• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.1
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• pp.41-49
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• 2008

Many machineries and equipments are being changing to various and complicated by development of recent technology and arrival of convergence age in distant future. These various and complicate equipments need more precise outcomes and low-power consumption sensors to get close and exact results. In this paper, we proposed fault tolerance and feedback theorem for sensor network and MEMS circuit which has a benefit of energy efficiency through wireless sensor network. The system is provided with independent sensor communication if possible as unused action, using idle condition of system and is proposed the least number of circuits. These technologies compared system efficiency after examining product of each Moving Distance by developed sensor which gives effects to execution of system witch is reduced things like control of management side and requirement for hardware, time, and interaction problems. This system is designed for practical application; however, it can be applied to a normal life and production environment such as "Ubiquitous City", "Factory Automata ion Process", and "Real-time Operating System", etc.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.35-42
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• 2011

Link quality assessment is a crucial part of sensor network formation to stably operate large-scale wireless sensor networks (WSNs). A stability of path consisting of several nodes strongly depends on all link quality between pair of consecutive nodes. Thus it is very important to assess the link quality on the stage of building a routing path. In this paper, we present a link quality assessment method, Hybrid Link Quality Metric (HQLM), which uses both of LQI and RSSI from RF chip of sensor nodes to minimize set-up time and energy consumption for network formation. The HQLM not only reduces the time and energy consumption, but also provides complementary cooperation of LQI and RSSI. In order to evaluate the validity and efficiency of the proposed method, we measure PDR (Packet Delivery Rate) by exchanging multiple messages and then, compare PDR to the result of HQLM for evaluation. From the research being carried out, we can conclude that the HQLM performs better than either LQI- or RSSI-based metric in terms of recall, precision, and matching on link quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.435-444
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• 2019

Recently, DL (Deep Learning) has been rapidly applied in various fields. In particular, classification and object recognition from images are major tasks in computer vision. Most of the DL utilizing imagery is primarily based on the CNN (Convolutional Neural Network) and improving performance of the DL model is main issue. While most CNNs are involve with images for training data, this paper aims to classify and recognize objects using DSM (Digital Surface Model), and slope and aspect information derived from the DSM instead of images. The DSM data sets used in the experiment were established by DGPF (German Society for Photogrammetry, Remote Sensing and Geoinformatics) and provided by ISPRS (International Society for Photogrammetry and Remote Sensing). The CNN-based SegNet model, that is evaluated as having excellent efficiency and performance, was used to train the data sets. In addition, this paper proposed a scheme for training data generation efficiently from the limited number of data. The results demonstrated DSM and derived data could be feasible for semantic classification with desirable accuracy using DL.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.679-684
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• 2005

There are a few representative wireless network access technologies used widely. WWAN is celluar based telecommunication networks supporting high mobility, WLAN ensures high data rate within hotspot coverage, and WDMB support both data and broadcasting services correspondingly. However, these technologies include some limitations especially on the mobility, data rate, transmission direction, and so on. In order to overvome these limitations, there are various studies have been proposed in terms of 'Vortical Handoff' that offers seamless connectivity by switching active connection to the appropriate interface which installed in the mobile devices. In this paper, we propose the interface selection algorithm and network architecture to maximize the life time of entire system by minimizing the unnecessary energy consumption of another interfaces such as WLAN, WDMB that are taken in the user equipment. In addition, by using the results of analyzing multiple types of traffic and managing user buffer as a metric for vertical handoff, we show that the energy efficiency of our scheme is $75\%$ and $34\%$ than typical WLAN for WDMB and WLAN preferred schemes, correspondingly.

• The KIPS Transactions:PartC
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• v.17C no.6
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• pp.471-484
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• 2010

In the wireless communication systems, the power consumption of a mobile device is very important issue due to its battery limitations. Hence most of the standards for wireless networks including a mobile WiMAX system are supporting their own power saving mode in way that a mobile device is able to reduce its energy usage while in the mode. However, those standards just define the arrangement of special time intervals, called a paging listening interval, during which the device needs to receive the paging-related control messages, and they do not specify how to effectively reduce the power in many different network environments. This means the amount of power spent by the device is very dependent on the implementations of individual device-vendors, and undesirable paging loss may happen according to the channel conditions. To reduce unnecessary power usage and the risk of paging loss, this paper proposes the effective frequency/BS selection algorithm applicable to a mobile device operating in the power saving mode, which serves the device with better BS based on the tracking for channel variation. This algorithm consists of the channel estimation phase during each paging listening interval, the tracking phase for the measured results, the frequency reselection phase based on the tracking activity, and the preferred BS reselection phase. Thus the proposed method can improve the paging performance while the device is moving in the network. Also the simulation result shows that the presented scheme is superior to other candidates in energy efficiency due to the channel-adaptive frequency/BS selection.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.60-68
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• 2012

In recent years, the requirements on the high quality environment monitoring by using the sensor nodes which can handle the multimedia data in WSN have been increased. However, because the volume of multimedia data is tremendous, the limited bandwidth of a wireless channel may incur the bottleneck of a system. To solve such a problem, most of the existing distributed multi-path routing protocols based on multimedia data just focused on overcoming the limited bandwidth in order to enhance the energy efficiency and the transmission rate. However, because the existing methods can not apply a key-based technique to encrypt the multimedia data, they are very weak for the security. In this paper, we propose a secure disjointed multipath routing scheme for multimedia data transmission. Since our proposed scheme divides multimedia data(eg. image) into pixels and sends them through disjointed multipath routing, it can provide security to the whole network without using the key-based method. Our experimental results show that our proposed scheme reduces about 10% the amount of the energy consumption and about 65% the amount of the missed data packets caused by malicious nodes over the existing methods on average.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.55-66
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• 2012

The practical way to minimize the greenhouse gas is to reduce the emission of carbon dioxide. For this reason, CCS(Carbon Capture and Storage) technology, which could reduce carbon dioxide emission, has risen as a realistic alternative in recent years. In addition, the researcher is recently working into ways of applying CCS technologies with deep saline aquifer. In this study, the evaluation model on the feasibility of $CO_2$ sequestration in the deep saline aquifer using ANN(Artificial Neural Network) was developed. In order to develop the efficiency-evaluation model, basic model was created in the deep saline aquifer and sensitivity analysis was performed for the aquifer characteristics by utilizing the commercial simulator of GEM. Based on the sensitivity analysis, the factors and ranges affecting $CO_2$ sequestration in the deep saline aquifer were chosen. The result from ANN training scenario were confirmed $CO_2$ sequestration by solubility trapping and residual trapping mechanism. The result from ANN model evaluation indicated there is the increase of correlation coefficient up to 0.99. It has been confirmed that the developed model can be utilized in feasibility of $CO_2$ sequestration at deep saline aquifer.

• Journal of Life Science
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• v.28 no.10
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• pp.1244-1253
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• 2018

Rumen microbiome consists of a wide variety of microorganisms, such as bacteria, archaea, protozoa, fungi, and viruses, that are in a symbiotic relationship in a strict anaerobic environment in the rumen. These rumen microbiome, a vital maker, play a significant role in feed fermentation within the rumen and produce different volatile fatty acids (VFAs). VFAs are essential for energy metabolism and protein synthesis of the host animal, even though emission of methane gas after feed fermentation is considered a negative indicator of loss of dietary energy of the host animal. To improve rumen microbial efficiency, a variety of approaches, such as feed formulation, the addition of natural feed additives, dietary feed-microbes, etc., have taken to increase ruminant performance. Recently with the application of high-throughput sequencing or next-generation sequencing technologies, especially for metagenomics and metatranscriptomics of rumen microbiomes, our understanding of rumen microbial diversity and function has significantly increased. The metaproteome and metabolome provide deeper insights into the complicated microbial network of the rumen ecosystem and its response to different ruminant diets to improve efficiency in animal production. This review summarized some recent advances of rumen microbiome techniques, especially "meta-omics," viz. metagenomic, metatranscriptomic, metaproteomic, and metabolomic techniques to increase feed fermentation and utilization in ruminants.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.27-40
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• 2016

This paper aims to analyze the business types of 237 IoT based smart home companies in the world (launched during 1999~2014) which got global investment last few years. For this, the previous literatures trying to analze technology and service types of smart home are searched and the typology of the platform is discussed. Based on it, this research conceptualizes an analysis framework that includes three areas of smart home like home automation, home security, and energy efficiency with the three platform types like product, software, and service. This study concludes that the development of business type for IoT based smart home ecosystem is from the product to software and it can be a platform or not. In current status, there are a few platforms of product and software, but in the device management (16%) and thermostat (11%), companies are persuing more platform like. It is difficult to find the service platform in overall areas, for application based service has a few attractions in the investment market due to the lack of cloud infrastructure and data analytics. The following three are the implication to domestic market: 1) More active offering of API and SDK, 2) more active introduction of wireless Intenet network protocols, and 3) more active interoperability efforts and alliance activities are needed.