• Smart Media Journal
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• v.12 no.5
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• pp.28-35
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• 2023

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1099-1107
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• 2013

This study proposes a weight reduction design for urban transit, specifically, a Korean EMU carbody made of aluminum extrusion profiles, according to size optimization and useful material changes. First, the thickness of the under-frame, side-panels, and end-panels were optimized by the size optimization process, and then, the weight of the Korean EMU carbody could be reduced to approximately 14.8%. Second, the under-frame of the optimized carbody was substituted with a frame-type structure made of SMA 570, and then, the weight of the hybrid-type carbody was 3.8% lighter than that of the initial K-EMU. Finally, the under-frame and the roof-panel were substituted with a composite material sandwich to obtain an ultralight hybrid-type carbody. The weight of the ultralight hybrid-type carbody was 30% lighter than that of the initial K-EMU. All the resulting carbody models satisfied the design regulations of the domestic Performance Test Standard for Electrical Multiple Unit.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.152-153
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• 2021

고성능 딥러닝 모델은 학습과 추론 과정에서 고비용의 전산 자원과 많은 연산량을 필요로 하여 이에 따른 개발 환경과 많은 학습 시간을 필요로 하여 개발 지연과 한계가 발생한다. 따라서 HW 또는 SW 개선을 통해 파라미터 수, 학습 시간, 추론시간, 요구 메모리를 줄이는 연구가 지속 되어 왔다. 본 논문은 EfficientNet에서 사용된 Linear Bottleneck을 변경하여 정확도는 소폭 감소 하지만 기존 모델의 파라미터를 55%로 줄이는 경량화 모델을 제안한다.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.9-17
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• 2023

Deep learning with large amount of computations is difficult to implement on micro-sized IoT devices or moblie devices. Recently, lightweight deep learning technologies have been introduced to make sure that deep learning can be implemented even on small devices by reducing the amount of computation of the model. Quantization is one of lightweight techniques that can be efficiently used to reduce the memory and size of the model by expressing parameter values with continuous distribution as discrete values of fixed bits. However, the accuracy of the model is reduced due to discrete value representation in quantization. In this paper, we introduce various quantization techniques to correct the accuracy. We selected APoT and EWGS from existing quantization techniques, and comparatively analyzed the results through experimentations The selected techniques were trained and tested with CIFAR-10 or CIFAR-100 datasets in the ResNet model. We found out problems with them through experimental results analysis and presented directions for future research.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.239-252
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• 2005

Ubiquitous Sensor Network(USN) , the core technology for the Ubiquitous environments ,must be operated in the restrictive battery capacity and computing. From this cause, USN needs the lightweight design for low electric energy and the minimum computing. The previous mutual authentication. based on J$\emptyset$sang's trust model, in USN has a character that makes the lightweight mutual authentication possible in conformity with minimum computing. But, it has an imperfection at the components of representing the trust from a lightweight point of view. In this paper, we improve on the J$\emptyset$sang's trust model to apply a lightweight mutual authentication in USN. The proposed trust model in USN defines the trust information with the only degree of trust-entity(x)'s belief. The defined trust information has a superiority over the J$\emptyset$sang's trust model from a computing Point of view. because it computes information by Probability and logic operation(AND).

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.247-249
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• 2005

UTM-02 모델의 초기형상을 기반으로 차체 프레임 구조물에 대한 경량화 설계를 수행하였다. 전체 프레임을 14개의 부재로 나누었으며, 부재 두께를 설계변수로 정하였다. 효율적인 설계최적화를 수행하기 위하여 반응면기법을 이용하였다. 반응면기법을 통해 근사된 목적함수와 제한조건을 이용하여 최적화를 수행하였으며 각 설계변수들에 대한 감도도 산출하였다.

• Journal of Space Technology and Applications
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• v.3 no.1
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• pp.44-57
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• 2023

In order for a latecomer in deep-space exploration such as Korea to quickly keep pace with advanced deep-space exploration countries in the mutually-beneficial space exploration market, it is essential to derive a deep-space probe reference model that can reduce development period and cost. In this paper, concept and configuration for the deep-space probe reference model consisting of basic, lightweight, and expansion types are newly presented, which are based on commonly required designs for various deep-space probes. The proposed configuration adopts modular design so that the expandability and design/implementation efficiency are improved. In addition, the electrical system design pursuing lightweight and expandability is also described, which is applicable to the proposed three-types of deep-space probe reference model.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.269-272
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• 2006

최근 들어, 유비쿼터스 홈네트워크에 대한 관심이 높아지고 있지만, 기능 구현에 초점을 맞추고 있다. 이러한 홈네트워크는 단일 서비스가 아닌 다양한 서비스 집합으로서의 성격이 강하므로 세분화된 보안 요구사항과 제한된 자원에서의 원활한 서비스를 위해 시스템 경량화는 필수적 요소이다. 이에 본 논문에서는 유비쿼터스 홈네트워크 환경에서 요구하는 보안성 및 경량화를 고려한 새로운 침입탐지 모델을 제안한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.594-597
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• 2014

본 논문에서는 국제 태양광 자동차 대회를 참가하는 태양광 자동차 프론트 너클의 경량화 설계에 관한 연구를 진행한다. 이를 위해 Cattle grid 를 포함한 실제 주행환경과 태양광 자동차를 동역학 시뮬레이션 모델로 구성하고 대회에서 차량의 평균속도인 70Km/h 로 주행 시, 서스펜션에서 발생되는 동하중을 측정하였다. 프론트 너클을 유한요소로 구성하고 다물체 동역학 시뮬레이션에서 도출된 하중들로 위상최적기법을 통해 프론트 너클의 경량화를 이루었다. 마지막으로 피로해석을 수행하여 그 타당성을 검증하였다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.764-765
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• 2017

A trajectory analysis program was developed using a 3 DOF model to analyze KSLV-II performance with the reducing weight. The program estimates the injection performance on the SSO orbit, which is determined as payload weight for the orbit, with various vehicle structural ratios. The KSLV-II can transport 2.58 ton to the target orbit with a reduced structural ratio similar to the Angara rockets.