• 대한산업공학회지
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• 제28권2호
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• pp.171-177
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• 2002

In this paper, we propose a new data fusion method to improve the performance of individual prediction models for time series data. Individual models used are ARIMA and neural network and their results are combined based on the weight reflecting the inverse of EWMA of squared prediction error of each individual model. Monte Carlo simulation is used to identify the situation where the proposed approach can take a vintage point over typical fusion methods which utilize MSE for weight. Study results indicate the following: EWMA performs better than MSE fusion when the data size is large with a relatively big amplitude, which is often observed in intra-cranial pressure data. Additionally, EWMA turns out to be a best choice among MSE fusion and the two individual prediction models when the data size is large with relatively small random noises, often appearing in tax revenue data.

• 로봇학회논문지
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• 제2권3호
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• pp.255-261
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• 2007

We propose a optimal fusion method for localization of multiple robots utilizing correlation between GPS on each robot in common workspace. Each mobile robot in group collects position data from each odometer and GPS receiver and shares the position data with other robots. Then each robot utilizes position data of other robot for obtaining more precise estimation of own position. Because GPS data errors in common workspace have a close correlation, they contribute to improve localization accuracy of all robots in group. In this paper, we simulate proposed optimal fusion method of odometer and GPS through virtual robots and position data.

• Smart Structures and Systems
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• 제24권6호
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• pp.693-707
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• 2019

This paper proposes an integrated safety assessment method that can take multiple sources data into consideration based on a data fusion approach. Data cleaning using the Kalman filter method (KF) was conducted first for monitoring data from each sensor. The inclination data from the four tilt sensors of the same monitoring section have been associated to synchronize in time. Secondly, the finite element method (FEM) model was established to physically correlate the external forces with various structural responses of the shield tunnel, including the measured inclination. Response surface method (RSM) was adopted to express the relationship between external forces and the structural responses. Then, the external forces were updated based on the in situ monitoring data from tilt sensors using the extended Kalman filter method (EKF). Finally, mechanics parameters of the tunnel lining were estimated based on the updated data to make an integrated safety assessment. An application example of the proposed method was presented for an urban tunnel during a nearby deep excavation with multiple source monitoring plans. The change of tunnel convergence, bolt stress and segment internal forces can also be calculated based on the real time deformation monitoring of the shield tunnel. The proposed method was verified by predicting the data using the other three sensors in the same section. The correlation among different monitoring data has been discussed before the conclusion was drawn.

• Journal of information and communication convergence engineering
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• 제11권1호
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• pp.24-29
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• 2013

In this paper, we propose a fuzzy inference model for a navigation algorithm for a mobile robot that intelligently searches goal location in unknown dynamic environments. Our model uses sensor fusion based on situational commands using an ultrasonic sensor. Instead of using the "physical sensor fusion" method, which generates the trajectory of a robot based upon the environment model and sensory data, a "command fusion" method is used to govern the robot motions. The navigation strategy is based on a combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance based on a hierarchical behavior-based control architecture. To identify the environments, a command fusion technique is introduced where the sensory data of the ultrasonic sensors and a vision sensor are fused into the identification process. The result of experiment has shown that highlights interesting aspects of the goal seeking, obstacle avoiding, decision making process that arise from navigation interaction.

• 응용통계연구
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• 제19권2호
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• pp.291-303
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• 2006

대용량의 데이터로부터 의미있는 지식을 찾는 과정에서 데이터의 질은 무엇보다도 중요하다. 본 연구에서는 데이터의 충실도를 높이기 위한 방법으로 여러 경로로부터 수집된 데이터의 정보를 활용하기 위해 데이터 마이닝 알고리즘인 의사결정 규칙을 이용한 데이터 통합 기법을 제안하고, 실제 데이터를 이용하여 모의실험을 통해 제안된 알고리즘의 효율성을 비교하였다. 실험결과 제안된 알고리즘이 데이터 통합의 성능을 향상시킴을 알 수 있었다.

• Journal of Information Processing Systems
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• 제11권3호
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• 한국해양공학회지
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• 제37권1호
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• pp.38-48
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• 2023

To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.69-77
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• 2017

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

• 전자공학회논문지
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• 제49권9호
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• pp.335-343
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• 2012

본 논문은 근거리에서 접근하는 표적에 대한 레이더와 열영상의 관측데이터를 기반으로 정보융합을 수행하여 표적을 추적하는 알고리즘을 기술하고 있다. 일반적으로 칼만필터를 이용한 추적 융합 방법은 동기화된 레이더 및 열영상의 데이터를 근간으로 하고 있으며, 비동기적으로 동작하는 실제 시스템에 적용하기에는 많은 제한사항을 가지고 있다. 제안된 알고리즘에서의 중점사항은 동기화되어 있지 않은 서로 다른 두 센서인 레이더와 열영상의 관측데이터가 입력되었을 때 레이더의 거리정보와 추적상태벡터를 이용하여 관측값의 시간차이를 보상하여 관측치 융합 후 추적을 수행하는 것이다. 제안된 알고리즘의 성능평가를 위해 기존의 궤적기반 정보융합방법 및 측정치 융합기법과 성능을 비교하여 제시한다.

• Smart Structures and Systems
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• 제11권6호
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• pp.569-587
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• 2013

This paper evaluates the closed loop performance of the reaching law based discrete sliding mode controller with multisensor data fusion (MSDF) in real time, by controlling the first two vibrating modes of a piezo actuated structure. The vibration is measured using two homogeneous piezo sensors. The states estimated from sensors output are fused. Four fusion algorithms are considered, whose output is used to control the structural vibration. The controller is designed using a model identified through linear Recursive Least Square (RLS) method, based on ARX model. Improved vibration suppression is achieved with fused data as compared to single sensor. The experimental evaluation of the closed loop performance of sliding mode controller with data fusion applied to piezo actuated structure is the contribution in this work.