• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.221-222
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• 2015

ITER AC/DC Converter의 부하는 초전도 코일이며 이에 필요한 컨버터는 총 6종류(2상한:TF, 4상한:PF, CS, VS, CCU/L, CCS)가 있다. 이중 VS 컨버터(${\pm}1050V$, ${\pm}22.5kA$)는 6대가 직렬로 접속되어 운전되고 CS 컨버터(${\pm}1050V$, ${\pm}4.5kA$)는 4대가 직렬로 접속되어 운전한다. 이들 컨버터용 제어기의 개발 단계에서 실 부하상태를 준비하는 것은 어렵기 때문에 $RTDS^{TM}$ (Real Time Digital Simulator)를 이용하여 제어 대상인 High Power 부분과 초전도 코일의 동적 시스템 모델을 HILS(Hardware-in-the-Loop Simulation)로 구축하였다. 본 논문에서는 HILS 구축에 대한 상세한 내용과 이를 활용하여 Control 시스템을 검증한 결과를 서술하였다.

• Journal of the Korean earth science society
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• v.44 no.3
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• pp.185-195
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• 2023

The prolonged and heavy East Asian summer precipitation in 2020 may have been caused by an enhanced Madden-Julian Oscillation (MJO), which requires evaluation using forecast models. We examined the performance of GloSea6, an operational forecast model, in predicting the East Asian summer precipitation during July 2020, and investigated the role of MJO in the extreme rainfall event. Two experiments, CON and EXP, were conducted using different convection schemes, 6A and 5A, respectively to simulate various aspects of MJO. The EXP runs yielded stronger forecasts of East Asian precipitation for July 2020 than the CON runs, probably due to the prominent MJO realization in the former experiment. The stronger MJO created stronger moist southerly winds associated with the western North Pacific subtropical high, which led to increased precipitation. The strengthening of the MJO was found to improve the prediction accuracy of East Asian summer precipitation. However, it is important to note that this study does not discuss the impact of changes in the convection scheme on the modulation of MJO. Further research is needed to understand other factors that could strengthen the MJO and improve the forecast.

• 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 the Korean Society for Precision Engineering
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• v.29 no.1
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• pp.96-102
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• 2012

The current establishment of car engineering plastic piping polyethylene (PE) tube used as bonding state or part of the health or safety of fusion is very important. A part of these fusion methods to determine the soundness of the short-term trials and long-term tests can be largely classified. Typical tests included short-term strength, tensile strength, impact strength, compressive strength, resiliency and compression. Polyethylene (PE) pipes installed in the domestic terms of overall penetration rate of 45% has been used. However, polyethylene (PE) pipes have reliability problems, and these occurs mostly in part by defective welding. Therefore, the test is necessary for safety. Non-destructive methods (ultrasonic testing) are difficult to be used. Therefore, Polyethylene (PE) pipe are used. Fusion of thses materilas is necessary in these field however, its technical, and basic research has not been studied well. In this research, short-term strength of welding parts, its tensile strength, hardness, fatigue, and microstructure have been analyzed to find the optimum process conditions to improve mechanical properties.

• Journal of Microbiology
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• v.40 no.3
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• pp.205-210
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• 2002

To study interactions between $C_{4}$-dicarboxylic acid transport protein D and E$\sigma$$^{54}$ in the dctA promoter regulatory region, we used the challenge phage system. An ant＇-｀lac fusion was recombined onto the challenge phage, and this ant＇-｀lac fusion along with Pant and the R. meliloti dctA promoter regulatory region were cloned onto a plasmid. The plasmid bearing the ant＇-｀lac fusion was used as a reporter plasmid in a coupled transcription-translation system. Addition of purified $\sigma$$^{54}$ to the coupled system specifically repressed transcription of the plasmid-borne ant＇-｀lac fusion. When DCTD was added along with $\sigma$$^{54}$ to the coupled system, transcription of the ant＇-｀lac fusion was even further repressed, suggesting that DCTD may stabilize closed complexes between E$\sigma$$^{54}$ and the dctA promoter.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.70-77
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• 2024

A low energy ion irradiation system based on the deuterium arc ion source with a high perveance of 1 µP for a single extraction aperture has been successfully developed for the investigation of ion irradiation on plasma-facing components including the first mirror of plasma optical diagnostics system. Under the optimum operating condition for mirror testing, the ion source has a beam energy of 200 eV and a current density of 3.7 mA/cm². The ion source comprises a magnetic cusp-type plasma source, an extraction system, a target system with a Faraday cup, and a power supply control system to ensure stable long time operation. Operation parameters of plasma source such as pressure, filament current, and arc power with D₂ discharge gas were optimized for beam extraction by measuring plasma parameters with a Langmuir probe. The diode electrode extraction system was designed by IGUN simulation to optimize for 1 µP perveance. It was successfully demonstrated that the ion beam current of ~4 mA can be extracted through the 10 mm aperture from the developed ion source. The target system with the Faraday cup is also developed to measure the beam current. With the assistance of the power control system, ion beams are extracted while maintaining a consistent arc power for more than 10 min of continuous operation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• ETRI Journal
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• v.45 no.5
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• Transactions on Electrical and Electronic Materials
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• v.11 no.3
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• pp.134-137
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• 2010

We have proposed a transflective liquid crystal display (LCD) driven by the fringe field using a liquid crystal (LC) with a negative dielectric anisotropy. The device used different twist angles of the liquid crystals (LC) in the transmissive (T) and the reflective (R) regions when voltage is applied. With the optimization of the pixel electrode width and the distance between them, the LC directors in the R- and T-regions can be rotated by about $22.5^{\circ}$ and $45^{\circ}$ on an average, respectively. As a result, a high image quality transflective LCD with a single gap, a single gamma, and a wide viewing angle characteristics in both the R- and T- regions can be realized.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1702-1703
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• 2007

KSTAR(Korea Superconducting Tokamak Advanced Research)의 보조 가열장치 중에 하나인 중성입자 빔 가열장치의 열량을 실시간으로 측정할 수 있는 시스템을 개발하였다. 실시간으로 열량을 측정하기위해 중성입자 빔 발생장치의 말단에 위치한 열량계(Calorimeter)의 냉각수 입 출구 온도를 온도센서 중의 하나인 열전대를 이용하여 측정하고 이를 열량 측정을 위해 개발한 알고리듬을 이용하여 실시간으로 중성입자 빔의 열량으로 연산한다. 연산된 열량은 사용자 인터페이스 화면에 출력하여 매 실험마다 열량계에 입사된 열량을 실시간으로 확인이 가능하도록 하였다.