• Journal of Korean Society of Industrial and Systems Engineering
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• 제42권1호
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• The KIPS Transactions:PartC
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• 제14C권7호
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• pp.583-588
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• 2007

It is easy to implement a temporary network with a mobile ad-hoc network in which mobile nodes have without using a infrastructure network. They depend on their limited power. Recently, it is a hot issue to save the energy in a mobile ad-hoc network because a mobile nodes have a limited energy. Research of IEEE 802.11 PSM was proposed in a single hop ad-hoc assumption. If IEEE 802.11 PSM is applied to multi hop ad-hoc network, non-optimal paths will be generated by the mobile nodes which didn't receive a message of routing request. Non-optimal paths increase not only a network latency but also energy consumption of mobile nodes. Reconfiguring algorithm of non-optimal paths caused by the mobile nodes which didn't receive a message of routing request is proposed in this paper. A mobile node can overhear the data in his range. A wireless medium is shared by all mobile nodes using the same bandwidth. All mobile nodes lookout the non-optimal paths with these properties of a medium, if non-optimal path is generated, optimal reconfiguring will be accomplished by modifying routing table of itself or sending a request message of routing update to nearby nodes. By reconfiguring the non-optimal paths to optimized ones, network latency and energy consumption was decreased. It is confirmed to ignore the overhead caused by a algorithm presented in this paper through the result of the simulation.

• Journal of the Institute of Electronics and Information Engineers
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• 제49권12호
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• pp.56-63
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• 2012

The WBAN technology means a short distance wireless network which provides each device interactive communication by connecting devices inside and outside of body. Standardization on the physical layer, data link layer, network layer and application layer is in progress by IEEE 802.15.6 TG BAN. Wireless body area network is usually configured in energy efficient using sensor and zigbee device due to the power limitation and the characteristics of human body. Wireless sensor network consist of sensor field and sink node. Sensor field are composed a lot of sensor node and sink node collect sensing data. Wireless sensor network has capacity of the self constitution by protocol where placed in large area without fixed position. Mobile sink node distribute energy consumption therefore network life time was increased than fixed sink node. The energy efficient is important matter in wireless body area network because energy resource was limited on sensor node. In this paper we proposed cluster topology algorithm for efficient data transmission in wireless body area network based mobile sink. The proposed algorithm show good performance under the advantage of grid routing protocol and TDMA scheduling that minimized overlap area on cluster and reduced amount of data on cluster header in error prone wireless sensor network based on mobile sink.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 한국에너지공학회 2010년도 춘계학술 발표회
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• pp.95-103
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.1529-1530
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• 2011

• Journal of the HCI Society of Korea
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• 제5권2호
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• pp.7-14
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• 2010

Urban Data collected through Sensor Network is becoming crucial to understand and analyse a city. Thus, the Ubiquitous Sensor Network builds the foundation of the u-City development. This research aims to develop an energy monitoring application with an intuitive visualization environment which integrates energy usage information on top of urban geospatial information. Such a system will be able to facilitate effective energy supply plan at the early stages of urban planning, and eventually to encourage citizens to conserve energy by giving them real time monitoring information in an easy to understand visual environment. The system provides multiple layers of energy-related information coupled with the geospatial information layer in order to accommodate multiple viewpoints. On the other hand, the system provides logical Level of Detail control based on urban spatial information hierarchy. We defined the system concept and functions, and designed the data structure and the methods of information visualization. This paper presents the visualization methods, data structure, interactions scenarios which combines spacial information, E-GIS data and the energy related sensor data. Furthermore this research tries to introduce the concept of Social Sensor Networking to enhance the monitoring quality.

• Journal of Energy Engineering
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• 제29권3호
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• pp.86-90
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• 2020

This paper examines a blockchain network-based transaction system using location proofing in power direct transactions between networked energy clouds, energy communities, and prosumer machines participating in smart cities. It utilizes location-based blockchain network technology, which enables long-distance travel with recharging by power purchases during autonomous movements, autonomous electric vehicles that can purchase and sell electricity, and solar street lights that can be produced and sold in fixed form. In addition, it is possible to provide optimum power transaction matching and settlement reliability between machines without human intervention in power transactions between electric chargers. It also introduces a business-to-object business model between autonomous machines that exist in multiple and different spaces and through energy clouds that are expected to be scattered with various transaction prices, policies, and incentives.

• Journal of Korea Society of Industrial Information Systems
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• 제16권1호
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• pp.31-36
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• 2011

Ubiquitous network and wireless sensor networks is being applied in various fields. Located at target areas, node of wireless sensor network uses batteries as a power source. Batteries have a limited energy in sensor network applications. Also, before use, the battery must be charged and It is difficult to replace the battery. Therefore, energy harvesting technology is being researched and being developed for long life of sensor node. Especially, sola energy is being extensively researched. because that can have great amounts of energy than other environmental energy in a short time. In this study, we tested battery charging and recharging, operation of sensor node using Solar Cell. Also, monitoring data gathering and voltage Analysis showed energy harvesting time of Sola Cell battery for sensor node and operation of sensor node.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.457-459
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• 2018

Solar power generation system is a energy generation technology that produces electricity from solar power, and it is growing fastest among renewable energy technologies. It is of utmost importance that the solar power system supply energy to the load stably. However, due to unstable energy production due to weather and weather conditions, accurate prediction of energy production is needed. In this paper, an Artificial Neural Network(ANN) that predicts solar energy using 15 kinds of meteorological data such as precipitation, long and short wave radiation averages and temperature is implemented and its performance is evaluated. The ANN is constructed by adjusting hidden parameters and parameters such as penalty for preventing overfitting. In order to verify the accuracy and validity of the prediction model, we use Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) as performance indices. The experimental results show that MAPE = 19.54 and MAE = 2155345.10776 when Hidden Layer $Sizes=^{\prime}16{\times}10^{\prime}$.