• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.3
• /
• pp.42-52
• /
• 2022

Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.

• Journal of IKEEE
• /
• v.26 no.1
• /
• pp.10-18
• /
• 2022

Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.392-392
• /
• 2021

자료동화(data assimilation) 기법은 관측 자료와 예측 모형의 정보를 동시에 활용, 모형의 상태량(state variables)이나 매개변수(model parameters)를 실시간으로 업데이트하는 Bayesian 필터링 이론에 근거한 방법으로, 최근 이를 활용한 수문 모의 정확도 향상 기술이 빠르게 발전하고 있다. 본 연구에서는 앙상블 자료동화의 정확성을 향상시키기 위한 세부 방법인 along-the-stream localization과 inflation 기법의 분포형 수문 모형에 대한 적용성을 대규모 지역 단위(regional-scale) 모의를 통해 검토한다. 분포형 수문모형과 자료동화 framework로는 WRF-Hydro(Weather Research and Forecasting Model Hydrological Modeling System)와 DART(Data Assimilation Research Testbed)를 각각 적용한다. WRF-Hydro는 미국의 전 대륙지역(CONUS; continental United States)에 대한 수문 모델링 framework인 National Water Model의 핵심엔진이고, DART는 미국 National Center for Atmospheric Research(NCAR) 연구소에서 개발한 범용 자료동화 도구이다. 본 연구에서는 지표수 수문모형의 자료동화를 위해 개발된 기법인 along-the-stream localization과 inflation 기법이 하도 추적에 미치는 영향을 분석한다. along-the stream localization 기법은 공간적 근접도 외에 하도의 수문학적 연관관계를 고려하는 localization 기법으로, 상대적으로 수문학적 상관도가 떨어지는 하도에 대한 과도한 자료동화를 줄여줄 수 있다. inflation 기법은 앙상블의 다양성을 증가시키는 기법으로, 칼만 필터(Kalman filter)에 의한 업데이트의 이전이나 이후 적용하여 앙상블 예측의 정확도를 추가적으로 향상시킬 수 있다. 본 고에서는 앙상블 자료동화 기법을 지표수 수문 모의에 적용할 경우 남아 있는 난제와 적용 가능한 방법에 대해 중점적으로 논의한다.

• Journal of Korean Society for Quality Management
• /
• v.51 no.2
• /
• pp.171-184
• /
• 2023

Purpose: First, this paper suggests an alternative approach to find optimal portfolio (stocks, bonds and ESG stocks) under the maximizing utility of investors. Second, we include ESG stocks in our optimal portfolio, and compare improvement of welfares in the case with and without ESG stocks in portfolio. Methods: Our main method of analysis follows Brennan et al(2002), designed under the continuous time framework. We assume that the dynamics of stock price follow the Geometric Brownian Motion (GBM) while the short rate have the Vasicek model. For the utility function of investors, we use the Power Utility Function, which commonly used in financial studies. The optimal portfolio and welfares are derived in the partial equilibrium. The parameters are estimated by using Kalman filter and ordinary least square method. Results: During the overall analysis period, the portfolio including ESG, did not show clear welfare improvement. In 2017, it has slightly exceeded this benchmark 1, showing the possibility of improvement, but the ESG stocks we selected have not strongly shown statistically significant welfare improvement results. This paper showed that the factors affecting optimal asset allocation and welfare improvement were different each other. We also found that the proportion of optimal asset allocation was affected by factors such as asset return, volatility, and inverse correlation between stocks and bonds, similar to traditional financial theory. Conclusion: The portfolio with ESG investment did not show significant results in welfare improvement is due to that 1) the KRX ESG Leaders 150 selected in our study is an index based on ESG integrated scores, which are designed to affect stability rather than profitability. And 2) Korea has a short history of ESG investment. During the limited analysis period, the performance of stock-related assets was inferior to bond assets at the time of the interest rate drop.

• Journal of Advanced Navigation Technology
• /
• v.28 no.3
• /
• pp.262-271
• /
• 2024

In this paper, by applying deep learning, one of the A.I. techniques, through angle information, which is optical observation data generated when observing satellites at observatories, distance information from observatories is learned to predict range data, thereby increasing the precision of satellite's orbit determination. To this end, we generated observational data from GMAT, reduced the learning data error of deep learning through preprocessing of the generated observational data, and conducted deep learning through MATLAB. Based on the predicted distance information from learning, trajectory determination was performed using an extended Kalman filter, one of the filtering techniques for trajectory determination, through GMAT. The reliability of the model was verified by comparing and analyzing the orbital determination with angular information without distance information and the orbital determination result with predicted distance information from the model.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.2
• /
• pp.247-258
• /
• 2003

In this paper, we present a pen-shaped input device equipped with accelerometers and gyroscopes that measure inertial movements when a user writes on 2 or 3 dimensional space with the pen. The measurements from gyroscope are integrated once to find the attitude of the system and are used to compensate gravitational effect in the accelerations. Further, the compensated accelerations are integrated twice to yield the position of the system, whose basic concept stems from the field of inertial navigation. However, the accuracy of the position measurement significantly deteriorates with time due to the integrations involved in recovering the handwriting trajectory This problem is common in the inertial navigation system and is usually solved by the periodic or aperiodic calibration of the system with external reference sources or other information in the filed of inertial navigation. In the presented paper, the calibration of the position or velocity is performed on-line and off-line. In the on-line calibration stage, the complementary filter technique is used, where a Kalman filter plays an important role. In the off-line calibration stage, the constant component of the resultant navigational error of the system is removed using the velocity information and motion detection algorithm. The effectiveness and feasibility of the presented system is shown through the experimental results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.1
• /
• pp.73-82
• /
• 2010

TERCOM(TERrain COntour Matching), which is the one of the Terrain Referenced Navigation and used in the cruise missile navigation system, is still under development. In this study, the TERCOM based on area-based matching algorithm and extended Kalman filter is analysed through simulation. In area-based matching, the mean square difference (MSD) and cross-correlation matching algorithms are applied. The simulation supposes that the barometric altimeter, radar altimeter and SRTM DTM loaded on board. Also, it navigates along the square track for 545 seconds with the velocity of 1000km per hour. The MSD and cross-correlation matching algorithms show the standard deviation of position error of 99.6m and 34.3m, respectively. The correlation matching algorithm is appeared to be less sensitive than the MSD algorithm to the topographic undulation and the position accuracy of the both algorithms is extremely depends on the terrain. Therefore, it is necessary to develop an algorithm that is more sensitive to less terrain undulation for reliable terrain referenced navigation. Furthermore, studies on the determination of proper matching window size in long-term flight and the determination of the best terrain database resolution needed by the flight velocity and area should be conducted.

• Journal of Korea Multimedia Society
• /
• v.16 no.2
• /
• pp.138-146
• /
• 2013

In this paper, we propose a new 3D PUI(Physical User Interface) system in which the index fingertip points and moves a mouse position on a given monitor screen. There are two 3D PUI schemes to control smart devices like smart TVs remotely, the relative pointing one and the absolute pointing one. The former has a problem in that it does not match the human perception process, and the latter requires excessive movement of the body. We combined the relative one and the absolute one, and develop a new intuitive and user-friendly pointing method, 3D PUI. It requires an establishment of a pyramid shape visible area (view volume) to point a mouse position on a screen with the dominant eye. In order to maintain the real-time view volume, however, it requires large computation depending on the movement of the dominant eye. We optimized the computation of the view volume in which it determines the internal and external position on the screen. In addition, Kalman filter is applied with tracing of the mouse pointer position to stabilize the trembling of the pointer and offers the user ease of use.

• Journal of IKEEE
• /
• v.15 no.1
• /
• pp.76-85
• /
• 2011

In this paper, we analysed RSS characteristics by external factors and presented an efficient algorithm for real-time location tracking and its hardware system. The proposed algorithm enhanced the ranging accuracy using Kalman Filter based on the RSS DB. The location tracking system that consists of the tag, AP(Access Point), a data collector(Data Receiver) with IEEE 802.15.4(ZigBee) network environment, and location tracking application that reveal locations of each tag is implemented for the test environment. The location tracking system presented in this paper is implemented with MSP430 microprocessor manufactured by TI(Texas Instrument), CC2420 RF chipset and the location tracking application. With the results of the experiment, the proposed algorithm and the system can achieve the efficiency and the accuracy of location tracking with the average error of 19.12cm, and its standard deviation of 5.31cm in outdoor circumstance. Also, the experimental result shows that exact tracking of position in indoor circumstance cannot achieve because of vulnerable RSS with external circumstance.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.5 no.1 s.9
• /
• pp.1-12
• /
• 2006

In order to estimate a dynamic origin and destination demand between on and off-ramps in the freeways, a traffic flow theory can be used to calculate a link distribution proportion of traffics moving between them. We have developed a dynamic traffic estimation model based on the three-phase traffic theory (Kerner, 2004), which explains the complexity of traffic phenomena based on phase transitions among free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatiotemporal features. The developed model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We have estimated the link, on and off-ramp volumes at every time interval by using traffic data collected from vehicle detection systems in Korea freeway sections. The analyzed results show that the developed model describes traffic flows adequately.