• Journal of Navigation and Port Research
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• v.33 no.6
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• pp.457-462
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• 2009

In this paper we suggest optimal positioning algorithm for DER(distributed energy resource)s near ocean side by using Newton-Rhapson load flow calculation. By installing DERs within urban area, electric power can be effectively transmitted to each loads without constructing additional large scale power stations and transmission lines. Therefore, DERs have attracted worldwide attention as urban area energy sources. However, there are quite a few studies for estimation of power loss due to DERs' location change within urban area Hence, in this study, an optimal positioning scheme for DERs is proposed in order to minimizing electrical power loss.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1188-1197
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• 2010

A most important thing in a connected dominating set(CDS)-based routing in a wireless ad-hoc network is to select a minimum number of dominating nodes and then build a backbone network which is made of them. Node failure in a CDS is an event of non-negligible probability. For applications where fault tolerance is critical, a traditional dominating-set based routing may not be a desirable form of clustering. It is necessary to minimize the frequency of reconstruction of a CDS to reduce message overhead due to message flooding. The idea is that by finding alternative nodes within a restricted range and locally reconstructing a CDS to include them, instead of totally reconstructing a new CDS. With the proposed algorithm, the resulting number of dominating nodes after partial reconstruction of CDS is not changed and also its execution time is faster than well-known algorithm of construction of CDS by 20~40%. In the case of high mobility situation, the proposed algorithm gives better results for the performance metrics, packet receive ratio and energy consumption.

• Journal of the Korean Institute of Gas
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• v.4 no.3 s.11
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• pp.19-25
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• 2000

In order to prevent secondary disaster such as gas explosion which comes after a devastating magnitude earthquake, the seismic monitoring and transmission system for natural gas governor station was developed. To measure ground motions precisely and operate the seismic monitoring system efficiently, the position and method of accelerometer installation were recommended by the analysis of ground noise patterns of governor station. For making a decision on prompt shut-off of gas supplies in the event of a great earthquake, the real-time calculation algorithm of PGA(Peak Ground Acceleration) and SI(Spectrum Intensity) were developed and it has been implemented in the seismic monitoring and transmission system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.856-859
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• 2010

WSN is composed of a lot of small sensors with the limited hardware resources. In WSN, at the initial stage, sensor nodes are randomly deployed over the region of interest, and self-configure the clustered networks by grouping a bunch of sensor nodes and selecting a cluster header among them. Specially, in Mobile-WSN environment, in which the administrator's intervention is restricted, the self-configuration capability is essential to establish a power-conservative Mobile-WSN which provides broad sensing coverage and communication coverage. In this paper, we propose a self-configuration routing protocol for Mobile-WSN, which consists of step-wise novel protocols for initial deployment, effective joining and removal of sensor nodes, which result in reducing overall power consumption, and extending the lifetime of network.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.92-92
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• 2022

산 사면의 지반이 붕괴되어 흙, 모래, 자갈 그리고 물 등이 혼합하여 유동하는 토석류는 예측과 대비가 어려운 자연재해 중 하나 이다. 특히, 강우로 인해 발생하는 토석류의 경우 매우 빠르게 유동하기 때문에 피해 예측이 제한적이다. 이러한 토석류가 도심지역 또는 마을주변에서 발생할 경우 많은 인명 및 재산 피해가 발생한다. 따라서 토석류의 유동을 최소화시키기 위해선 1차적으로 수치모형을 통한 전반적인 유동 및 피해 규모 예측이 이루어져야 하며, 이러한 분석을 바탕으로 사방댐과 같은 구조물의 효율적인 설계가 이루어져야 한다. 이에 수치모형을 통해 토석류의 유동을 분석하고자 하는 많은 연구가 진행된 바 있으며, 사방댐 설계 분석 또한 수치모형과 실험을 통해 연구된 바 있다. 선행연구들에 따르면, 1) 발생부로부터의 거리, 2) 토석류 에너지의 감소, 3) 침식-연행 작용, 4) 사방댐의 용량 등이 효율적인 사방댐 설계에 영향을 미친다고 분석된 바 있다. 하지만 위의 항목들에 대한 종합적인 비교분석은 미비한 실정이다. 따라서 본 연구에선 위에서 제시한 4가지의 항목들을 바탕으로 사방댐 설계에 중요한 요소를 평가하고 산정하고자 한다. 토석류의 유동과 사방댐을 모의분석하기 위해 Deb2D 수치모형을 활용하였으며, Voellmy 유변학적 모형과 침식-연행-퇴적 작용을 분석할 수 있는 알고리즘을 사용하여 토석류의 유동을 현실에 가깝게 모의하였다. 2011년 서울 우면산에서 발생한 산사태 유역들 중에서 래미안 아파트 유역과2019년 강원도 갈남리에서 발생한 산사태를 대상지구로 선정하였다. 연구 결과에 따르면 4가지 요소들 중에서 사방댐의 용량이 효율적인 사방댐 설계에 가장 주요한 요인으로 분석되었다.

• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.77-84
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• 2007

The intima media thickness(IMT) of the carotid artery from B mode ultrasound images has recently been proposed as the most useful index of individual atherosclerosis and can be used to predict major cardiovascular events. Ultrasonic measurements of the IMT are conventionally obtained by manually tracing interfaces between tissue layers. The drawbacks of this method are the inter and intra observer variability and its inefficiency. In this paper, we present a multiresolution snake method combined with the dynamic programming, which overcomes the various noises and sensitivity to initialization of conventional snake. First, an image pyramid is constructed using the Gaussian pyramid that maintains global edge information with smoothing in the images, and then the boundaries are automatically detected in the lowest resolution level by minimizing a cost function based on dynamic programming. The cost function includes cost terms which are representing image features and geometrical continuity of the vessel interfaces. Since the detected boundaries are selected as initial contour of the snake for the next level, this automated approach solves the problem of the initialization. Moreover, the proposed snake improves the problem of converging th the local minima by defining the external energy based on multiple image features. In this paper, our method has been validated by computing the correlation between manual and automatic measurements. This automated detection method has obtained more accurate and reproducible results than conventional edge detection by considering multiple image features.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.9
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• pp.557-567
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• 2016

In this study, the optimal design of heat exchangers, including the evaporator and condenser of a solar-heating ocean thermal energy conversion (SH-OTEC), is investigated. The power output of the SH-OTEC is assumed to be 100 kW, and the SH-OTEC uses the working fluid of R134a and high-performance commercial tubes. The surface heat transfer area and the pressure drop were strongly dependent on the number of tubes, as well as the number of tube passes. To solve the reciprocal tendency between the heat transfer area and pressure drop with respect to the number of tubes, as well as the number of tube passes, a genetic algorithm (GA) with two objective functions of the heat transfer area (the capital cost) and operating cost (pressure drop) was used. Optimal results delineated the feasible regions of heat transfer area and operating cost with respect to the pertinent number of tubes and tube passes. Pareto fronts of the evaporator and condenser obtained from multi-objective GA provides designers or investors with a wide range of optimal solutions so that they can select projects suitable for their financial resources. In addition, the surface heat transfer area of the condenser took up a much higher percentage of the total heat transfer area of the SH-OTEC than that of the evaporator.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.4
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• pp.361-368
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• 2002

The aim of this study was to investigate the effect of preprocessing the transmitted energy spectrum data on development of a robust model to predict the sugar content in intact apples. The spectrum data were measured from 120 Fuji apple samples conveying at the speed of 2 apples per second. Computer algorithms of preprocessing methods such as MSC, SNV, first derivative, OSC and their combinations were developed and applied to the raw spectrum data set. The results indicated that correlation coefficients between the transmitted energy values at each wavelength and sugar contents of apples were significantly improved by the preprocessing of MSC and SNV in particular as compared with those of no-preprocessing. SEPs of the prediction models showed great difference depending on the preprocessing method of the raw spectrum data, the largest of 1.265%brix and the smallest of 0.507% brix. Such a result means that an appropriate preprocessing method corresponding to the characteristics of the spectrum data set should be found or developed for minimizing the prediction errors. It was observed that MSC and SNV are closely related to prediction accuracy, OSC is to number of PLS factors and the first derivative resulted in decrease of the prediction accuracy. A robust calibration model could be d3eveloped by the combined preprocessing of MSC and OSC, which showed that SEP=0.507%brix, bias=0.0327 and R2=0.8823.

• The Journal of Bigdata
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• v.9 no.1
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• pp.153-168
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• 2024

In summer, as chillers are considered the main energy consumer of building, the efficient chiller operation is considered important. However, it is difficult to operate chillers to meet the cooling demand of the building as the demand fluctuates with various factors like the internal, external environment and behavior of the occupants and as chiller's constraint cause the current operation constrains operation in future. To address these problems, this study proposes a multi-chiller operation model based on deep reinforcement learning considering the minimum up-time of the chiller. The proposed model learns the value of the chiller operations according to the state composed of metrological and cooling system information and determines operation that minimizes the difference between the supply load and the cooling demand among feasible operations. The practical applicability was improved by applying the training algorithm considering the minimum up-time constraint and Experiments results using the actual data from a Korean university confirmed that the proposed model complies with the chiller constraints and outperforms the existing chiller operation logic of the university in terms of differences from the building cooling demand.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.446-460
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• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.