• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.8
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• pp.573-578
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• 2010

Topological matrix which reflects characteristics of network connectivity has been widely used in efficient solving for complicated flow network. Using topological matrix, one can easily define continuity at each node of flow network and make algorithm to automatically generate continuity equation of matrix form. In order to analyze flow network completely it is required to satisfy energy conservation in closed loops of flow network. Fundamental cycle retrieving algorithm based on graph theory automatically constructs energy conservation equation in closed loops. However, it is often accompanied by NP-complete problem. In addition, it always needs fundamental cycle retrieving procedure for every structural change of flow network. This paper proposes alternative mathematical method to analyze flow network without fundamental cycle retrieving algorithm. Consequently, the new mathematical method is expected to reduce solving time and prevent error occurrence by means of simplifying flow network analysis procedure.

• The KIPS Transactions:PartC
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• v.15C no.5
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• pp.329-342
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• 2008

The Sensor Networks have limitations in utilizing energies, developing energy-efficient routing protocol and secure routing protocol are important issues in Sensor Network. In the field of data management, Tributaries and Deltas(TD) which incorporates tree topology and multi-path topology effectively have been suggested to provide efficiency and robustness in data aggregation. And our research rendered hierarchical property to TD and proposed Clustering-based Tributaries-Deltas. Through this new structure, we integrated efficiency and robustness of TD structure and advantages of hierarchical Sensor Network. Clustering-based Tributaries-Deltas was proven to perform better than TD in two situations through our research. The first is when a Base Station (BS) notices received information as wrong and requests the network's sensing data retransmission and aggregation. And the second is when the BS is mobile agent with mobility. In addition, we proposed key establishment mechanism proper for the newly proposed structure which resulted in new Sensor Network structure with improved security and energy efficiency as well. We demonstrated that the new mechanism is more energy-efficient than previous one by analyzing consumed amount of energy, and realized the mechanism on TmoteSKY sensor board using TinyOS 2.0. Through this we proved that the new mechanism could be actually utilized in network design.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.248-250
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• 2001

In this paper, marginal loss factors are calculated for 12 load cases that represent the impact of marginal network tosses on nodal prices at the transmission network connection points at which generators are located. Based on comparison analysis of marginal loss factors on generation energy resources, we can find the characteristics of each plants according to its energy resources in KOREA.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.1433-1434
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• 2004

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.37-42
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• 2007

This paper reconsiders established power conservation models for ubiquitous sensor networks that use relay nodes instead of direct communication and proposes novel network power consumption model with consideration of the channel level and radio chip level simultaneously. We estimate the effect of minimum hop-count policy in terms of network power consumption through simulation of various situations for low power RF module CC2420. It is observed that maximum RF power and minimum hop-count results in lower energy consumption relatively. Also, in total network energy consumption, which is included re-transmission, minimum hop count policy presents decrease by 33.1% of energy consumption in compare with the conventional model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2800-2807
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• 2012

An ad-hoc wireless network provides self-organizing data networking while they are routing of packets among themselves. Typically multi-hop and control packets overhead affects the change of route of transmission. There are numerous routing protocols have been developed for ad hoc wireless networks as the size of the network scale. Hence the scalable routing protocol would be needed for energy efficient various network routing environment conditions. The number of depth or layer of hierarchical clustering nodes are analyzed the different clustering structure with topology in this paper. To estimate the energy efficient number of cluster layer and energy dissipation are studied based on distributed homogeneous spatial Poisson process with context-awareness nodes condition. The simulation results show that CACHE-R could be conserved the energy of node under the setting the optimal layer given parameters.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.87-91
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• 2019

WSN is a wirelessly configured network of sensor nodes with limited power such as batteries. If the sensor node's battery is exhausted, the node is no longer available. Therefore, if the network is to be used for a long time, energy consumption should be minimized. There are many Wireless Sensor Network Protocols to improve energy efficiency, including Cluster-based and chain-based Protocols. Cluster-based Protocols elect Cluster Heads and divide sensor field into Clusters. The Cluster Head collects the data in the Cluster and transmits it to the Base Station. In the case of nodes elected as Cluster Heads, there is a problem of energy consumption. The chain-based Protocol links sensor nodes in a chain and finally transmits all data to the Base Station. In this paper, we intend to increase the network lifetime by using a chain to reduce the energy consumption of the Cluster Head in the Cluster-based Protocol, LEACH Protocol.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.2
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• pp.134-142
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• 2022

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.125-131
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• 2010

In wireless sensor networks application, sensor nodes are randomly deployed in wide and opened environment typically. Since sensor networks have these features, it is vulnerable to physical attacks in which an adversary can capture deployed nodes and use them to inject a fabricated report into the network. This threats of network security deplete the limited energy resource of the entire network using injected fabricated reports. A dynamic en-route filtering scheme is proposed to detect and drop the injected fabricated report. In this scheme, node executes the key redistribution to increases the detection power. It is very important to decide the authentication key redistribution because a frequent key redistribution can cause the much energy consumption of nodes. In this paper, we propose a key redistribution determining method to enhance the energy efficiency and maintain the detection power of network. Each node decides the authentication key redistribution using a fuzzy system in a definite period. The proposed method can provide early detection of fabricated reports, which results in energy-efficiency against the massive fabricated report injection attacks.