• 한국정보처리학회논문지
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• 제6권10호
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• pp.2728-2739
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• 1999

This paper proposes an effective fusion algorithm for retrieval results from heterogeneous information sources in federated digital libraries. The algorithm determines the population of documents retrieved from involved information sources for a given query and evaluates the degree of relevance between the query and the population. The evaluated results are used as relevance distribution information for collection fusion. The main informations used for the fusion are relevance distribution among collections, the population size N, and ranking information of relevant documents in their origin. We also present th performance evaluation of the algorithm by developing the prototype of a meta-searcher.

• 자동차안전학회지
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• 제13권4호
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• 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.

• 한국군사과학기술학회지
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• 제12권6호
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• pp.697-703
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• 2009

Target Motion Analysis(TMA) means to detect target position, velocity and course for using passive sonar system with bearing-only measurement. In this paper, we apply the TMA algorithm for a submarine with Multi-Sensor Data Fusion(MSDF) and we will decide the best TMA algorithm for a submarine by a series of computer simulation runs.

• Investigative Magnetic Resonance Imaging
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• 제24권3호
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• pp.123-131
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• 2020

Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination. Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF's parameters (r_pxy, r_pz, r_sxy, r_sz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge. Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of r_pxy = 10, r_pz = 1, r_sxy = 3, r_sz = 1, and β = 3. Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.509-520
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• 2005

Data fusion is defined as the process of combining data and information from different sources for the effectiveness of the usage of useful information contents. In this paper, we propose a data fusion algorithm using k-means clustering method for data enrichment to improve data quality in knowledge discovery in database(KDD) process. An empirical study was conducted to compare the proposed data fusion technique with the existing techniques and shows that the newly proposed clustering data fusion technique has low MSE in continuous fusion variables.

• 자동차안전학회지
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• 제3권2호
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• pp.28-33
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• 2011

This paper presents driving path estimation algorithm for adaptive cruise control system and advanced emergency braking system using multi-sensor fusion. Through data collection, yaw rate filtering based road curvature and vision sensor road curvature characteristics are analyzed. Yaw rate filtering based road curvature and vision sensor road curvature are fused into the one curvature by weighting factor which are considering characteristics of each curvature data. The proposed driving path estimation algorithm has been investigated via simulation performed on a vehicle package Carsim and Matlab/Simulink. It has been shown via simulation that the proposed driving path estimation algorithm improves primary target detection rate.

• Advances in aircraft and spacecraft science
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• 제1권4호
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• pp.455-469
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• 2014

High-precision autonomous localization technique is essential for future Mars rovers. This paper addresses an innovative integrated localization algorithm using a multiple information fusion approach. Firstly, the output of IMU is employed to construct the two-dimensional (2-D) dynamics equation of Mars rover. Secondly, radio beacon measurement and terrain image matching are considered as external measurements and included into the navigation filter to correct the inertial basis and drift. Then, extended Kalman filtering (EKF) algorithm is designed to estimate the position state of Mars rovers and suppress the measurement noise. Finally, the localization algorithm proposed in this paper is validated by computer simulation with different parameter sets.

• 대한임베디드공학회논문지
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• 제6권3호
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• pp.157-167
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• 2011

Although vehicles become more intelligent for convenience and safety of drivers, traffic accidents are increased more and more. Especially, car-to-car single rear impacts in the urban area are increased rapidly because of driver inattention. To prevent rear impacts in the urban area, commercial automobile vendor applies the low-speed short-distance collision warning system. This paper presents low-speed short-distance collision warning algorithm for the city driving by using sensor fusion of laser sensor and ultrasonic sensor. An experiment using embedded microprocessor in the driving track was used to demonstrate the feasibility of the collision warning algorithm.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1461-1471
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• 2021

This paper presents a light exposure correction algorithm for less pleasant images, acquired with a light metering failure. Since conventional tone mapping and gamma correction methods adopt a function mapping with the same range of input and output, the results are pleasurable for almost symmetric distributions to their intensity average. However, their corrections gave insufficient outputs for asymmetric cases at either bright or dark regions. Also, histogram modification approaches show good results on varied pattern images, but these generate unintentional noises at flat regions because of the compulsive shift of the intensity distribution. Therefore, in order to sufficient corrections for both bright and dark areas, the proposed algorithm calculates the gamma coefficients using primary parameters extracted from the global distribution. And the fusion weights are adaptively determined with complementary parameters, considering the classification information of a binary segmentation. As the result, the proposed algorithm can obtain a good output about both the symmetric and the asymmetric distribution images even with severe exposure values.