• 가족자원경영과 정책
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• 제24권1호
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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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• 제42권5호
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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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• 제11권2호
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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.

• 인터넷정보학회논문지
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• 제23권5호
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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.

• 한국융합학회논문지
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• 제12권10호
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• pp.27-35
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• 2021

기계번역 사후교정 (Automatic Post Editing, APE)이란 번역 시스템을 통해 생성한 번역문을 교정하는 연구 분야로, 영어-독일어와 같이 학습데이터가 풍부한 언어쌍을 중심으로 연구가 진행되고 있다. 최근 APE 연구는 전이학습 기반 연구가 주로 이루어지는데, 일반적으로 self supervised learning을 통해 생성된 사전학습 언어모델 혹은 번역모델이 주로 활용된다. 기존 연구에서는 번역모델에 전이학습 시킨 APE모델이 뛰어난 성과를 보였으나, 대용량 언어쌍에 대해서만 이루어진 해당 연구를 저 자원 언어쌍에 곧바로 적용하기는 어렵다. 이에 본 연구에서는 언어 혹은 번역모델의 두 가지 전이학습 전략을 대표적인 저 자원 언어쌍인 한국어-영어 APE 연구에 적용하여 심층적인 모델 검증을 진행하였다. 실험결과 저 자원 언어쌍에서도 APE 학습 이전에 번역을 한차례 학습시키는 것이 유의미하게 APE 성능을 향상시킨다는 것을 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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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.

• 한국통신학회논문지
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• 제38B권7호
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• pp.512-518
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• 2013

분산 클라우드 컴퓨팅에서 자원 할당 알고리즘은 사용자 만족도와 서비스 수용 및 처리 능력과 밀접한 관련을 가지기 때문에 중요하다. 즉, 분산 클라우드에서는 서비스 처리를 위해 이용가능한 자원이 없을 때 발생하는 서비스 거부는 사용자 만족도를 반감시킨다. 따라서 본 논문에서는 서비스 거부를 최소화하기 위하여 데이터센터 자원 상황을 고려한 자원 할당 알고리즘을 제안한다. 제안하는 알고리즘은 Q-Learning 기반의 자원 할당량 학습에 의해서 클라우드 데이터센터에서 최대 자원 할당량 만큼 할당을 할 수 있으면 자원 할당량이 증가하고 그렇지 못할 때는 자원 할당량이 감소하게 된다. 본 논문에서는 제안하는 알고리즘과 기존의 두 알고리즘을 평가하고 제안하는 알고리즘이 두 알고리즘 보다 낮은 서비스 거부율을 보임을 증명한다.

• 한국도서관정보학회지
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• 제42권2호
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• pp.215-236
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• 2011

구성주의교육은 현대지식기반 사회에서 학교교육의 중요한 목표이다. 구성주의는 학습자의 사회적 학교생활을 통해서 지식의 구성이 개인적, 주관적으로 이루어진다고 주장한다. 학습자의 창의성을 바탕으로 하는 지식의 개인적, 주관적 구성은 오늘날 학교교육의 목표를 학습자중심교육으로 발전시키고 있다. 본 연구는 구성주의교육의 원리와 특성을 규명하고, 구성주의교육의 구현요소로서 학교도서관활용수업의 개념과 특성을 살펴보고자 한다. 학교도서관활용수업은 최근 학교현장에서 관심을 받고 있으나 아직은 충분한 단계로 발전하지 못하고 있다. 본 연구에서는 학교도서관활용수업의 범위와 단계를 조사하고 구성주의교육에서 적용될 수 있는 일반적인 특징을 제시하고자 한다. 구성주의교육은 학습자의 실제적(authentic)경험과 체험을 중시하는 학습환경의 조성을 강조하며, 자료중심교육을 바탕으로 하는 토의학습, 문제 중심학습, 프로젝트학습 등의 학습방법이 효과적이다. 학교도서관은 학교의 정보자료센터로서 자료중심교육의 필수적 요소이며, 학교도서관활용수업은 가장 효과적인 자료중심교육의 방법이다.

• Industrial Engineering and Management Systems
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• 제8권3호
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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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• 제52권6호
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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.