• Journal of Powder Materials
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• v.27 no.4
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• pp.305-310
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• 2020

In this study, partially dry transfer is investigated to solve the problem of fully dry transfer. Partially dry transfer is a method in which multiple layers of graphene are dry-transferred over a wet-transferred graphene layer. At a wavelength of 550 nm, the transmittance of the partially dry-transferred graphene is seen to be about 3% higher for each layer than that of the fully dry-transferred graphene. Furthermore, the sheet resistance of the partially dry-transferred graphene is relatively lower than that of the fully dry-transferred graphene, with the minimum sheet resistance being 179 Ω/sq. In addition, the fully dry-transferred graphene is easily damaged during the solution process, so that the performance of the organic photovoltaics (OPV) does not occur. In contrast, the best efficiency achievable for OPV using the partially dry-transferred graphene is 2.37% for 4 layers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.229-240
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• 2012

Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.119-120
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• 2015

ITER 한국사업단은 AC/DC Converter 최종설계검토(FDR)를 2014년 4월에 끝내고, 제작에 관련된 준비사항 점검인 MRR (Manufacturing Readiness Review)를 2014년 10월에 수행하였다. 1차 제작공정인 CCU/L 컨버터와 관련 I&C System은 2014년 11월부터 제작에 착수하여 곧 FAT(Factory Acceptance Test)를 앞두고 있다. 이 시점에서 중요한 것은 FAT를 앞둔 제작된 기기가 ITER의 설계요건을 충분히 만족하는지에 대한 검증을 하는 것이다. 본 논문은 초전도자석 및 컨버터의 이상상태 또는 고장 시에 장치를 보호하는 중요한 Plant Interlock System(PIS)이 ITER의 설계요건을 충실히 반영하였는지 확인하기 위한 요건추적매트릭스(RTM) 기법을 소개하고, Interlock System의 중요기능인 사고 시 초전도 코일에 충전된 에너지를 급속 방전하기 위한 장치인 DLIB와의 연계기능에 적용한 예를 보이고자 한다.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1860-1865
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• 2019

The confinement degradation of the energetic particles during RMP would be a key issue in success of realizing the successful energy production using fusion plasma, because a 3.5 MeV energetic alpha particle should be able to sustain the burning plasma after the ignition. As KSTAR recent results indicate the generation of high-performance plasma(${\beta}_p{\sim}3$), the confinement of the energetic particles is also an important key aspect in neutral beam driven plasma. In general, the measured absolute value of the neutron intensity is generally used for to estimating the confinement time of energetic particles by comparing it with the theoretical value based on transport calculations. However, the availability of, but for its calculation process, many accurate diagnostic data of plasma parameters such as thermal and incident fast ion density, are essential to the calculation process. In this paper, the time evolution of the neutron signal from an He3 counter during the beam blank has permitted to facilitate the estimation of the slowing down time of energetic particles and the method is applied to investigate the fast ion effect on ELM suppressed KSTAR plasma which is heated by high energy deuterium neutral beams.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.462-472
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• 2021

With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• Journal of Microbiology and Biotechnology
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• v.33 no.11
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• pp.1513-1520
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• 2023

Kex2 protease (Kex2p) is a membrane-bound serine protease responsible for the proteolytic maturation of various secretory proteins by cleaving after dibasic residues in the late Golgi network. In this study, we present an application of Kex2p as an alternative endoprotease for the in vitro processing of recombinant fusion proteins produced by the yeast Saccharomyces cerevisiae. The proteins were expressed with a fusion partner connected by a Kex2p cleavage sequence for enhanced expression and easy purification. To avoid in vivo processing of fusion proteins by Kex2p during secretion and to guarantee efficient removal of the fusion partners by in vitro Kex2p processing, P₁', P₂', P₄, and P₃ sites of Kex2p cleavage sites were elaborately manipulated. The general use of Kex2p in recombinant protein production was confirmed using several recombinant proteins.

• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.25 no.3
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• pp.253-259
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• 2023

Precise evaluation of the feeders, fistulous points, and draining veins plays a key role for successful embolization of intracranial dural arteriovenous fistulas (DAVF). Digital subtraction angiography (DSA) is a gold standard diagnostic tool to assess the exact angioarchitecture of DAVFs. With the advent of new image postprocessing techniques, we lately have been able to apply image fusion techniques with two different image sets obtained with flat panel detector rotational angiography. This new technique can provide additional and better pretherapeutic information of DAVFs over the conventional 2D and 3D angiographies. In addition, it can be used during the endovascular treatment to help the accurate and precise navigation of the microcatheter and microguidwire inside the vessels and identify the proper location of microcatheter in the targeted shunting pouch. In this study, we briefly review the process of an image fusion technique and introduce our clinical application for treating DAVFs, especially focused on the transvenous embolization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2103-2118
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• 2024

Chinese herbal slices (CHS) automated recognition based on computer vision plays a critical role in the practical application of intelligent Chinese medicine. Due to the complexity and similarity of herbal images, identifying Chinese herbal slices is still a challenging task. Especially, easily-confused CHS have higher inter-class and intra-class complexity and similarity issues, the existing deep learning models are less adaptable to identify them efficiently. To comprehensively address these problems, a novel tiny easily-confused CHS dataset has been built firstly, which includes six pairs of twelve categories with about 2395 samples. Furthermore, we propose a ResNeSt-CHS model that combines multilevel perception fusion (MPF) and perceptive sparse fusion (PSF) blocks for efficiently recognizing easilyconfused CHS images. To verify the superiority of the ResNeSt-CHS and the effectiveness of our dataset, experiments have been employed, validating that the ResNeSt-CHS is optimal for easily-confused CHS recognition, with 2.1% improvement of the original ResNeSt model. Additionally, the results indicate that ResNeSt-CHS is applied on a relatively small-scale dataset yet high accuracy. This model has obtained state-of-the-art easily-confused CHS classification performance, with accuracy of 90.8%, far beyond other models (EfficientNet, Transformer, and ResNeSt, etc) in terms of evaluation criteria.