• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.124-130
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• 2023

In this paper, we intend to improve the network lifetime by improving the energy efficiency of sensor nodes in a wireless sensor network by utilizing machine learning using K-means clustering algorithm. A wireless sensor network is a wireless network composed of physical devices including batteries as physical sensors. Due to the characteristics of sensor nodes, all resources must be efficiently used to minimize energy consumption to maximize network lifetime. A cluster based approach is used to manage groups of relatively large numbers of nodes. In the proposed protocol, by improving the existing LEACH algorithm, we propose a clustering algorithm that selects a cluster head using a cluster based approach and a location based approach. The performance results to be improved were measured using Matlab simulation. Through the experimental results, K-means clustering was applied to the energy efficiency part. By utilizing K-means, it is confirmed that energy efficiency is improved and the lifetime of the entire network is extended.

• Journal of Pharmacopuncture
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• v.24 no.3
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• pp.138-141
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• 2021

Objectives: This study aims to describe the utilization of herbal formulas from Wenrejingwei by using network analysis and understand the treatment of acute exogenous febrile diseases. Methods: We constructed a matrix of high-frequency herbal combinations (HCs) from Wenrejingwei and Shang Han Lun and cluster networks based on cohesive analysis. Network analysis was performed to compare the results. Results: The results of the high-frequency HC network in Wenrejingwei showed cohesive patterns in three categories corresponding to dampness-heat and warm-fever treatment. Compared to the Shang Han Lun network, the Wenrejingwei network indicated a careful approach in the use of pungent and warm herbs such as Guizhitang. Moreover, the combination of Scutellaria baicalensis and Coptis chinensis along with the use of herbs strengthening yin, such as Ginseng Radix and Liriopes Radix, provide evidence of a holistic approach in the treatment of exogenous febrile diseases by considering the balance of the human body damaged by heat. Conclusion: The results of this study could help select appropriate herbal formulas and treatment methods for treating Onbyeong and modern acute febrile infectious diseases.

• Journal of the Korean Society of Systems Engineering
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• v.4 no.2
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• pp.45-54
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• 2008

This paper describes an operational concept of the network based future airborne force power using a systems engineering approach. The battlefield is changing to new system of systems that command and control by the network based BM/C4ISR. Also, it is composed of various sensors and shooters in an single theater. Future threats may be characterized as unmanned moving bodies that the strategic effect is great such as UAVs, cruise missile and tactical ballistic missiles. New threats such as low altitude stealth cruise missile may also appear. The implementation of future airborne force power network systems against these future threats is required to complex and integrated approach based on systems engineering. This work developed the operational concepts of the future airborne network system, and then derived the requirements for performing missions effectively. In addition, the scheme of future airborne force power network systems is presented.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.213-220
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• 2014

This study suggests new approach to identify core technologies through patent analysis. Specially, the approach applied data mining technique and multi-criteria decision making method to the co-classification information of registered patents. First, technological interrelationship matrices of intensity, relatedness, and cross-impact perspectives are constructed with support, lift and confidence values calculated by conducting an association rule mining on the co-classification information of patent data. Second, the analytic network process is applied to the constructed technological interrelationship matrices in order to produce the importance values of technologies from each perspective. Finally, data envelopment analysis is employed to the derived importance values in order to identify priorities of technologies, putting three perspectives together. It is expected that suggested approach could help technology planners to formulate strategy and policy for technological innovation.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1685-1688
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• 2002

Application Routing Load Balancing (ARLB) is a novel load balancing mode that combines QoS routing and load balancing in per application to support QoS far real-time application based on wired network. Zone Routing Protocol (ZRP) is a recent hybrid proactive/reactive routing approach in an attempt to achieve scalability of ad-hoc network. This routing approach has the potential to be efficient in the generation of control traffic than traditional routing schemes. Up to now, without proper load balancing tools, the ZRP can actually guarantee QoS for delay-sensitive applications when congestion occurred in ad-hoc network. In this paper, we propose the ARLB to improve QoS fur delay-sensitive applications based on ZRP in ad-hoc network when congestion occurred and to be forwarding mechanism fur route coupling to support QoS for real-time applications. The critical point is that the routing metric of ARLB is originally designed for wired network environment. Therefore, we study and present an appropriate metric or cost computation routing of ARLB for recently proposed ZRP over ad-hoc network environment.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.777-782
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• 2011

Neural network technique is widely employed in the fields of signal processing, control systems, pattern recognition, etc. Learning of neural networks is an important procedure to accomplish dynamic system modeling. This paper presents a novel learning approach for differential neural network models based on the Kalman-Bucy filter theory. We construct an augmented state vector including original neural state and parameter vectors and derive a state estimation rule avoiding gradient function terms which involve to the conventional neural learning methods such as a back-propagation approach. We carry out numerical simulation to evaluate the proposed learning approach in nonlinear system modeling. By comparing to the well-known back-propagation approach and Kalman-Bucy filtering, its superiority is additionally proved under stochastic system environments.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.679-688
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• 2003

In order to navigate the mobile robots safely in unknown environments, many researches have been studied to devise navigational algorithms for the mobile robots. In this paper, we propose a navigational algorithm that consists of an obstacle-avoidance behavior module, a goal-approach behavior module and a radial basis function network(RBFN) supervisor. In the obstacle-avoidance behavior module and goal-approach behavior module, the fuzzy-artificial immune networks are used to select a proper steering angle which makes the autonomous mobile robot(AMR) avoid obstacles and approach the given goal. The RBFN supervisor is employed to combine the obstacle-avoidance behavior and goal-approach behavior for reliable and smooth motion. The outputs of the RBFN are proper combinational weights for the behavior modules and velocity to steer the AMR appropriately. Some simulations and experiments have been conducted to confirm the validity of the proposed navigational algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2848-2869
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• 2017

Mobile Wireless Sensor Networks (MWSN) are usually constrained in energy supply, which makes energy efficiency a key factor to extend the network lifetime. The management of the network topology has been widely used as a mechanism to enhance the lifetime of wireless sensor networks (WSN), and this work presents an alternative to this. Software Defined Networking (SDN) is a well-known technology in data center applications that separates the data and control planes during the network management. This paper proposes a solution based on SDN that optimizes the energy use in MWSN. The network intelligence is placed in a controller that can be accessed through different controller gateways within a MWSN. This network intelligence runs a Topology Control (TC) mechanism to build a backbone of coordinator nodes. Therefore, nodes only need to perform forwarding tasks, they reduce message retransmissions and CPU usage. This results in an improvement of the network lifetime. The performance of the proposed solution is evaluated and compared with a distributed approach using the OMNeT++ simulation framework. Results show that the network lifetime increases when 2 or more controller gateways are used.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.493-496
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• 2000

We study the problem of designing at minimum cost a two-connected network topology such that the shortest cycle to which each edge belongs does not exceed a given maximum number of hops. This problem is considered as part of network planning and arises in the design of backbone networks. We propose a genetic algorithm approach that uses a solution representation, in which the connectivity and ring constraints can be easily encoded. We also propose a crossover operator that ensures a generated solution is feasible. By doing so, the checking of constraints is avoided and no repair mechanism is required. We carry out experimental evaluations to investigate the solution representation issues and GA operators for the network design problem.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.130-138
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• 1995

In this paper, a Novel Approach is Proposed which Identifies linear system Parameters Using a multilayer feedforward neural network trained with backpropagation algorithm. The parameters of linear system can be represented by x9t)/x(t) and x(t)/u(t). Thud, its parameters can be represented in terms of the derivative of output with respect to input of parameters can be represented in terms of the derivative of output with respect to input of trained neural network which is a function of weights and output of neurons. Mathematical representation of the proposed approach is derived, and its validity is shown by simulation results on 2-layer and 3-layer neural network.