• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(C)
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• pp.63-65
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• 2012

플라즈마 물성데이터는 플라즈마 내에서 일어나는 입자(전자, 원자, 이온분자 등) 들의 충돌에 의해 발생되는 데이터로써 플라즈마 현상을 이해하는 필수 데이터이며, 다양한 분야에서 응용되고 있다. 플라즈마 물성데이터를 산업계에서 지속적으로 참조하여 사용할 수 있도록 만든 수치데이터나 통계자료를 플라즈마 물성 참조데이터라고 한다. 기존 플라즈마 물성데이터 센터의 플라즈마 물성 참조데이터 수집평가 시스템은 사용자가 이용하고자 하는 플라즈마 물성 참조데이터를 일일이 자신의 시스템에 맞춰 데이터를 다시 가공해야 하는 어려움이 있어 데이터를 효율적인 관리하고 다양한 분야에 적용하기 위한 XML Schema 설계 대해 논의한다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (B)
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• pp.345-349
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• 2007

과학연구 분야에서는 부단히 새로운 연구 과제를 제기되고 있으며 이에 대한 연구조건 및 연구방법을 다각도로 변화시키면서 보다 효과적인 연구결과를 얻기 위한 노력을 거듭하고 있다. 이와 같은 연구활동이 활발히 진행됨에 따라 급증하는 데이터를 효율적으로 관리할 필요성이 증가하였으며 다양한 데이터 저장소에 저장되어 있는 다량의 데이터를 통합관리하기 위한 연구가 계속되어 왔다. 이런 연구를 진행함에 있어서 데이터를 효율적으로 관리하고 데이터 구조, 형태의 변화에 유연하게 대처하기 위해서는 데이터모델 관리가 필요하다. 본 논문에서는 사용자로 하여금 다양한 저장소의 데이터 모델을 관리하고 이로부터 동적으로 데이터를 생성하여 통합 관리할 수 있는 XML Schema 관리 툴을 제공한다.

• International Journal of Aeronautical and Space Sciences
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• 제14권1호
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• pp.91-98
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• 2013

The rigorous requirements of modern spacecraft missions necessitate a precise attitude determination strategy. This paper mainly researches that, based on three space-borne attitude sensors: 3-axis rate gyros, 3-antenna GPS receiver and star-sensor. To obtain global attitude estimation after an information fusion process, a feedback-involved Federated Kalman Filter (FKF), consisting of two subsystem Kalman filters (Gyros/GPS and Gyros/Star-sensor), is established. In these filters, the state equation is implemented according to the spacecraft's kinematic attitude model, while the residual error models of GPS and star-sensor observed attitude are utilized, to establish two observation equations, respectively. Taking the sensors' different update rates into account, these two subsystem filters are conducted under a variable step size state prediction method. To improve the fault tolerant capacity of the attitude determination system, this paper designs malfunction warning factors, based on the principle of ${\chi}^2$ residual verification. Mathematical simulation indicates that the information fusion strategy overwhelms the disadvantages of each sensor, acquiring global attitude estimation with precision at a 2-arcsecs level. Although a subsystem encounters malfunction, FKF still reaches precise and stable accuracy. In this process, malfunction warning factors advice malfunctions correctly and effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2801-2816
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• 2016

Owing to their low scalability, weak support on big data, insufficient data collaborative analysis and inadequate situational awareness, the traditional methods fail to meet the needs of the security data analysis. This paper proposes visualization methods to fuse the multi-source security data and grasp the network situation. Firstly, data sources are classified at their collection positions, with the objects of security data taken from three different layers. Secondly, the Heatmap is adopted to show host status; the Treemap is used to visualize Netflow logs; and the radial Node-link diagram is employed to express IPS logs. Finally, the Labeled Treemap is invented to make a fusion at data-level and the Time-series features are extracted to fuse data at feature-level. The comparative analyses with the prize-winning works prove this method enjoying substantial advantages for network analysts to facilitate data feature fusion, better understand network security situation with a unified, convenient and accurate mode.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.58-73
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• 2014

As is well known, cooperative sensing can significantly improve the sensing accuracy as compared to local sensing in cognitive radio networks (CRNs). However, a large number of cooperative secondary users (SUs) reporting their local detection results to the fusion center (FC) would cause much overhead, such as sensing delay and energy consumption. In this paper, we propose a fast cooperative sensing scheme, called double threshold fusion (DTF), to reduce the sensing overhead while satisfying a given sensing accuracy requirement. In DTF, FC respectively compares the number of successfully received local decisions and that of failed receptions with two different thresholds to make a final decision in each reporting sub-slot during a sensing process, where cooperative SUs sequentially report their local decisions in a selective fashion to reduce the reporting overhead. By jointly considering sequential detection and selective reporting techniques in DTF, the overhead of cooperative sensing can be significantly reduced. Besides, we study the performance optimization problems with different objectives for DTF and develop three optimum fusion rules accordingly. Simulation results reveal that DTF shows evident performance gains over an existing scheme.

• 한국조리학회지
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• 제14권4호
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• pp.134-149
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• 2008

This thesis is aimed to analyze the importance, satisfaction, perception and intake frequency of fusion menu in order to develop the market segmentations and marketing strategies for useful information on the fusion menu and its improvement in the food industries. The findings of the study are as follows: First, the study revealed that diet menu(low fat, low-cal) and vegetarian menu items have more influence on females than males in regard to the importance of fusion menu when examining gender. Second, the study revealed that Bulgogi pizza, Bulgogi burger, cheese cutlet, cheese kimbab, sweet and sour pork items have great influence on customers in their 10s while green tea latte, rice burger, Bulgogi pizza, kimchi hamburger items have a high effect on customers in their 20s in regard to the perception of fusion menu when examining age. Finally, the study revealed that the taste of food, the cleanliness of vessels, food hygiene, the freshness of food, the quality of menu, the portions of food, the nutrition of food, the speed of food service, food material harmony, the temperature of food, the flavor of food, distinctions from existing food, environment-friendly organic agriculture food material items have maintaining the good performance of fusion menus. It also showed that various strategies for price of fusion menu should be made when examining the IPA analysis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.257-262
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• 2004

The purpose of data fusion is collecting maximized information from combining the data attained from more than two same or different kind sensor systems. Data fusion of same kind sensor systems like optical imagery has been on focus, but recently, LIDAR emerged as a new technology for capturing rapidally data on physical surfaces and the high accuray results derived from the LIDAR data. Considering the nature of aerial imagery and LIDAR data, it is clear that the two systems provide complementary information. Data fusion is consisted of two steps, alignment and matching. However, the complementary information can only be fully utilized after sucessful alignment of the aerial imagery and lidar data. In this research, deal with centroid of building extracted from lidar data as control information for estimating exterior orientation parameters of aerial imagery relative to the LIDAR reference frame.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제60차 하계학술대회논문집 27권2호
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• pp.385-386
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• 2019

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification.