• 대한기계학회논문집A
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• 제21권10호
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• pp.1561-1570
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• 1997

This study explores the treatment of the max-value cost function over a parameter interval in parametric optimization. To avoid the computational burden of the transformation treatment using an artificial variable, a direct treatment of the original max-value cost function is proposed. It is theoretically shown that the transformation treatment results in demanding an additional equality constraint of dual variables as a part of the Kuhn-Tucker necessary conditions. Also, it is demonstrated that the usability and feasibility conditions on the search direction of the transformation treatment retard convergence rate. To investigate numerical performances of both treatments, typical optimization algorithms in ADS are employed to solve a min-max steady-state response optimization. All the algorithm tested reveal that the suggested direct treatment is more efficient and stable than the transformation treatment. Also, the better performing of the direct treatment over the transformation treatment is clearly shown by constrasting the convergence paths in the design space of the sample problem. Six min-max transient response optimization problems are also solved by using both treatments, and the comparisons of the results confirm that the performances of the direct treatment is better than those of the tranformation treatment.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2468-2478
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• 2018

We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.

• 한국정보통신학회논문지
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• 제12권10호
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• pp.1875-1882
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• 2008

최근 IEEE 802.16 WiMAX에서는 인터넷 기반의 다양한 서비스와 애플리케이션의 빈번한 사용으로 인하여 저비용, 고효율의 특성을 가지는 이동 단말기의 사용이 일반화되고 있다. 이동 단말기의 사용이 일반화되면서 고속 인터넷 서비스의 보안 문제를 해결하기 위한 연구가 IEEE 802.16e 표준을 중심으로 연구되고 있다. 이 논문에서는 IEEE 802.16 WiMAX의 보안 요구사항을 충족하기 위해 IEEE 802.16e 표준에서 제공하는 기본 기능이외에 SS의 인증부하 및 보안공격(reply 공격과 man-in-the-middle 공격)에 안전한 보안 메커니즘을 제안한다. 제안된 메커니즘은 SS와 BS가 생성한 난수와 비밀값을 이용하여 TEK과 데이터 암호에 필요한 키 정보를 교환한다. 또한 SS의 초기 인증정보와 인증서를 이용하여 BS의 추가 인증 과정을 수행하지 않도록 하여 BS의 성능 부하를 줄인다.