• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.

• The Journal of Korea Robotics Society
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• v.2 no.3
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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.

• Korean Journal of Remote Sensing
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• v.21 no.1
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• pp.3-13
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• 2005

National Research Lab Project 'Optimal Data Fusion of Geophysical and Geodetic Measurements for Geological Hazards Monitoring and Prediction' supported by Korea Ministry of Science and Technology is briefly described. The research focused on the geohazard analysis with geophysical and geodetic instruments such as superconducting gravimeter, seismometer, magnetometer, GPS, and Synthetic Aperture Radar. The aim of the NRL research is to verify the causes of geological hazards through optimal fusion of various observational data in three phases: surface data fusion using geodetic measurements; subsurface data fusion using geophysical measurements; and, finally fusion of both geodetic and geophysical data. The NRL hosted a special session 'Geohazard Monitoring with Space and Geophysical Technology' during the International Symposium on Remote Sensing in 2004 to discuss the current topics, challenges and possible directions in the geohazard research. Here, we briefly describe the special session papers and their relationships to the theme of the special session. The fusion of satellite and ground geophysical and geodetic data gives us new insight on the monitoring and prediction of the geological hazard.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.528-544
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• 2015

Base Station Cooperation (BSC) has been a promising technique for combating the Inter-Cell Interference (ICI) by exchanging information through a high-speed optical fiber back-haul to increase the diversity gain. In this paper, we propose a novel pilot symbol assisted data fusion scheme for distributed Uplink BSC (UBSC) based on Differential Evolution (DE) algorithm. Furthermore, the proposed scheme exploits the pre-defined pilot symbols as the sample of transmitted symbols to constitute a sub-optimal Weight Calculation (WC) model. To circumvent the non-linear programming problem of the proposed sub-optimal model, DE algorithm is employed for searching the proper fusion weights. Compared with the existing equal weights based soft combining scheme, the proposed scheme can adaptively adjust the fusion weights according to the accuracy of cooperative information, which remains the relatively low computational complexity and back-haul traffic. Performance analysis and simulation results show that, the proposed scheme can significantly improve the system performance with the pilot settings of the existing standards.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.593-599
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• 2013

China's Mapping Satellite-1, developed by the China Aerospace Science and Technology Corporation (CASC), was launched in three years ago. The data from Mapping Satellite-1 are able to use for efficient surveying and geometric mapping application field. In this paper, we fuse the panchromatic and multispectral images of Changchun area, which are obtained from the Mapping Satellite-1, the one that is the Chinese first transmission-type three-dimensional mapping satellite. The four traditional image fusion methods, which are HPF, Mod.IHS, Panshar and wavelet transform, were used to approach for effectively fusing Mapping Satellite-1 remote sensing data. Subsequently we assess the results with some commonly used methods, which are known a subjective qualitative evaluation and quantitative statistical analysis approach. Consequently, we found that the wavelet transform remote sensing image fusion is the optimal in the degree of distortion, the ability of performance of details and image information availability among four methods. To further understand the optimal methods to fuse Mapping Satellite-1 images, an additional study is necessary.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.91.4-91
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• 2001

In this paper, a development of the sensor fusion algorithm for a visual control of mobile robot is presented. The output data from the visual sensor include a time-lag due to the image processing computation. The sampling rate of the visual sensor is considerably low so that it should be used with other sensors to control fast motion. The main purpose of this paper is to develop a method which constitutes a sensor fusion system to give the optimal state estimates. The proposed sensor fusion system combines the visual sensor and inertial sensor using a modified Kalman filter. A kind of multi-rate Kalman filter which treats the slow sampling rate ...

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.64.4-64
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• 2001

Localization is the key role in controlling the Mobile Robot. In this papers, a development of the sensor fusion algorithm for controling UTV(Unmanned Tracked Vehicle) is presented. The multi-sensocial dead-rocking subsystem is established based on the optimal filtering by first fusing heading angle reading from a magnetic compass, a rate-gyro and two encoders mouned on the robot wheels, thereby computing the deat-reckoned location. These data and the position data provoded by LBL system are fused together by means of an extended Kalman filter. This algorithm is proved by simulation studies.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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• pp.69-78
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• 2009

Localization is a very important technique for the mobile robot to navigate in outdoor environment. In this paper, the development of the sensor fusion algorithm for controlling mobile robots in outdoor environments is presented. The multi-sensorial dead-reckoning subsystem is established based on the optimal filtering by first fusing a heading angle reading data from a magnetic compass, a rate-gyro, and two encoders mounted on the robot wheels, thereby computing the dead-reckoned location. These data and the position data provided by a global sensing system are fused together by means of an extended Kalman filter. The proposed algorithm is proved by simulation studies of controlling a mobile robot controlled by a backstepping controller and a cascaded controller. Performances of each controller are compared.

• Journal of Korean Neurosurgical Society
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• v.67 no.3
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• pp.354-363
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• 2024

Objective : This study aims to determine the optimal dose of recombinant-human bone morphogenic protein-2 (rhBMP-2) for successful bone fusion in minimally invasive lateral lumbar interbody fusion (MIS LLIF). Previous studies show that rhBMP is an effective alternative to autologous iliac crest bone graft, but the optimal dose remains uncertain. The study analyzes the fusion rates associated with different rhBMP doses to provide a recommendation for the optimal dose in MIS LLIF. Methods : Ninety-three patients underwent MIS LLIF using demineralized bone matrix (DBM) or a mixture of rhBMP-2 and DBM as fusion material. The group was divided into the following three groups according to the rhBMP-2 usage : group A, only DBM was used (n=27); group B, 1 mg of rhBMP-2 per 5 mL of DBM paste (n=41); and group C, 2 mg of rhBMP-2 per 5 mL of DBM paste (n=25). Demographic data, clinical outcomes, postoperative complication and fusion were assessed. Results : At 12 months post-surgery, the overall fusion rate was 92.3% according to Bridwell fusion grading system. Groups B and C, who received rhBMP-2, had significantly higher fusion rates than group A, who received only DBM. However, there was no significant increase in fusion rate when the rhBMP-2 dosage was increased from group B to group C. The groups B and C showed significant improvement in back pain and Oswestry disability index compared to the group A. The incidence of screw loosening was decreased in groups B and C, but there was no significant difference in the occurrence of other complications. Conclusion : Usage of rhBMP-2 in LLIF surgery leads to early and increased final fusion rates, which can result in faster pain relief and return to daily activities for patients. The benefits of using rhBMP-2 were not significantly different between the groups that received 1 mg/5 mL and 2 mg/5 mL of rhBMP-2. Therefore, it is recommended to use 1 mg of rhBMP-2 with 5 mL of DBM, taking both economic and clinical aspects into consideration.