insufficient treatment in previous studies on halal certifications in Muslim countries. This study found the purchase practices and influencing factors behind Malaysian consumers' purchases of imported foods and Korean foods. Therefore, it is expected that this result can give Korean food industry an insights and strategies for exporting Korean food to Malaysia.

• Asian Journal of Innovation and Policy
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• v.12 no.1
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• pp.75-100
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• 2023

Over the past few decades, globalization has been shifting economic power upward to transnational actors on the one hand, and downward to subnational or regional spaces on the other. This phenomenon has resulted in the centrality of territorially delimited subnational regions acting as critical loci of economic governance within a complex and globally distributed value chain of trade and service flows. Within this broader context of industrial restructuring are economic regions that span national borders in their collective assets. The paper focuses on investigating the economic competitiveness and productivity of cross-border (or binational) economic regions. Using the conceptual framework of economic clusters, an econometric model that measures proxies of geographic proximity of firms in the life sciences cluster, and a new binational economic model, the paper examines the key characteristics, potentials and constraints of economic competitiveness and productivity in a cross-border region comprising counties in Western New York and regional municipalities in Southern Ontario. The findings demonstrate the direct and indirect benefits of closer cross-border economic cooperation. The paper then concludes with some policy observations about leveraging cross-border economic clusters for strategic industrial cooperation.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.159-167
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• 2023

Objective: • To detect black hole and warm hole attacks in wireless sensor networks. • To give a solution for energy depletion and security breach in wireless sensor networks. • To address the security problem using strategic decision support system. Methods: The proposed stackelberg game is used to make the spirited relations between multi leaders and multi followers. In this game, all cluster heads are acts as leaders, whereas agent nodes are acts as followers. The game is initially modeled as Quadratic Programming and also use backtracking search optimization algorithm for getting threshold value to determine the optimal strategies of both defender and attacker. Findings: To find optimal payoffs of multi leaders and multi followers are based on their utility functions. The attacks are easily detected based on some defined rules and optimum results of the game. Finally, the simulations are executed in matlab and the impacts of detection of black hole and warm hole attacks are also presented in this paper. Novelty: The novelty of this study is to considering the stackelberg game with backtracking search optimization algorithm (BSOA). BSOA is based on iterative process which tries to minimize the objective function. Thus we obtain the better optimization results than the earlier approaches.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.