• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.179-190
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• 2009

Travel time is one of the most important traffic parameters to evaluate operational performance of freeways. A variety of methods have been proposed to estimate travel times. One feasible solution to estimating travel times is to utilize existing loop detector-based infrastructure since the loops are the most widely deployed detection system in the world. This study proposed a new approach to estimate travel times for freeways. Inductive vehicle signatures extracted from the loop detectors were used to match vehicles from upstream and downstream stations. Ground-truthing was also conducted to systematically evaluate the performance of the proposed algorithm by recognizing individual vehicles captured by video cameras placed at upstream and downstream detection stations. A lexicographic optimization method vehicle reidentification algorithm was developed. Vehicle features representing the characteristics of individual vehicles such as vehicle length and interpolations extracted from the signature were used as inputs of the algorithm. Parameters associated with the signature matching algorithm were calibrated in terms of maximizing correct matching rates. It is expected that the algorithm would be a useful method to estimate freeway link travel times.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.727-739
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• 1998

The main objectives o reservoir optimal operation can be described as follows : maximization of the benefits through optimal allocation of the limited water resources for various purpose; minimization of t도 costs by the flood damage in potential damaging regions and risk of dam failure, etc. through safe drainage of a bulky volume of excessive water by a proper reservoir operation. Reviewing the past research works related to reservoir operation, we can find that the study on the matter of the former has been extensively carried out in last decades rather than the matter of the latter. This study is focused on developing a methodology of optimal reservoir operation for flood control, and a case study is performed on the Chungju multipurpose reservoir in Korea. The final goal of the study is to establish a reservoir optimal operation system which can search optimal policy to compromise two conflicting objectives: downstream flood damage and dam safety-upstream flood damage. In order to reach the final goal of the study, the following items were studied : (1)validation of hydrological data using HYMOS: (2)establishment of a downstream flood routing model coupling a rainfall-runoff model and SOBEK system for 1-D hydrodynamic flood routing; (3)replication of a flood damage estimation model by a neural network; (4)development of an integrated reservoir optimization module for an optimal operation policy.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.3 s.55
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• pp.1-10
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• 2007

In general, the tension forces of hanger cable in suspension bridges play an important role in evaluating the bridge conditions. The vibration method, as a conventional one, has been widely applied to estimate the tension forces by using the measured frequencies on hanger cables. However, the vibration method is not applicable to short hanger cables because the fiequencies of short cables are severely sensitive to flexural rigidity. Thus, in this study, the tension forces of short hanger cables, of which the length is shorter than 10 meters, were estimated through back analysis of the cable fiequencies measured from Gwang-An suspension bridge in Korea. Direct approach to back analysis is adopted using the univariate method among the direct search methods as an optimization technique. The univariate method is able to search the optimal tension forces without regard to the initial ones and has a rapid convergence rate. To verify the feasibility of back analysis, the results from back analysis and vibration method are compared with the design tension forces. From the comparison, it can be inferred that back analysis results are more reasonable agreement with the design tension forces of short hanger cable. Therefore, it is concluded that back analysis applied in this study is an appropriate tool for estimating tension forces of short hanger cables.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.221-226
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• 2004

Ant Colony System(ACS) Algorithm is new meta-heuristic for hard combinational optimization problem. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. Recently, various methods and solutions are proposed to solve optimal solution of graph coloring problem that assign to color for adjacency node($v_i, v_j$) that they has not same color. In this paper introducing ANTCOL Algorithm that is method to solve solution by Ant Colony System algorithm that is not method that it is known well as solution of existent graph coloring problem. After introducing ACS algorithm and Assignment Type Problem, show the wav how to apply ACS to solve ATP And compare graph coloring result and execution time when use existent generating functions(ANT_Random, ANT_LF, ANT_SL, ANT_DSATUR, ANT_RLF method) with ANT_XRLF method that use XRLF that apply Randomize to RLF to solve ANTCOL. Also compare graph coloring result and execution time when use method to add re-search to ANT_XRLF(ANT_XRLF_R) with existent generating functions.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.1
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• pp.97-104
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• 2015

This study is a part of the high pressure injection system development on the Turbo GDI engine in order to reduce smoke emission in case of using the low volatile(high DI) fuel which is used as normal gasoline fuel in the US market. Firstly, theoretical approach was done regarding gasoline fuel property, performance, definition of particle matters and its creation as well as problems of the high DI fuel. In this experimental study, 2L Turbo GDI engine was selected and optimized system parameter was inspected by changing fuel, fuel injection mode (single/multiple), fuel pressure, distance between injector tip and combustion chamber, start of injection, intake valve timing in engine dyno at all engine speed range with full load. In case of normal gasoline fuel, opacity was contained within 2% in all conditions. On the other hands, in case of low volatile fuel (high DI fuel), it was confirmed that the opacity was rapidly increased above 5,000 rpm at 14.5 ~ 20 MPa of fuel pressure and there were almost no differences on the opacity(smoke) between 17 MPa and 20 MPa fuel pressure. According to the SOI retard, smoke decrease tendency was observed but intake valve close timing change has almost no impact on the smoke level in this area. Consequently, smoke decrease was observed and 16% at 6000rpm respectively with injector washer ring installed. By removing injector washer to make injector tip closer to the combustion chamber, smoke decrease was observed by 46% at 5,500 rpm, 42% at 6,000 rpm. It is assumed that the fuel injection interaction with cylinder head, piston head, intake and exhaust valve is reduced so that impingement is reduced in local area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5622-5632
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• 2011

This paper presents the simulation design and analysis of Integrated Logistics System(ILS) which is operated by using the AGV(Automated Guided Vehicle). To maximize the operation performances of ILS with AGV, many parameters should be considered such as the number, velocity, and dispatching rule of AGV, part types, scheduling, and buffer sizes. We established the design of experiment in a way of Orthogonal Array in order to consider (1)maximizing the throughput; (2)maximizing the vehicle utilization; (3)minimizing the congestion; and (4)maximizing the Automated Storage and Retrieval System(AS/RS) utilization among various critical factors. Furthermore, we performed the optimization by using the simulation-based analysis and Evolution Strategy(ES). As a result, Orthogonal Array which is conducted far fewer than ES significantly saved not only the time but the same outcome when compared after validation test on the result from the two methods. Therefore, this approach ensures the confidence and provides better process for quick analysis by specifying exact experiment outcome even though it provides small number of experiment.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.118-118
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• 2012

수자원의 효율적 이용 및 관리는 심화되고 있는 기후변화에 선제적으로 대응하고 발생가능한 물위기에 대비하기 위한 필수조건이다. 그 중 가장 핵심이 되는 요소는 댐에 저수된 물을 효과적으로 이용하는 것, 즉 댐 건설목적에 따라 시간 및 공간별로 적절하게 할당시키는 것이라고 할 수 있다(Kim, 1998). 그러나 단일 댐의 운영과는 달리 수계내 댐군의 연계 운영은 매우 복잡하고 어려운 문제이다. 연계된 댐들간 저수 상황을 고려하여 유역내 시 공간적인 용수 수요의 지속적인 충족을 위하여 유입량 예측의 정확성을 높이도록 하고, 상류 댐에 최대한 저류하도록 하며, 여수로 방류 같은 불필요한 방류를 최소화 하고, 서로 상충되는 목표를 갖고 있지만, 홍수용량 및 발전수위를 최대로 확보하도록 하여야 한다. 이처럼 댐 운영을 위한 실제 상황은 단일 목적에 의한 최적화와는 달리 여러 상충되는 목적 및 구성 요소들간의 타협, 조정을 필요로 한다. 댐군의 연계운영 문제는 1960년대 초부터 현재까지 활발히 연구가 진행되어 온 분야 중 하나이나 문제의 복잡성과 어려움으로 인해 아직까지도 최선의 방안을 제시하기 어려운 문제이다(ReVelle, 2000). 이를 위한 방법은 시뮬레이션 모형 활용기법과 최적화 모형 활용기법으로 대별할 수 있으며 각 방법의 서로 다른 구조적 특성과 장단점으로 인하여 이원화된 체계로 사용되는 것이 현재의 국내 실정이다. 대부분의 실무에서는 이해도도 쉽고, 비교적 결과를 빨리 도출할 수 있는 시뮬레이션 기반의 모형을 활용하며 대표적으로 HEC-5, K-ModSim, HEC-ResSim 등이 활용되어왔다. 반면, 학계에서는 DP, MIP, SLP, SDP 등 최적화기법을 댐운영에 활용 할 것을 제안하고 있지만, 활용에 대한 거부감이 남아있는 것이 현실이다. 본 연구에서는 시뮬레이션과 최적화기법의 원론적 비교를 통해 각 방법의 장단점과 한계점을 분석하고, 왜 이원화된 사용체계로 되었는지에 대한 고찰과 이에 대한 해결책으로 시뮬레이션모형의 장점과 최적화기법의 장점을 결합한 모형을 제안한다. 국내에는 Kim and Park(1998)이 시뮬레이션 기반의 최적화 모형 CoMOM(Coordinated Multi-Reservoir Operating Model)을 개발하였으며, 이후 21C프론티어 연구사업(2001-2011)에서 모형의 보완수정 검증을 통해 실무 활용도를 높여 왔다. 본 연구를 통하여 거부감의 원천을 추적해 보고, 타당한 이유가 있는지 대한 것을 심층 분석해보고, CoMOM모형과 시뮬레이션 모형, 다른 최적화 기법들과의 원론적 비교를 통해 각 방법들의 효율적인 활용방안과 최적화모형의 구체적인 활용방안을 제시하고자 한다.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.249-249
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• 2013

Surface-enhanced Raman spectroscopy (SERS) is a sensitive approach to detect and to identify a variety of molecules. To enhance the Raman signal, optimization of the gap between nanostructures is quite important. One-dimensional materials such as nanowires, nanotubes, and nanograsses have great potential to be used in SERS due to their unique sizes and shape dependent characteristics. In this study we investigate a simple way to fabricate SERS substrates based on randomly grown copper oxide (CuO) nanowires. CuO nanograss is fabricated on pre-cleaned Cu foils. Cu oxidized in an ammonium ambient solution of 2.5 M NaOH and 0.1 M $(NH_4)_2S_2O_8$ at $4^{\circ}C$ for 10, 30, and 60 minutes. Then, Cu(OH)2 nanostructures are formed and dried at $180^{\circ}C$ for 2 h. With the drying process, the Cu(OH)2 nanostructure is transformed to CuO nanograss by dehydration reaction. CuO nanograss are grown randomly on Cu foil with the average length of 10 ${\mu}m$ and the average diameter of a 100 nm. CuO nanograsses are covered by Ag with various thicknesses from 10 to 30 nm using a thermal evaporator. Then, we immerse uncoated and Ag coated CuO nanowire samples of various oxidation times in a 0.001M methanol-based 4-mercaptopyridine (4-Mpy) in order to evaluate SERS enhancement. Raman shift and SERS enhancement are measured using a Raman spectrometer (Horiba, LabRAM ARAMIS Spectrometer) with the laser wavelength of 532 nm. Raman scattering is believed to be enhanced by the interaction between CuO nanograss and Ag island film. The gaps between Ag covered CuO nanograsses are diverse from <10 nm at the bottom to ~200 nm at the top of nanograsses. SERS signal are improved where the gaps are minimized to near 10s of nanometers. There are many spots that provide sufficiently narrow gap between the structures on randomly grown CuO nanograss surface. Then we may find optimal enhancement of Raman signal using the mapping data of average results. Fabrication of CuO nanograss based on a solution method is relatively simple and fast so this result can potentially provide a path toward cost effective fabrication of SERS substrate for sensing applications.