• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1242-1250
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• 2019

Super resolution technique aims to convert a low-resolution image with coarse details to a corresponding high-resolution image with refined details. In the past decades, the performance is greatly improved due to progress of deep learning models. However, universal solution for various objects is a still challenging issue. We observe that learning super resolution with a general dataset has poor performance on faces. In this paper, we propose a super resolution fusion scheme that works well for both general- and face datasets to achieve more universal solution. In addition, object-specific feature extractor is employed for better reconstruction performance. In our experiments, we compare our fusion image and super-resolved images from one- of the state-of-the-art deep learning models trained with DIV2K and FFHQ datasets. Quantitative and qualitative evaluates show that our fusion scheme successfully works well for both datasets. We expect our fusion scheme to be effective on other objects with poor performance and this will lead to universal solutions.

• Nuclear Engineering and Technology
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• 제50권7호
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• pp.1060-1067
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• 2018

A feasibility of reusing option of spent nuclear fuel in a fusion-fission hybrid system without partitioning was checked as an alternative option of pyro-processing with critical reactor system. Neutronic study was performed with MCNP 6.1 for this option, direct reuse of spent PWR fuel (DRUP). Various options with DRUP fuel were compared with the reference design concept; transmutation purpose blanket with (U-TRU)Zr fuel loading connected with pyro-processing. Performance parameters to be compared are transmutation performance of transuranic (TRU) nuclides, required fusion power and tritium breeding ratio (TBR). When blanket part is loaded only with DRUP, initial $k_{eff}$ level becomes too low to maintain a practical subcritical system, increasing the required fusion power. In this case, production rate of TRU nuclides exceeds the incineration rate. Design optimization is done for combining DRUP fuel with (U-TRU)Zr fuel. Reactivity swing is reduced to about 2447 pcm through fissile breeding compared to (U-TRU)Zr fuel option. Therefore, a required fusion power is reduced and tritium breeding performance is improved. However, transmutation performance with TRU nuclides especially $^{241}Am$ is degraded because of softening effect of spectrum. It is known that partitioning and transmutation should be accompanied with fusion-fission hybrid system for the effective transmutation of TRU.

• Journal of electromagnetic engineering and science
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• 제18권3호
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• pp.206-211
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• 2018

In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.783-789
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• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권12호
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• pp.3061-3080
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• 2012

To improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. In this paper, we focus on the optimization of cooperative spectrum sensing in which multiple cognitive users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in multiple cross-over cognitive radio networks. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, the optimal threshold of the energy detector is derived in both noncooperative single-user and cooperative multiuser sensing scenarios. Under hard information fusion, the optimal randomized rule and the optimal decision threshold are derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each cognitive user which is randomly distributed in the multiple cross-over cognitive radio networks.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.647-648
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• 2008

Traditional FTL and flash file systems based of NAND flash memory may not be adaptively applied to new fusion flash memory which combines the advantages of NAND and NOR flash memory. In this paper, we propose a FTL performance measurement tool using Multi Block Erase function of fusion flash memory. The performance measurement tool shows that multi block erase function can be effectively utilized in performance enhancement of garbage collection for fusion flash memory.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.69-77
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• 2017

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

• International journal of advanced smart convergence
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• 제9권1호
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• pp.193-201
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• 2020

The main objective of this study is to investigate the impact of additional modalities on the performance of emotion recognition using speech, facial expression and physiological measurements. In order to compare different approaches, we designed a feature-based recognition system as a benchmark which carries out linear supervised classification followed by the leave-one-out cross-validation. For the classification of four emotions, it turned out that bimodal fusion in our experiment improves recognition accuracy of unimodal approach, while the performance of trimodal fusion varies strongly depending on the individual. Furthermore, we experienced extremely high disparity between single class recognition rates, while we could not observe a best performing single modality in our experiment. Based on these observations, we developed a novel fusion method, called parametric decision fusion (PDF), which lies in building emotion-specific classifiers and exploits advantage of a parametrized decision process. By using the PDF scheme we achieved 16% improvement in accuracy of subject-dependent recognition and 10% for subject-independent recognition compared to the best unimodal results.

• 정보관리학회지
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• 제32권1호
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• pp.7-22
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• 2015

이 논문은 시스템 성능을 최적화하기 위해 정적 및 동적 튜닝 방법을 이용한 웹 융합검색 연구의 내용을 보고합니다. 기존의 융합 방식을 넘어선 "다이나믹 튜닝"이라는 과정을 도입하여 웹의 다양한 정보소스의 기여를 최적화 시킬 수 있는 융합 공식을 생성하는 방법을 조사한 이 연구의 결과는 웹 검색 환경의 풍요로운 여러 데이터 소스를 활용하는 것이 효과적인 전략이라는 것을 보여주었습니다. 본 연구에서는 즉각적인 시스템 피드백 인지분석을 기반으로 융합 매개 변수를 미세 조정하는 반복적 인 다이나믹 튜닝 과정을 통해 크게 검색 성능을 향상시킬 수 있었습니다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2420-2433
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• 2014

Fusing the scores of multibiometrics is a very promising approach to improve the overall system's accuracy and the verification performance. In recent years, there are several approaches towards studying score level fusion of several biometric systems. However, most of them does not consider the genuine and imposter score distributions and result in a higher equal error rate usually. In this paper, a novel score level fusion approach of different biometric systems (dual iris, thermal and visible face traits) based on $Acz{\acute{e}}l$-Alsina triangular norm is proposed. It achieves higher identification performance as well as acquires a closer genuine distance and larger imposter distance. The experimental tests are conducted on a virtual multibiometrics database, which merges the challenging CASIA-Iris-Thousand database with noisy samples and the NVIE face database with visible and thermal face images. The rigorous results suggest that significant performance improvement can be achieved after the implementation of multibiometrics. The comparative experiments also ascertain that the proposed fusion approach outperforms the state-of-art verification performance.