• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.39-44
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• 2009

In order to make the effect of the navigation system more direct, the paper proposes a thought of vehicle navigation system based on Video-GIS. A semantic framework has been defined whose core is focused on the integration and interaction of video and spatial information, which supports full content retrieval based on multimodal metadata extraction and fusion, and supports kinds of wireless access mode. Furthermore, requirements of prototype system are discussed. Then the design and implementation of framework are discussed. Next, describe the key ideas and technologies involved. Finally, we point out its future research trend.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.631-633
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• 2005

다양한 형태의 상황 정보를 결합하여 추론하기 위해 베이지안 네트워크를 많이 사용한다. 그러나 일반 베이지안 네트워크는 각 노드의 상태가 이산적이기 때문에, 연속적이거나 여러 상태가 동시에 존재할 수 있는 현실의 상황 정보를 처리하기 어렵다. 본 논문에서는 이와 같은 베이지안 네트워크의 단점을 보완하기 위해 다양한 형태의 상황 정보를 퍼지를 통해 전처리하여 베이지안 네트워크를 통해 추론하는 퍼지 베이지안 네트워크를 제안한다. 유용성을 보이기 위해 음악 추천 에이전트를 설계하여 일반 베이지안 네트워크와 비교 실험한 결과, 제안한 방법으로 다양한 상황 정보에 대해 유연한 처리가 가능함을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.811-819
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• 2014

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.72-77
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• 2008

Identification of humans from multiple view points is an important task for surveillance and security purposes. For optimal performance the system should use the maximum information available from sensors. Multimodal biometric systems are capable of utilizing more than one physiological or behavioral characteristic for enrollment, verification, or identification. Since gait alone is not yet established as a very distinctive feature, this paper presents an approach to fuse face and gait for identification. In this paper we will use the single camera case i.e both the face and gait recognition is done using the same set of images captured by a single camera. The aim of this paper is to improve the performance of the system by utilizing the maximum amount of information available in the images. Fusion in considered at decision level. The proposed algorithm is tested on the NLPR database.

• Journal of Korea Game Society
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• v.9 no.1
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• pp.93-103
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• 2009

A Serious Game helps the learners to recognize the problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Owing not only to this game-like fun but also to the educational effect, The Serious Game can be usefully applied to education and training in the areas of scientific technology and industrial technology. This study proposes the Serious Game that users can apply to biotechnology by using intuitive multi-modal interfaces. In this study, a stereoscopic monitor is used to make three dimensional molecular structures, and multi-modal interface is used to efficiently control. Based on a such system, this study easily solved the docking simulation function, which is one of the important experiments, by applying these game factors. For this, we suggested the level-up concept as a game factor that depends on numbers of objects and users. The proposed system was evaluated in performance comparison in result time of a new drug design process on AIDS virus with previous approach.

• Journal of KIISE:Software and Applications
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• v.34 no.4
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• pp.377-388
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• 2007

In this paper, we propose a multi-modal biometric system based on face, iris and fingerprint recognition system. To effectively aggregate two systems, we use statistical distribution models based on matching values for genuine and impostor, respectively. And then, We performed reveal fusion algorithms including weighted summation, Support Vector Machine(SVM), Fisher discriminant analysis, Bayesian classifier. From the various experiments, we found that the performance of multi-modal biometric system was influenced with the normalization methods and classifiers.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.476-486
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• 2019

Objective : The functional information of $^{11}C$-methionine positron emission tomography (MET-PET) images can be applied for Gamma knife radiosurgery (GKR) and its image quality may affect defining the tumor. This study conducted the phantom-based evaluation for geometric accuracy and functional characteristic of diagnostic MET-PET image co-registered with stereotactic image in Leksell $GammaPlan^{(R)}$ (LGP) and also investigated clinical application of these images in metastatic brain tumors. Methods : Two types of cylindrical acrylic phantoms fabricated in-house were used for this study : the phantom with an array-shaped axial rod insert and the phantom with different sized tube indicators. The phantoms were mounted on the stereotactic frame and scanned using computed tomography (CT), magnetic resonance imaging (MRI), and PET system. Three-dimensional coordinate values on co-registered MET-PET images were compared with those on stereotactic CT image in LGP. MET uptake values of different sized indicators inside phantom were evaluated. We also evaluated the CT and MRI co-registered stereotactic MET-PET images with MR-enhancing volume and PET-metabolic tumor volume (MTV) in 14 metastatic brain tumors. Results : Imaging distortion of MET-PET was maintained stable at less than approximately 3% on mean value. There was no statistical difference in the geometric accuracy according to co-registered reference stereotactic images. In functional characteristic study for MET-PET image, the indicator on the lateral side of the phantom exhibited higher uptake than that on the medial side. This effect decreased as the size of the object increased. In 14 metastatic tumors, the median matching percentage between MR-enhancing volume and PET-MTV was 36.8% on PET/MR fusion images and 39.9% on PET/CT fusion images. Conclusion : The geometric accuracy of the diagnostic MET-PET co-registered with stereotactic MR in LGP is acceptable on phantom-based study. However, the MET-PET images could the limitations in providing exact stereotactic information in clinical study.