• Journal of Family Resource Management and Policy Review
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• v.24 no.1
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• pp.41-53
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• 2020

The purpose of this study was to identify the factors that affect middle-old aged housewives' participation in lifelong learning as a part of human resource development. Through purposive sampling, the study recruited 163 full-time housewives over age 40 years who live in C City. As a result, first, 87.1 percent of all respondents, or 142, said they were willing to participate in lifelong learning in the future. There was no statistically significant difference in the results of cross-checking by age, educational background and monthly household income variables. Additionally, we used cluster analysis to measure differences in participation intentions according to the perception of human resource development of middle-old aged full-time housewives. The perception variable of lifelong learning is: First, Cognitive degree, second, importance, third, activation awareness. Cluster 1(n=16) was divided into generally low-perception types, such as cognitive degree, importance, and life-long learning activation of the C city, while Cluster 2(n=61) was classified as a type of person who thinks that lifelong learning is important to life and Cluster 3(n=86) was generally classified as a type with a higher lifelong learning perception. and we found that there was no difference in the intention to participate in lifelong learning by all cluster Lastly, we found that participants who valued human resource development scored significantly higher on measures of cognition than those who did not value it. Based on these results, we advocates social change that encourages the cultivation of talent through lifelong learning programs that can positively affect one's unique identity, not just wife and mother, and provide opportunities for self-development.

• ETRI Journal
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• v.42 no.5
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.142-152
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• 2022

We study confirmed the relationship between the adversity index, entrepreneurial orientation, and organizational learning competency perceived by organizational members as follows. First, the adversity index showed a positive (+) effect on entrepreneurial orientation (hypothesis 1) and organizational learning competency (hypothesis 2). Second, the entrepreneurial orientation was statistically significant in organizational learning competency (hypothesis 3). Third, the partial mediating role of entrepreneurial orientation (Hypothesis 4) was confirmed in the process of the adversity index affecting organizational learning competency. Meanwhile, the main implications of this study are as follows. First, it is the aspect that provides additional theoretical implications in the reality that studies on the adversity index and entrepreneurial orientation that affect organizational learning competency are lacking. Second, it is the aspect that the importance of adversity index and start-up orientation was confirmed in improving organizational learning competency based on securing differentiated competitiveness for the advancement of the organization's sustainability management system. In addition, it is the aspect of drawing practical implications for strategic human resource management and human resource development to systematically improve it.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.27-35
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• 2021

Automatic post editing is a research field that aims to automatically correct errors in machine translation results. This research is mainly being focus on high resource language pairs, such as English-German. Recent APE studies are mainly adopting transfer learning based research, where pre-training language models, or translation models generated through self-supervised learning methodologies are utilized. While translation based APE model shows superior performance in recent researches, as such researches are conducted on the high resource languages, the same perspective cannot be directly applied to the low resource languages. In this work, we apply two transfer learning strategies to Korean-English APE studies and show that transfer learning with translation model can significantly improves APE performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1545-1559
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• 2023

In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.512-518
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• 2013

A resource allocation algorithm has a high impact on user satisfaction as well as the ability to accommodate and process services in a distributed cloud computing. In other words, service rejections, which occur when datacenters have no enough resources, degrade the user satisfaction level. Therefore, in this paper, we propose a resource allocation algorithm considering the cloud domain's remaining resources to minimize the number of service rejections. The resource allocation rate based on Q-Learning increases when the remaining resources are sufficient to allocate the maximum allocation rate otherwise and avoids the service rejection. To demonstrate, We compare the proposed algorithm with two previous works and show that the proposed algorithm has the smaller number of the service rejections.

• Journal of Korean Library and Information Science Society
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• v.42 no.2
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• pp.215-236
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• 2011

I studied on the relations of constructivism and school library assisted instruction in this paper. Constructivism is the most important goal of the modern schooling. In constructivism they insist that knowledge is constructed by the learner individually and subjectively. So in constructivism they focus their attention on setting authentic environment of learning for each individual learner. Constructivism was developed into the learner-centered instruction in schooling nowadays. In constructivism the following instructions are very important for achievement its' goal ; problem based learning, project based learning, discussion based learning etc. These instructions are supported commonly by resource based learning. Educational resources are managed in school library totally. School library assisted instruction is the most effective one for resource based learning. And information literacy instruction by teacher librarian relates closely meta cognitive learning of reflections in constructivism. School library assisted instruction is the essential element for the practice of constructivism in schooling.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• Human Ecology Research
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• v.52 no.6
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• pp.669-682
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• 2014

The purpose of this study is to present a practical class design model that applies the problem-based learning (PBL) method to the subject of home economics. To begin with, a specific class model example was developed by conducting thorough document research and expert consulting. Two modules, named "Click! Global Leisure Environment" and "Happy Leisure Product Launching" were presented as the PBL questions. The case study focused upon in this research is an elective course called "Leisure Culture and Life Management". The 21 students enrolled in this course were considered in this study. Two teaching methods, namely a face-to-face teaching method and a web-based system "Snowboard" teaching method, were used to run the class. The research results are as follows: first, theoretical research and program development and demonstration were practiced with five different age groups: childhood, adolescence, university student, middle age, and senescence. Then, selfevaluation, peer evaluation, and group evaluation were conducted to motivate the students. Finally, a class evaluation was conducted by questioning the lecturer, who ranked well, scoring higher than or equal to 4.0 points out of 5.0 on all the questions. Through the PBL method, students showed an improved study attitude with more proactive participation in the class, they strengthened their communication skills and created a synergy with their team members. This study has significant meaning because it is the first research to apply the PBL method to home economics. Therefore, we expect other curricula to apply PBL and fully utilize this teaching method as well in the future.