• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.2
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• pp.147-154
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• 2012

In this paper, we revise the JDL data fusion model to have an ability of distributed data fusion(DDF). Data fusion is a function that produces valuable information using data from multiple sources. After the network centric warfare concept was introduced, the data fusion was required to be expanded to DDF. We identify the data transfer and control between nodes is the core function of DDF. The previous data fusion models can not be used for DDF because they don't include that function. Therefore, we revise the previous JDL data fusion model by adding the core function of DDF and propose this new model as a model for DDF. We show that our model is adequate and useful for DDF by using several examples.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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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.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.927-935
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• 2007

Currently, Gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn#t mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, we present data fusion method of statistical survey data. Also, we suggest application methodology of association rule mining by clustering through data fusion of statistical survey data.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1201-1208
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• 2006

Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. Currently, Gyeongnam province is executing the social survey every year with the provincials. But, they have the limit of the analysis as execute the different survey to 3 year cycles. In this paper, we study to data fusion of environmental survey data using sas macro. We can use data fusion outputs in environmental preservation and environmental improvement.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.279-287
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• 2007

Data fusion is the process of combining multiple data in order to produce information of tactical value to the user. Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. In this paper, we develop was macro program for statistical matching which is one of five branch types for data fusion. And then we apply data fusion and association rule techniques to environmental data.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.21-27
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• 2006

Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. Currently, Gyeongnam province is executing the social survey every year with the provincials. But, they have the limit of the analysis as execute the different survey to 3 year cycles. In this paper, we study to data fusion of environmental survey data using sas macro. We can use data fusion outputs in environmental preservation and environmental improvement.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.9
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• pp.414-419
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• 2006

This paper presents a design of a multi-sensor data fusion filter for a Flight Test System. The multi-sensor data consist of positional information of the target from radars and a telemetry system. The data fusion filter has a structure of a federated Kalman filter and is based on the Singer dynamic target model. It consists of dedicated local filter for each sensor, generally operating in parallel, plus a master fusion filter. A fault detection and correction algorithms are included in the local filter for treating bad measurements and sensor faults. The data fusion is carried out in the fusion filter by using maximum likelihood estimation algorithm. The performance of the designed fusion filter is verified by using both simulation data and real data.

• 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.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.368-375
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• 2003

Data fusion approach is investigated to avoid suction in the left ventricular assist system (LVAS) using a nonpulsatile pump. LVAS requires careful control of pump speed to support the heart while preventing suction in the left ventricle and providing proper cardiac output at adequate perfusion pressure to the body. Since the implanted sensors are usually unreliable for long-term use, a sensorless approach is adopted to detect suction. The pump model is developed to provide the load coefficient as a necessary signal to the data fusion system without the implanted sensors. The load coefficient of the pump mimics the pulsatility property of the actual pump flow and provides more comparable information than the pump flow after suction occurs. Four signals are generated from the load coefficient as inputs to the data fusion system for suction detection and a neural fuzzy method is implemented to construct the data fusion system. The data fusion approach has a good ability to classify suction status and it can also be used to design a controller for LVAS.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.305-316
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• 2010

The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor has 36 bands at 250m, 500m, 1km spatial resolution. However, 500m or 1km MODIS data exhibits a few limitations when low resolution data is applied at small areas that possess complex land cover types. In this study, we produce seven 250m spectral bands by fusing two MODIS 250m bands into five 500m bands. In order to recommend the best fusion method by which one acquires MODIS data, we compare seven fusion methods including the Brovey transform, principle components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the least mean and variance matching method, the least square fusion method, the discrete wavelet fusion method, and the wavelet-PCA fusion method. Results of the above fusion methods are compared using various evaluation indicators such as correlation, relative difference of mean, relative variation, deviation index, peak signal-to-noise ratio index and universal image quality index, as well as visual interpretation method. Among various fusion methods, the local mean and variance matching method provides the best fusion result for the visual interpretation and the evaluation indicators. The fusion algorithm of 250m MODIS data may be used to effectively improve the accuracy of various MODIS land products.