• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.10
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• pp.622-624
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• 2014

In this letter, the joint mode selection and resource allocation method is proposed for D2D communication underlaying OFDMA based cellular networks. In the proposed scheme, D2D mode possible region is determined which satisfies QoS. Then we solve the optimization problem utilizing primal-dual algorithm. The proposed scheme shows better performance than conventional schemes.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.1
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• pp.51-58
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• 2011

Extensible processors provide an efficient mechanism to boost the performance of the whole system without losing much flexibility. However, due to the intense demand of low cost and power consumption, customizing an embedded system has been more difficult than ever. In this paper, we present a framework for custom instruction generation considering both area constraints and resource sharing. We also present how we can speed up the process through pruning and library-based design space exploration.

• 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.

• Journal of English Language & Literature
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• v.57 no.6
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• pp.911-938
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• 2011

The present study examined the effect of two variables of task complexity, reasoning demand and time pressure, each from the resourcedirecting and resource-dispersing dimension in Robinson's (2001) framework of task classification. Reasoning demand was operationalized as the two types of texts to read and summarize, expository and argumentative. Time pressure was operationalized as the two modes of performance, oral and written. Six university students summarized the two types of text orally and twenty four students from the same school summarized them in the written form. Results from t test and ANCOVA showed that in the oral mode, reasoning demand tends to heighten the complexity of the language used in the summary in competition with accuracy but such an effect disappeared in the written mode. It was interpreted that the degree of time pressure is not the only difference between the oral and written modes but that the two modes may be fundamentally different cognitive tasks, and that Robinson's (2001) and Skehan's (1998) models were differentially supported by the oral mode of tasks but not by the written mode of the tasks.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.16-26
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• 2024

Mobile edge computing (MEC) plays a crucial role in improving the performance of resource-constrained mobile devices by offloading computation-intensive tasks to nearby edge servers. However, existing methods often neglect the critical consideration of future task requirements when making offloading decisions. In this paper, we propose an innovative approach that addresses this limitation. Our method leverages recurrent neural networks (RNNs) to predict task sizes for future time slots. Incorporating this predictive capability enables more informed offloading decisions that account for upcoming computational demands. We employ genetic algorithms (GAs) to fine-tune fitness functions for current and future time slots to optimize offloading decisions. Our objective is twofold: minimizing total processing time and reducing energy consumption. By considering future task requirements, our approach achieves more efficient resource utilization. We validate our method using a real-world dataset from Google-cluster. Experimental results demonstrate that our proposed approach outperforms baseline methods, highlighting its effectiveness in MEC systems.

• Journal of Information Technology Services
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• v.10 no.4
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• pp.149-169
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• 2011

As the number of smart phone users increases, many organizations begin to adopt social media rapidly to diversify communication channels with customers. Specifically, twitter, which supports instant and two-way communications between users and between organizations and users, has been adopted by many organizations as an efficient way not only to identify new customers but also to retain existing customers. However, little attention has been given to the issue on how organizations can effectively use twitter to improve customer satisfaction. To explore the issue, this study proposes two major dimensions, customer participation and organization resource utilization, which should be considered in building a utilization strategy for twitter in organizations. We then develop four different combinations along with these dimensions-follow, mention, retweet, and review types. Based on case studies of 27 organizations that use twitter, we evaluate the degrees of customer participation, resource utilization, and customer satisfaction, and examine matching or mismatching of the adoption purpose of twitter and its actual utilization. The study results reveal that organizations in the matching group show higher customer satisfaction that those in the mismatching group. This study sheds new light on twitter research by developing a new conceptual framework and using a case study approach to explore the relationship between the utilization strategy of twitter and customer satisfaction.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.3
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• pp.69-75
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• 2003

NAPRT(Naming Authority Pointer) is a type of resource record specified in IETF RFC 2915. NAPTR enables to register various services in the Domain Name Systems and thus provides a way to discover services available on specific hosts. This paper describes the design and implementation of a Proxy DNS system aimed at supporting NAPTRs. The goal of this work is to study on the feasibility of the service discovery registered in DNS via NAPTR records and provides the result for simplicity and extensibility of implementation through the implementation of a actual Test-bed system This research result can be applied to service discovery in the resource information management for high performance GRE environments as well as to the implementation of DNS infrastructure for the ENUM.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.1891-1908
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• 2011

Cyber-physical systems (CPSs) are an emerging discipline that involves engineered computing and communicating systems interfacing the physical world. The widespread applications of CPSs still face enormous challenges because of the lack of theoretical foundations. In this technical survey, we review state-of-the-art design techniques from various angles. The aim of this work is to provide a better understanding of this emerging multidisciplinary methodology. The features of CPSs are described, and the research progress is analyzed using the following aspects: energy management, network security, data transmission and management, model-based design, control technique, and system resource allocation. We focus on CPS resource optimization, and propose a system performance optimization model with resource constraints. In addition, some classic applications (e.g., integrating intelligent road with unmanned vehicle) are provided to show that the prospects of CPSs are promising. Furthermore, research challenges and suggestions for future work are outlined in brief.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.12
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• pp.962-966
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• 2013

The internet will should require the QoS(quality of service) guarantees that the real-time traffic like as audio and video data will be operated well in the future. We had designed Q-MOTP which transfer multimedia object data and proved excellent performance. This paper describes an adaptive QoS management architecture and mechanism based on the information of system resources. Resource manager reports the system resource information periodically or when resources are in the overload state, or on demand by the QoS manager. By using this information, the QoS manager can predict QoS degradation and perform CAC.