• Journal of The Korean Association of Information Education
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• v.12 no.3
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• pp.333-345
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• 2008

The purpose of the research is to investigate the perception of corporate and e-Learning institute on ubiquitous society and learning environment in the context of human resource development. In addition, the study aims to analyze their recognition of u-Learning's components into the level of significance and actualization. The total of 118 institutes were participated in the research survey among the 144 e-learning certified institutes from the Ministry of Labor. The research includes the results of survey and implications on the area of human resource development.

• The Korean Fashion and Textile Research Journal
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• v.14 no.1
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• pp.37-47
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• 2012

The purpose of this study was to explore an intrinsic concept of 'company innovativeness' based on qualitative data obtained from 54 practitioners with at least 5 year-experience in the industry. Data were analyzed based on the grounded theory and Nvivo 2.0 program. Based on results, seven theme were generated. Seven intrinsic theme include 5 intrinsic concepts such as learning orientation, excellence of human resource, treatment for employees, market orientation and work efficiency, and 2 performance concepts such as business performance and innovation performance. These diverse concepts were considered as an important guideline in judging whether a company is innovative or not. In addition, diverse programs were provided by companies for a purpose of enhancing company innovativeness. Based on results, a research model was suggested to be elaborated in future studies. Implications and suggestions were generated based on results.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.769-774
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• 2024

The availability of PMT(Project Management Tool) in the market has been increasing rapidly in recent years and Significant advancements have been made for project managers to use for planning, monitoring, and control. Recently, studies applying the Reinforcement-Learning Based Construction Schedule Simulation algorithm for construction project process planning/management are increasing. When reinforcement learning is applied, the agent recognizes the current state and learns to select the action that maximizes the reward among selectable actions. However, if the action of global optimal points is not selected in simulation selection, the local optimal resource may receive continuous compensation (+), which may result in failure to reach the global optimal point. In addition, there is a limitation that the optimization time can be long as numerous iterations are required to reach the global optimal point. Therefore, this study presented a method to improve optimization performance by increasing the probability that a resource with high productivity and low unit cost is selected, preventing local optimization, and reducing the number of iterations required to reach the global optimal point. In the performance evaluation process, we demonstrated that this method leads to closer approximation to the optimal value with fewer iterations.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.205-216
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• 2022

One of the many tools used for distributed deep learning training is Kubeflow, which runs on Kubernetes, a container orchestration tool. TensorFlow jobs can be managed using the existing operator provided by Kubeflow. However, when considering the distributed deep learning training jobs based on the parameter server architecture, the scheduling policy used by the existing operator does not consider the task affinity of the distributed training job and does not provide the ability to dynamically allocate or release resources. This can lead to long job completion time and low resource utilization rate. Therefore, in this paper we proposes a new operator that efficiently schedules distributed deep learning training jobs to minimize the job completion time and increase resource utilization rate. We implemented the new operator by modifying the existing operator and conducted experiments to evaluate its performance. The experiment results showed that our scheduling policy improved the average job completion time reduction rate of up to 84% and average CPU utilization increase rate of up to 92%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.799-806
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• 2024

Construction planners for hard rock tunnel projects often encounter practical challenges caused by inherent uncertainties in ground conditions and resource constraints. Therefore, planners cannot rapidly generate optimal excavation schedules for the shortest project durations with a given equipment fleet by considering the uncertainties in ground conditions. Although some schedule optimization methods exist, they are not tailored for resource-constrained hard rock tunnel projects. To overcome these limitations, the authors specified a formal Q-learning-based schedule optimization methodology for resource-constrained hard rock tunnel projects. States are defined according to the locations of tunnel faces under excavation. Actions consist of multiple and comprehensive heuristic-based rules, which are efficient methods for resource allocation. Rewards are the time intervals required between current states and next states. After that, the methodology is validated using a case study. The generated Q tables indicate (1) best actions under different states and (2) the shortest remaining durations when the project starts from specific (state, action) pairs. The results demonstrate that the optimal schedules can be obtained by applying the proposed methodology. Furthermore, it is beneficial for planners to rapidly assign optimal rules for each state under one ground condition scenario. The results further show the potential to consider the uncertainties in ground conditions using the information of possible ground condition scenarios provided.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3821-3841
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• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.87-94
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• 2006

Internet makes it possible to access anytime, anywhere learning and so many LMS(Learning Management Systems) serve web based learning. But LMS has not flexible and qualified metadata to offer customired teaming. So we need extensible and flexible techniques which make if possible to define and share advanced teaming metadata. This paper presents an approach for implementing advanced learning metadata in LMS using RDF and the Semantic Web language. So we will first sketch the learning scenario in Semantic Web environment and structure of metadata management. Next we suggest two types of RDF authoring tool and search RDF documents. Advanced metadata management techniques enables the organization of learning materials around small pieces of semantically annotated learning objects. With these metadata learner can customize learning courses, improve retrieval performances.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.47-52
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• 2023

With the growing data size and the increased computing load in machine learning, energy-efficient resource planning in IoT systems is becoming increasingly important. In this paper, we suggest a new resource planning policy for real-time workloads that can be fluctuated over time in IoT systems. To handle such situations, we categorize real-time tasks into fixed tasks and variable tasks, and optimize the resource planning for various workload conditions. Based on this, we initiate the IoT system with the configuration for the fixed tasks, and when variable tasks are activated, we update the resource planning promptly for the situation. Simulation experiments show that the proposed policy saves the processor and memory energy significantly.

• Management & Information Systems Review
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• v.35 no.2
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• pp.45-66
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• 2016

Firms can make scale of economy and scope of economy by internalizing and using new advanced technology and knowledge from technology cooperation network, decrease risk and cost with partner firm of technology cooperation network, and increase market advantage of product & strengthen firms' position in the market. Due to the advantages of technology cooperation network, the related studies have focused on the positive effect of technology cooperation network. However, the related studies investigating the relationship between technology cooperation network and firm performance have only examined the role of technology cooperation network. Safeguarding mechanism, relationship learning, and relationship performance are categorized into the process of technology cooperation network, and this categorization is applied as resources, capability, and performance by resource based view. The empirical results are reported as belows. First, relationship specific investment and relationship capital positively affect on relationship learning as capability. Second, information sharing, common information understanding, and relationship specific memory development positively affect on long-term orientation, but information sharing has no impact on efficiency and effectiveness. Third, relationship specific investment positively affects on relationship capital and efficiency and effectiveness have positive effects on long-term orientation. Applying technology cooperation network in asymmetric technology dependency with resource based theory, this study suggested the importance of both safeguarding and relationship learning by investigating the relationship among safeguarding, relationship learning, and relationship performance. And it is worthy that this study investigated how firms' behavior change affects relationship performance in the relationship of technology cooperation partner.