• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.169-174
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• 2014

Practical deployment methods for sensor nodes are demanding as applications using sensor nodes increase. In particular, node connectivity is crucial not only for the network longevity but also for direct impacts on sensing and data collection capability. Economic requirement at building sensor networks and often limited access for sensor fields due to hostile environment force to remain at random deployment from air. However, random deployment often result in lost connection problem and inefficient network topology issue due to node irregularity. In this paper, mixed deployment of key nodes that have better communication capability is proposed to support the original deployment into working in an efficient way. Node irregularity is improved by introducing mixed nodes and an efficient mixed node density is also analyzed. Simulation results show that the mixed deployment method has better performance than the existing deployment methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.55-62
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• 2010

Monitoring systems are able to report certain events at region of interest(ROI) and to take an appropriate action. From industrial product line full of robots to fire detection, intrusion detection, smart grid application, environmental pollution alarm system, monitoring system has widely used in diverse industry sector. Recently, due to advance of wireless communication technology and availability of low cost sensors, intelligent and/or smart monitoring systems such as sensor networks has been developed. Several deployment methods are introduced to meet various monitoring needs and deployment performance criteria are also summarized to be used to identify weak point and be useful at designing monitoring systems. Both efficiency during deployment and usefulness after the deployment should be assessed. Efficiency factors during deployment are elapsed time, energy required, deployment cost, safety, sensor node failure rate, scalability. Usefulness factors after deployment are ROI coverage, connectivity, uniformity, target density similarity, energy consumption rate per unit time and so on.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.1
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• pp.47-58
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• 2019

The paper presents the results of a multi-criteria comparative evaluation of 12 feasible Russian nuclear energy deployment scenarios with thermal and fast reactors in a closed nuclear fuel cycle. The comparative evaluation was performed based on 6 performance indicators and 5 different MCDA methods (Simple Scoring Model, MAVT / MAUT, AHP, TOPSIS, PROMETHEE) in accordance with the recommendations elaborated by the IAEA/INPRO section. It is shown that the use of different MCDA methods to compare the nuclear energy deployment scenarios, despite some differences in the rankings, leads to well-coordinated and similar results. Taking into account the uncertainties in the weights within a multi-attribute model, it was possible to rank the scenarios in the absence of information regarding the relative importance of performance indicators and determine the preference probability for a certain nuclear energy deployment scenario. Based on the results of the uncertainty/sensitivity analysis and additional analysis of alternatives as well as the whole set of graphical and attribute data, it was possible to identify the most promising nuclear energy deployment scenario under the assumptions made.

• Fire Science and Engineering
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• v.20 no.1 s.61
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• pp.55-70
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• 2006

The purpose of this study is to analyze an adequate deployment of fire service force to be prepared to respond appropriately and effectively in Korea by settlement pattern. In order to examine the deployment of fire service force by the present standard, we analyzed the logical basis and the deployment of fire service force by city and province. We also classified clusters for settlement pattern through the statistical methods and raised several points for the existing deployment model of fire service force by the classified settlement pattern. As a result, it was confirmed that the deployment of fire service force by the settlement pattern was irrelevant to fire service need.

• Fire Science and Engineering
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• v.20 no.1 s.61
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• pp.28-42
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• 2006

The purpose of this study is to offer the improvement for deployment of fire service force in Korea by settlement patterns on the basis of analysis for the present standard and deployment of fire service force. For the adequate deployment and operation of fire service force by settlement patterns, we carried out the analysis of the present standard calculated with allocation of the authorized strength. We also classified clusters for settlement pattern through the statistical methods. We proposed the standard for deployment of fire service force reflected with environmental and need factors through the introduction of standardized index.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.189-192
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• 1997

New product development is a complex managerial process which involves multiple functional groups, each with a different perspective. Quality function deployment (QFD) is a new product development process which stresses cross-functional integration. QFD provides a specific approach for ensuring quality through each stage of the product development and production process. This paper provides an overview of QFD including its concepts and methods, and then proposes an integrated approach to formulating and solving the QFD process. This paper also discusses issues associated with the prescriptive modeling of QFD.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.462-483
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• 2016

The research on optimization of cellular network's energy efficiency (EE) towards environmental and economic sustainability has attracted increasing attention recently. In this survey, we discuss the opportunities, trends and challenges of this challenging topic. Two major contributions are presented namely 1) survey of proposed energy efficiency metrics; 2) survey of proposed energy efficient solutions. We provide a broad overview of the state of-the-art energy efficient methods covering base station (BS) hardware design, network planning and deployment, and network management and operation stages. In order to further understand how EE is assessed and improved through the heterogeneous network (HetNet), BS's energy-awareness and several typical HetNet deployment scenarios such as macrocell-microcell and macrocell-picocell are presented. The analysis of different HetNet deployment scenarios gives insights towards a successful deployment of energy efficient cellular networks.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.447-466
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• 2021

Purpose: This article aims 1) to propose Quality 4.0 concept through surveying related literature, 2) to suggest key elements of Quality 4.0 by arranging the elements of Quality 4.0 that appeared in the literature, 3) to determine the levels of Quality 4.0, and 4) to suggest ideas for effective deployment of Quality 4.0. Methods: Eleven papers or documents are reviewed for Quality 4.0 concept; two papers and one document are investigated for key element extraction of Quality 4.0; and smart factory roadmap and industry 4.0 maturity model are studied to determine the levels of Quality 4.0. Results: 1) Quality 4.0 definition is proposed. 2) Three key elements are determined: data acquisition and analytics, connection and integration, and leadership and culture. 3) Six Quality 4.0 levels are determined. 4) Some suggestions are addressed for effective deployment of Quality 4.0. Conclusion: 1) Definition, key elements, levels, and some suggestions on effective deployment of Quality 4.0 are addressed. 2) Specific contents of Quality 4.0 education and training courses should be provided in the future. 3) Two future research directions are proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2179-2188
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• 1994

In this paper, we studied the deployment of AIN(Advanced Intelligent Network) that was recommended by ITU-T there are five methods of deployment. Here we divided these methods into two types. One is the direct connection with SCP(Service Control Point) and IP(Intelligent Peripheral) via No.7 and the other is the indirect connection with SCP and IP Through SSP(Service Switching Point). First, we suggested the structure of AIN SSP and defined S/W functional blocks. Second, we proposed H/W functional extension for interaction with IP. On the basis of the suggested structure, we mathematically evaluated and simulated two types of the deployment of AIN. In the result, we knew that, in comparison with the indirect method, the direct method was effective.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.