• 제어로봇시스템학회논문지
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• 제22권10호
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• pp.803-810
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• 2016

This paper presents a study of localization methods based on particle filter using 2D laser sensor measurements and road feature map information, for autonomous vehicles. In order to navigate in an urban environment, an autonomous vehicle should be able to estimate the location of the ego-vehicle with reasonable accuracy. In this study, road features such as curbs and road markings are detected to construct a grid-based feature map using 2D laser range finder measurements. Then, we describe a particle filter-based method for accurate positional estimation of the autonomous vehicle in real-time. Finally, the performance of the proposed method is verified through real road driving experiments, in comparison with accurate DGPS data as a reference.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2483-2503
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• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• 분석과학
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• 제22권1호
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• pp.35-43
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• 2009

단순히 완제품만을 평가하는 기존의 방법에서 제조 공정의 보다 깊은 이해와 공정 제어를 통한 품질 확보가 PAT (공정분석기술, process analytical technology)가 지향하고 있는 핵심이다. 생산 상 위험 요소가 많이 발생하는 공정을 제어할 수 있는 목적으로 새로운 실시간 공정 제어 기술을 개발하는 것이 PAT 연구 내용이다. 공정 단계에 대한 이해, 공정에 필요한 여러 가지 변수 및 공정 생산품의 평가 기술은 PAT를 수행하기 위한 기술적 핵심 요소이다. 이에 관련하여 다국적 제약회사의 실제 공정에서의 실시간 품질관리 사례는 상당히 많은 것으로 알려져 있으나, 제조공정 및 분석 기술 자체가 기업 비밀인 경우가 많아 관련 정보는 극히 제한되어 있다. 본 총설을 통하여 실제 제약 산업 현장에서 활용되고 있는 적용사례를 소개하고, 각 제조 공정 및 완제품 실시간 품질 관리를 위한 PAT 연구 사례 및 기술개발 현황에 대하여 언급하고자 한다.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2012년도 춘계학술발표대회 논문집
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• 한국지구과학회지
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• 제31권5호
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• pp.465-474
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• 2010

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

• Journal of Positioning, Navigation, and Timing
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• 제5권2호
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• pp.59-66
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• 2016

The International GNSS Service (IGS) provides the IGS-Real Time Service (IGS-RTS) corrections that can be used in stand-alone positioning in real time. In this study, the positioning accuracy before and after the application of the corrections to broadcast ephemeris by applying the IGS-RTS corrections at code pseudo-range based stand-alone positioning was compared with positioning result using precise ephemeris. The analysis result on IGS-RTS corrections showed that orbit error and clock error were 0.05 m and 0.5 ns compared to precise ephemeris and accuracy improved by about 8.5% compared to the broadcast ephemeris-applied result when the IGS-RTS was applied to positioning. Furthermore, regionally dispersed five observatories were selected to analyze the effect of external environments on positioning accuracy and positioning errors according to location and time were compared as well as the number of visible satellites and position dilution of precision by observatory were analyzed to verify a correlation with positioning error.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.472-477
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• 2013

An internet-based, real-time GPS synchronized wide-area power system frequency monitoring network(FNET) has been monitoring wide-area power system frequency in continuous time in the United States. This paper analyzes the FNET measurement to the verified disturbances in the US eastern interconnected power system and simulates it using the dynamic system model. By comparing the frequency measurements with its simulation results to the same disturbances in detail, this paper finds that the sequence of monitoring points to detect the frequency fluctuation caused by the disturbances is matched well in the measured data and the simulation results. The similarity comparison index is also proposed to quantify the similarity of the compared cases. The dynamic model based simulation result is expected to compensate for the lack of FNET measurement in its applications.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2439-2442
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• 2000

This paper presents a practical On-The-Fly(OTF) integer ambiguity resolution algorithm for real-time precise positioning with low cost, $L_1$ single frequency, conventional C/A code GPS receiver. A state reconfiguration scheme is adopted in the Kalman filter to deal with the variation of ambiguity states caused by varying sets of visible GPS satellites. The proposed algorithm reduces the ambiguity search space from the coarse m-level C/A code pseudorange measurements of the conventional C/A code reciever, thereby reducing the computational time. Simulation results are presented to show that the algorithm achieves a cm-level accuracy.

• International Journal of Ocean System Engineering
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• 제3권3호
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.

• 센서학회지
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• 제33권1호
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• pp.30-33
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• 2024

The occurrence of harmful algae on the coast of Korea has been a cause of damage to the aquaculture industry and deterioration of the coastal ecosystem environment. A method is required to predict their outbreak in real-time at the site. Therefore, this study attempted to develop a small hybrid optical sensor and real-time monitoring system based on LiDAR that can be used in the field and laboratory and can be applied to various platforms. FMS-L specifically suggested the amount of Chlorophyll a (Chl a) in the sample by measuring and analyzing the fluorescence emitted by the irradiating light. The accuracy of FMS-L was verified by measuring the concentrations of standard Chlorophyll a substances and Margalfidinium polykirkoids. In addition, the precision was verified by comparing the measurement results of FMS-L using commercial equipment Phyto-PAM-II. This equipment is compact and easy to move. Therefore, it can be easily applied to field surveys, allows short time measurements (10 s), and can be applied at a distance of 10 m from the measurement site.