• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.781-782
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• 2007

• 대한지역사회영양학회지
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• 제17권4호
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• pp.376-389
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• 2012

The purpose of this study was to develop a fusion-food cooking program and apply that to sixth grade elementary school students, and to investigate its' effects on their knowledge, preferences, and perceptions of Korean traditional foods. The program focused on ten components. Students learned the general features of Korean traditional foods and Western foods in the 1st lesson. They learnt about kinds, nutrition value, and histories of kimchi, Tteok (rice cake), and Jeon (pan fried food), and cooked two fusion-foods of kimchi, Tteok, and Jeon each. As a result of learning about those food items, the students advanced their knowledge (p < 0.001) of kimchi, Tteok, and Jeon. Their preferences for Tteok (p < 0.05) and Jeon (p < 0.01) were increased, but those for kimchi remained unchanged. The interest (p < 0.05) and preferences (p < 0.01) for the general Korean traditional foods were increased. In conclusion, our results suggested that the fusion-food cooking program had the ability to improve elementary school students' perceptions of Korean traditional foods by increasing their knowledge, preferences, and interest in them. Furthermore it was considered that the program could help students understand the value of Korean traditional foods and in turn may encourage them to consume such food items more frequently. Since the fusion-food cooking activity program can be a good learning program as shown by the results of this study, more fusion-foods cooking activity programs, which are not discussed in this study, should be evaluated and developed in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• 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.

• 수산해양교육연구
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• 제25권5호
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• pp.1031-1045
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• 2013

The purpose of this study was to analyze on the convergence for knowledge fusion in the filed of the engineering, science, aesthetics, humanities and social sciences. For the study, the related literatures were reviewed focusing on the convergence in the basis of the inter-disciplinary. In addition, interviews with 5 professors in the field of the engineering, science, aesthetics, humanities and social sciences were analyzed. The keys of analysis were perspective of academic disciplines. The findings of this study were as follows; most of professors recognized the inter-disciplinary of engineering, science, aesthetics, humanities and social sciences. But, there were some barriers engineering of professors in inter-disciplinary.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1467-1470
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• 2003

In order to enhance the productivity of design and manufacture of injection molds, various computer-aided systems have been developed and introduced to mold manufacturers. The customized 3-D CAD systems for mold design is one of the most representative computer-aided system. However, these systems usually do not provide a convenient way to access and manage design knowledge implemented in them. To overcome this shortage, knowedge-based engineering systems have been developed, and now they are imbedded as modules of commercial CAD systems. In this paper, we introduced a 3-D design system for standard mold base based on Knowledge Fusion, a commercialized KBE system imbedded in Unigraphics. By introducing KBE system, design knowledge can be exposed to the users and modified by the end-users.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 추계 종합학술대회 논문집
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• pp.702-705
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• 2006

정보의 폭발적인 증가로 인해 연구 개발을 위한 전 과정 중 연구동향 분석에 많은 시간이 소모되고 있다 최근 특정 분야의 지식이 연구개발이나 제품개발로 이루어지던 시대에서 융합지식을 통한 연구개발이나 제품생산으로 빠르게 진화하고 있다. 이러한 패러다임을 수용하기 위해 기존의 독립적이고 단편적인 정보로부터 융합정보를 제공할 수 있는 체계로의 전환이 필요하게 되었다. 또한 과학 기술 정책 및 산업 정책을 수립하기 위해 최근 과학, 기술, 산업의 지식 흐름에 대한 연구가 활발히 진행되고 있으나 정량적인 분석을 활용하기란 매우 어려운 문제이다. 왜냐하면 과학-기술간 지식흐름을 분석할 수 있는 정보자원이 존재하지 않기 때문이다. 이 연구는 연구개발이나 과학기술정책 및 산업정책에 활용할 수 있는 특허정보와 학술 문헌간 강 결합 연계 체제를 갖는 프레임웍을 개발하고자 한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.97-98
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• 2006

Fusion of decisions from multiple distributed sensor nodes is studied in this work. Based on the canonical parallel fusion model, we derive the optimal likelihood ratio based fusion rule with the assumptions of the generalized Gaussian noise model and the arbitrary fading channel. This optimal fusion rule, however, requires the complete knowledge of the channels and the detection performance of local sensor nodes. To mitigate these requirements and to provide near optimum performance, we derive suboptimum fusion rules by using high and low signal-to-noise ratio (SNR) approximations to the optimal fusion rule. Performance evaluation is conducted through simulations.

• BMB Reports
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• 제49권6호
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• pp.303-304
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• 2016

Myoblast fusion is important for skeletal muscle formation. Even though the knowledge of myoblast fusion mechanism has accumulated over the years, the initial signal of fusion is yet to be elucidated. Our study reveals the novel function of a phosphatidylserine (PS) receptor, stabilin-2 (Stab2), in the modulation of myoblast fusion, through the recognition of PS exposed on myoblasts. During differentiation of myoblasts, Stab2 expression is higher than other PS receptors and is controlled by calcineurin/NFAT signaling on myoblasts. The forced expression of Stab2 results in an increase in myoblast fusion; genetic ablation of Stab2 in mice causes a reduction in muscle size, as a result of impaired myoblast fusion. After muscle injury, muscle regeneration is impaired in Stab2-deficient mice, resulting in small myofibers with fewer nuclei, which is due to reduction of fusion rather than defection of myoblast differentiation. The fusion-promoting role of Stab2 is dependent on its PS-binding motif, and the blocking of PS-Stab2 binding impairs cell-cell fusion on myoblasts. Given our previous finding that Stab2 recognizes PS exposed on apoptotic cells for sensing as an "eat-me" signal, we propose that PS-Stab2 binding is required for sensing of a "fuse-me" signal as the initial signal of myoblast fusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.287-304
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• 2012

Spectrum sensing is an important functionality for cognitive users to look for spectrum holes before taking transmission in dynamic spectrum access model. Unlike previous works that assume perfect knowledge of the SNR of the signal received from the primary user, in this paper we consider a realistic case where the SNR of the primary user's signal is unknown to both fusion center and cognitive radio terminals. A Kalman filter based adaptive Takagi and Sugeno's fuzzy system is designed to make the global spectrum sensing decision based on the observed energies from cognitive users. With the capacity of adapting system parameters, the fusion center can make a global sensing decision reliably without any requirement of channel state information, prior knowledge and prior probabilities of the primary user's signal. Numerical results prove that the sensing performance of the proposed scheme outperforms the performance of the equal gain combination based scheme, and matches the performance of the optimal soft combination scheme.