• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.164-166
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• 2022

본 논문에서는 ATSC 3.0 Layered-Division-Multiplexing(LDM) Multiple-Inputs-Multiple-Outputs(MIMO) 방송 시스템에서 Core-Layer 신호에 대한 새로운 Hybrid PGA(HPGA) 복호 기법을 제안하고자 한다. 제안된 기법은 각 수신단의 Enhanced-Layer 신호의 평균전력 대비 잡음 값에 따라 기존의 GA 기법과 PGA 기법을 선택적으로 적용하여 기존의 PGA 기법에 비해 큰 성능저하 없이 복호 복잡도는 약 25% 줄일 수 있다.

• Progress in Superconductivity and Cryogenics
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• v.11 no.3
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• pp.50-54
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• 2009

In Korea, 22.9kV 50MVA HTS (High Temperature Superconducting) cables and 630A/3kA hybrid SFCLs (Superconducting Fault Current Limiters) have been or are being developed by LS Cable, LS Industrial System, and Korea Electric Power Research Institute. They will be installed at Icheon 154kV Substation for real-power-distribution-system operation in 2010. This paper proposes specification of current limiting resistor/reactor for the SFCL and fault current condition of the HTS cable for applying the superconducting devices to Korean power distribution system, from the viewpoint of power system protection.

• Annual Conference of KIPS
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• 2020.05a
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• pp.523-525
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• 2020

In recent years, deep learning has been widely used in news recommendation systems. In the previous personalized news recommendation, a large number of CF-based methods, content-based or hybrid methods have been proposed. But most of the works are only modeling the user's interaction history, ignoring the hidden meaning of the user's continuous behaviors. In this paper, we propose to adopt the powerful Transformer model in order to understand the hidden meaning of the user's continuous behaviors in news recommendations. The experimental results prove the superiority of the transformer, and the AUC has been significantly improved as compared to the original model.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.794-802
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• 2022

The recovery of reasonable depth information from different scenes is a popular topic in the field of computer vision. For generating depth maps with better details, we present an efficacious monocular depth prediction framework with coordinate attention and feature fusion. Specifically, the proposed framework contains attention, multi-scale and feature fusion modules. The attention module improves features based on coordinate attention to enhance the predicted effect, whereas the multi-scale module integrates useful low- and high-level contextual features with higher resolution. Moreover, we developed a feature fusion module to combine the heterogeneous features to generate high-quality depth outputs. We also designed a hybrid loss function that measures prediction errors from the perspective of depth and scale-invariant gradients, which contribute to preserving rich details. We conducted the experiments on public RGBD datasets, and the evaluation results show that the proposed scheme can considerably enhance the accuracy of depth prediction, achieving 0.051 for log10 and 0.992 for δ<1.25³ on the NYUv2 dataset.

• Journal of Auto-vehicle Safety Association
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• v.8 no.2
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• pp.30-36
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• 2016

According to revolutionary developments in automobile technologies, various electronically controlled components of vehicles are rapidly increasing. A variety of advanced vehicles (hybrid vehicle, hydrogen fuel-cell vehicle, electric vehicle, etc.) using electrical energy source are increasing, too. The electromagnetic compatibility is getting more important for development of a vehicle because those advanced vehicles are equipped with more new electronic systems. In general, electromagnetic compatibility tests consist of an electromagnetic interference (EMI) test and an electromagnetic susceptibility (EMS) test. In this paper, in order to investigate the electromagnetic interference in the cabin of vehicle by various electric and electronic components of vehicles, a series of narrowband electromagnetic emission tests are conducted. For comparison, the several digital home appliances (smartphone under charging, laptop compuer and digital camera), which are used a lot in daily lives, are tested.

• Annual Conference of KIPS
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• 2008.11a
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• pp.936-939
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• 2008

차량 내부 네트워크(In-Vehicle Network)에 많은 관심이 고조되는 가운데 Time-Triggered 와 Event-Triggered 패러다임의 장점을 모두 갖춘 하이브리드(Hybrid) 프로토콜인 FlexRay 가 최근 들어 많은 자동차 분야 업체들로부터 주목 받으면서 이에 대한 다양한 성능 분석 시도가 이루어지고 있다. 하지만 FlexRay 프로토콜의 메시지 전송의 유연성(Flexibility)으로 인해 정확한 성능 분석에 많은 어려움이 있다. 이에 본 논문에서는 다양한 길이의 메시지를 고려한 동적 구간 분석 모델을 제안하였고, 이를 바탕으로 잔여 미니 슬롯 확률 분포(Empty Minislot Distribution)를 도출하였으며 FlexRay 메시지 셋(Set)를 이용한 실험을 통하여 특정 길이의 동적 구간에서 미니 슬롯 개수에 따른 잔여 미니 슬롯 발생확률 값의 변화를 확인하였다.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.325-332
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• 2024

Contemporary power systems demand efficient and sustainable technologies. Single-phase induction motors, while widely used, face efficiency challenges due to inherent rotor losses. Proposed solutions include the Line-start Permanent Magnet Synchronous Motor (LSPMSM), leveraging permanent magnets for enhanced energy density but facing demagnetization and cost issues. Alternatively, the Line-start Synchronous Reluctance Motor (LSRM) operates as a hybrid motor without permanent magnets, reducing rotor losses and potentially improving efficiency. This paper focuses on designing an LSRM rotor for air conditioner compressors, analyzing start-up characteristics and efficiency through finite element analysis. A comparative study with single-phase induction motors provides insights for future motor technology selection, balancing efficiency and other requirements.

• ETRI Journal
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• v.46 no.1
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• pp.137-153
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• 2024

This study introduces a new approach in Korean morphological analysis combining dictionary-based techniques with Transformer-based deep learning models. The key innovation is the use of a BERT-based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first-stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary-based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.458-476
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• 2024

To address the issue of class imbalance in network traffic data, which affects the network intrusion detection performance, a combined framework using transformers is proposed. First, Tomek Links, SMOTE, and WGAN are used to preprocess the data to solve the class-imbalance problem. Second, the transformer is used to encode traffic data to extract the correlation between network traffic. Finally, a hybrid deep learning network model combining a bidirectional gated current unit and deep neural network is proposed, which is used to extract long-dependence features. A DNN is used to extract deep level features, and softmax is used to complete classification. Experiments were conducted on the NSLKDD, UNSWNB15, and CICIDS2017 datasets, and the detection accuracy rates of the proposed model were 99.72%, 84.86%, and 99.89% on three datasets, respectively. Compared with other relatively new deep-learning network models, it effectively improved the intrusion detection performance, thereby improving the communication security of network data.

• Journal of Satellite, Information and Communications
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• v.4 no.2
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• pp.28-33
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• 2009

In this paper, we evaluate the performance of various diversity techniques which can contribute to provide efficient multimedia broadcasting services via hybrid/integrated satellite and terrestrial network. Space-time coding can achieve the diversity gain in a multi-path environment without additional bandwidth requirement. Recent study results reported that satellite systems can achieve high diversity gains by appropriate utilization of STC and/or forward error correction. Based on these previous study results, we present various cooperative diversity techniques by combing STC and rate compatible turbo codes in order to realize the transmit diversity for the mobile satellite system. The satellite and several terrestrial repeaters operate in unison to send the encoded signals, so that receiver may realize diversity gain. The results demonstrated in this paper can be utilized in future system implementation.