• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.95-100
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• 2015

These days, satellite core technologies are being developed as a way to provide various information by considering simultaneously sending, wide area covering, highly precide, and anti-disaster technologies. Not only global positioning, and image but also space launcher, satellite bus, satellite payload, earth station are being convergently developed in a different technological field. Especially, it is required a lot of initial investing expenditure to provide the Earth observational information service based on the space technologies. Such a trend and change of satellite technologies Korea has realized the necessity for the domestic independent development of next generation earth observation satellites, and are preparing the profound items such as a detailed implementation plan for the efficient development project. Like the satellite advanced countries, it should be transparently carried out that an efficient implementation of the developing target related to the geostationary earth observation satellite development, establishment of technological auditing function and quality assurance system, implementation plan, progressing courses and results of the satellite development program by way of planning, evaluation and management. For these things cited above, it is necessary to operate systematically and continuously the professional structural system by the governmental department in order to control the geostationary earth observation satellite development project. Therefore, this study proposes a development project management improvement method of the Korea next generation geostationary earth observation satellite based on the development project management system of the domestic geostationary satellite system.

• Journal of IKEEE
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• v.24 no.1
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• pp.347-353
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• 2020

In this paper, the durability of an electromagnetic metamaterial absorber is verified in a temperature varying condition mimicking a maritime environment for the purpose of applying it to reduce the radar cross section of an integrated mast of the next-generation destroyer. To validate the durability, the reflectance of the electromagnetic metamaterial absorber was measured after storing it in a chamber that can control the temperature according to Procedure I of Method 501.7 included in MIL-STD-810H. Before and after the environmental test, both of the measured reflectances were retained less than -10 dB over the X band, that can guarantee the stealth functionality.

• Research in Plant Disease
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• v.21 no.2
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• pp.74-81
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• 2015

The specific and sensitive nested-PCR method to detect Acidovorax citrulli, a causal agent of bacterial fruit blotch on cucurbitaceae, was developed. PCR primers were designed from the draft genome sequence which was obtained with the Next Generation Sequencing of A. citrulli KACC10651, and the nested-PCR primer set (Ac-ORF 21F/Ac-ORF 21R) were selected by checking of specificity to A. citrulli with PCR assays. The selected nested-PCR primer amplified the 140 bp DNA only from A. citrulli strains, and detection sensitivity of the nested PCR increased 10,000 times of $1^{st}$ PCR detection limit (10 ng genomic DNA/PCR). The nested PCR detected A. citrulli from the all samples of seed surface wash (external seed detection) of the artificially inoculated watermelon seeds with $10^1cfu/ml$ and above population of A. citrulli while the nested PCR could not detected A. citrulli from the mashed seed suspension (internal seed detection) of the all artificially inoculated watermelon seeds. When the naturally infested watermelon seeds (10% seed infested rate with grow-out test) used, the nested PCR detected A. citrulli from 2 seed samples out of 10 replication samples externally and 5 seed samples out of 10 replication samples internally. We believe that the nested-PCR developed in this study will be useful method to detect A. citrulli from the Cucurbitaceae seeds.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.7-18
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• 2017

Recently, the number of SNS user has rapidly increased due to smart device industry development and also the amount of generated data is exponentially increasing. In the twitter, Text data generated by user is a key issue to research because it involves events, accidents, reputations of products, and brand images. Twitter has become a channel for users to receive and exchange information. An important characteristic of Twitter is its realtime. Earthquakes, floods and suicides event among the various events should be analyzed rapidly for immediately applying to events. It is necessary to collect tweets related to the event in order to analyze the events. But it is difficult to find all tweets related to the event using normal keywords. In order to solve such a mentioned above, this paper proposes A Generation and Matching Method of Normal-Transient Dictionary for realtime topic detection. Normal dictionaries consist of general keywords(event: suicide-death-loop, death, die, hang oneself, etc) related to events. Whereas transient dictionaries consist of transient keywords(event: suicide-names and information of celebrities, information of social issues) related to events. Experimental results show that matching method using two dictionary finds more tweets related to the event than a simple keyword search.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.369-374
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• 2014

Path generation methods are required for safe and efficient driving in unmanned autonomous vehicles. There are two kinds of paths: global and local. A global path consists of all the way points including the source and the destination. A local path is the trajectory that a vehicle needs to follow from a way point to the next in the global path. In this paper, we propose a novel method for local path generation through machine learning, with an effective curve function used for initializing the trajectory. First, reinforcement learning is applied to a set of candidate paths to produce the best trajectory with maximal reward. Then the optimal steering angle with respect to the trajectory is determined by training an artificial neural network. Our method outperformed existing approaches and successfully found quality paths in various experimental settings, including the cases with obstacles.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.164-168
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• 2015

Due to the uncertainty of weather, it is difficult to construct an accurate forecasting model for daily PV power generation. It is very important work to know PV power in next day to manage power system. In this paper, correlation analysis between weather and power generation was carried out and daily PV power forecasting models based on Extreme Learning Machine(ELM) was presented. Performance of district ELM model was compared with single ELM model. The proposed method has been tested using actual data set measured in 2014.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.5
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• pp.549-556
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• 2009

One of the basic technology for implementing the autonomy of UGV(Unmanned Ground Vehicle) is a path planning algorithm using obstacle and raw terrain information which are gathered from perception sensors such as stereo camera and laser scanner. In this paper, we propose a generation method of DVGM(Directional Velocity Grid Map) which have traverse speed of UGV for the five heading directions except the rear one. The fuzzy system is designed to generate a resonable traveling speed for DVGM from current patch to the next one by using terrain slope, roughness and obstacle information extracted from raw world model data. A simulation is conducted with world model data sampled from real terrain so as to verify the performance of proposed fuzzy inference system.

• Korean Journal of Construction Engineering and Management
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• v.8 no.6
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• pp.235-245
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• 2007

This research study presents the development and application of an Elitist Genetic Algorithm (Elitist GA) for solving the resource-constrained project scheduling problem, which is one of the most challenging problems in construction engineering. Main features of the developed algorithm are that the elitist roulette selection operator is developed to preserve the best individual solution for the next generation so as to obtain the improved solution, and that parallel schedule generation scheme is used to generate a feasible solution to the problem. The experimental results on standard problem sets indicate that the proposed algorithm not only produces reasonably good solutions to the problems over the heuristic method and other GA, but also can find the optimal and/or near optimal solutions for the large-sized problems with multiple resources within a reasonable amount of time that will be applicable to the construction industry. This paper will help researchers and/or practitioners in the construction project scheduling software area with alternative means to find the optimal schedules by utilizing the advantages of the Elitist GA.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.50-56
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• 2021

Due to the recent increase in the mobile streaming market, mobile traffic is increasing exponentially. IMT-2020, named as the next generation mobile communication standard by ITU, is called the 5th generation mobile communication (5G), and is a technology that satisfies the data traffic capacity, low latency, high energy efficiency, and economic efficiency compared to the existing LTE (Long Term Evolution) system. 5G implements this technology by utilizing a high frequency band, but there is a problem of path loss due to the use of a high frequency band, which is greatly affected by system performance. In this paper, small cell technology was presented as a solution to the high frequency utilization of 5G mobile communication system, and furthermore, the system performance was improved by applying machine learning technology to macro communication and small cell communication method decision. It was found that the system performance was improved due to the technical application and the application of machine learning techniques.

• Journal of Electrical Engineering and Technology
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• v.1 no.2
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• pp.137-144
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• 2006

This paper proposes novel protection schemes for grid-connected distributed generation (DG) units using the fault location algorithm. The grid-connected DG would be influenced by abnormal distribution line conditions. Identification of the fault location for the distribution lines at the relaying point of DG helps solve the problems of the protection relays for DG. The proposed scheme first identifies fault locations using currents and voltages measured at DG and source impedance of distribution networks. Then the actual faulted feeder is identified, applying time-current characteristic curves (TCC) of overcurrent relay (OCR). The method considering the fault location and TCC of OCR might improve the performance of the conventional relays for DG. Test results show that the method prevents the superfluous operations of protection devices by discriminating the faulted feeder, whether it is a distribution line where DG is integrated or out of the line emanated from the substation to which the DGs are connected.