• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.764-784
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• 2017

Energy efficiency is an essential requirement in designing a MAC protocol for wireless sensor networks (WSNs) using battery-operated sensor nodes. We proposed a new receiver-initiated MAC protocol, RIX-MAC, based on the X-MAX protocol with asynchronous duty cycles. In this paper, we analyzed the performance of RIX-MAC protocol in terms of throughput, delay, and energy consumption using the model. For modeling the protocol, we used the Markov chain model, derived the transmission and state probabilities, and obtained the equations to solve the performance of throughput, delay, and energy consumption. Our proposed model and analysis are validated by comparing numerical results obtained from the model, with simulation results using NS-2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.514-537
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• 2020

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1481-1501
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• 2019

In opportunistic network, the behavior of a node is autonomous and has social attributes such as selfishness.If a node wants to forward information to another node, it is bound to be limited by the node's own resources such as cache, power, and energy.Therefore, in the process of communication, some nodes do not help to forward information of other nodes because of their selfish behavior. This will lead to the inability to complete cooperation, greatly reduce the success rate of message transmission, increase network delay, and affect the overall network performance. This article proposes a hybrid incentive mechanism (Mim) based on the Reputation mechanism and the Credit mechanism.The selfishness model, energy model (The energy in the article exists in the form of electricity) and transaction model constitute our Mim mechanism. The Mim classifies the selfishness of nodes and constantly pay attention to changes in node energy, and manage the wealth of both sides of the node by introducing the Central Money Management Center. By calculating the selfishness of the node, the currency trading model is used to differentiate pricing of the node's services. Simulation results show that by using the Mim, the information delivery rate in the network and the fairness of node transactions are improved. At the same time, it also greatly increases the average life of the network.

• Journal of Information Technology Services
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• v.21 no.6
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• pp.117-125
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• 2022

In respond to climate change caused by global environmental problems, countries around the world are actively promoting the advancement of new electricity industries. The new energy business is being applied to energy storage systems (ESS), electric vehicle charging business, and power demand response using cutting edge technologies. In 2022, the Korean government is also establishing a policy stance to foster new energy industries and making efforts to improve its responsiveness to power demand response with the innovative technologies. In Korea, attempts to commercialize energy power are also being made in the private and public sectors to control energy power in houses, buildings, and industries. For example, private companies, local governments, and central government are making all-out efforts to develop new energy industry models through joint investment. There are forms such as establishing energy-independent facilities by region, establishing an electric vehicle charging system, controlling urban lighting systems with Information technologies, and managing demand between power suppliers and power consumers. This study examined the business model applied with energy storage system, electric vehicle charging business, smart lighting, and power demand response based on information communication technology to examine the site where smart energy system was introduced. According to this study, company missions and government tasks are suggested to apply new energy business technologies as economical energy solutions that meet the purpose of use by region, industry, and company.

• Journal of Advanced Navigation Technology
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• v.19 no.1
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• pp.66-73
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• 2015

In this paper, an architecture of a perpetual smart video device and its energy control model for the internet of things (IoT) are proposed. The smart video device consists of a processor, an image sensor, a video codec, and a network controller. In the proposed energy control model, energy consumed by image sensing, video encoding, and transmission and energy harvested by solar panels are defined as an input and an output of a battery, an energy buffer. Frame rate, quantization parameter, and operating frequency of processor are defined as the energy control parameters, and these parameters control the input and the output energy of the energy buffer, finally control the energy left in the battery. The proposed energy control model is validated by the energy consumption measurement of the smart phone based platform for various combinations of energy control parameters, and can be used for the design of perpetual smart video device.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3621-3640
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• 2016

In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.136-141
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• 2021

Research on wireless sensor networks has focused on the monitoring and characterization of large-scale physical environments and the tracking of various environmental or physical conditions, such as temperature, pressure, and wind speed. We propose a stochastic mobility model that can be applied to a MANET (Mobile Ad-hoc NETwork). environment, and apply this mobility model to a newly proposed clustering-based routing protocol. To verify its stability and durability, we compared the proposed stochastic mobility model with a random model in terms of energy efficiency. The FND (First Node Dead) was measured and compared to verify the performance of the newly designed protocol. In this paper, we describe the proposed mobility model, quantify the changes to the mobile environment, and detail the selection of cluster heads and clusters formed using a fuzzy inference system. After the clusters are configured, the collected data are sent to a base station. Studies on clustering-based routing protocols and stochastic mobility models for MANET applications have shown that these strategies improve the energy efficiency of a network.

• New & Renewable Energy
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• v.9 no.2
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• pp.15-22
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• 2013

The solar energy are an infinite source of energy and a clean energy without secondary pollution. The global solar energy reaching the earth's surface can be calculated easily according to the change of latitude, altitude, and sloped surface depending on the amount of the actual state of the atmosphere and clouds. The high-resolution solar-meteorological resource map with 1km resolution was developed in 2011 based on GWNU (Gangneung-Wonju National University) solar radiation model with complex terrain. The very high resolution solar energy map can be calculated and analyzed in Seoul and Eunpyung with topological effect using by 1km solar-meteorological resources map, respectively. Seoul DEM (Digital Elevation Model) have 10m resolution from NGII (National Geographic Information Institute) and Eunpyeong new town DSM (Digital Surface Model) have 1m spatial resolution from lidar observations. The solar energy have small differences according to the local mountainous terrain and residential area. The maximum bias have up to 20% and 16% in Seoul and Eunpyung new town, respectively. Small differences are that limited area with resolutions. As a result, the solar energy can calculate precisely using solar radiation model with topological effect by digital elevation data and its results can be used as the basis data for the photovoltaic and solar thermal generation.

• Journal of the Regional Association of Architectural Institute of Korea
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• v.20 no.6
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• pp.81-87
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• 2018

In this paper, in order to maximize the input process efficiency of the building energy simulation field, the authors developed the automatic extraction module of spatial information based BIM geometry information. Existing research or software extracts geometry information based on object information, but it can not be used in the field of energy simulation because it is inconsistent with the geometry information of the object constituting the thermal zone of the actual building model. Especially, IFC-based geometry information extraction module is needed to link with other architectural fields from the viewpoint of reuse of building information. The study method is as follows. (1) Grasp the category and attribute information to be extracted for energy simulation and Analyze the IFC structure based on spatial information (2) Design the algorithm for extracting and reprocessing information for energy simulation from IFC file (use programming language Phython) (3) Develop the module that generates a geometry information database based on spatial information using reprocessed information (4) Verify the accuracy of the development module. In this paper, the reprocessed information can be directly used for energy simulation and it can be widely used regardless of the kind of energy simulation software because it is provided in database format. Therefore, it is expected that the energy simulation process efficiency in actual practice can be maximized.

• Journal of the Korean institute of surface engineering
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• v.43 no.3
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• pp.159-164
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• 2010

Surface roughness of deposited or etched film strongly depends on ion bombardment. Relationships between ion bombardment variables and surface roughness are too complicated to model analytically. To overcome this, an empirical neural network model was constructed and applied to a deposition process of silicon nitride (SiN) films. The films were deposited by using a pulsed plasma enhanced chemical vapor deposition system in $SiH_4$-$NH_4$ plasma. Radio frequency source power and duty ratio were varied in the range of 200-800 W and 40-100%. A total of 20 experiments were conducted. A non-invasive ion energy analyzer was used to collect ion energy distribution. The diagnostic variables examined include high (or) low ion energy and high (or low) ion energy flux. Mean surface roughness was measured by using atomic force microscopy. A neural network model relating the diagnostic variables to the surface roughness was constructed and its prediction performance was optimized by using a genetic algorithm. The optimized model yielded an improved performance of about 58% over statistical regression model. The model revealed very interesting features useful for optimization of surface roughness. This includes a reduction in surface roughness either by an increase in ion energy flux at lower ion energy or by an increase in higher ion energy at lower ion energy flux.