• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.12
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• pp.1965-1970
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• 2009

This study examined the effect of surfactant pretreatment on the pectinase-treated cotton/chitosan blends by weight loss and properties such as water absorbency, dyeability, tensile strength, pilling property, and surface morphology. The weight loss of cotton/chitosan blends was 1.5% by the surfactant pretreatment/pectinase treatment. The water absorbency and dyeability of samples showed a significant improvement by the surfactant pretreatment/pectinase treatment. The tensile strength and pilling property of treated fabrics showed no change. The water absorbency and dyeability of pectinase treated samples improved with the pretreatment of the surfactant without damaging the fibers.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.239-241
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• 2012

최근 클라우드 서비스 공급자들은 세계적으로 증가하는 고객의 수요에 대응하기 위하여, 다양한 지역에 데이터센터들을 구축하고 시스템의 규모를 확장시키고 있다. 클라우드는 통합된 자원을 이용하기 때문에 부하 분산(load balancing)을 통해서 시스템의 성능 및 안정성을 향상시킬 수 있다. 하지만 지역적으로 분산된 데이터센터들을 운영하는 클라우드 컴퓨팅 환경에서는 자원 부하의 분산과 서비스 응답 속도(service response time) 사이의 상관 관계를 고려할 필요가 있다. 그럼에도 불구하고 대부분의 클라우드 서비스 공급자들은 고정된 최소 서비스 응답 속도를 서비스 수준 협약(SLA: Service-Level Agreements)을 제시하고 있는 현실이다. 이와 같이 SLA에 최소 서비스 응답 속도를 임의로 고정할 경우, 거리차이가 있는 데이터센터(SLA에 명시된 응답 속도 보다 느린 데이터센터)에는 부하가 분산되기 어렵다. 따라서 지역적으로 분산된 데이터센터 환경에서는 응답 속도에 대한 가변 SLA를 적용할 필요가 있다. 본 연구에서는 지역적으로 분산된 데이터센터를 운영하는 클라우드 컴퓨팅 환경에서 가변 응답 속도를 적용하기 위한 방법으로써 서비스 응답 속도와 가격을 고려한 자동화된 SLA 협상 메커니즘을 적용하고 그 유효성을 평가하였다. 시뮬레이션을 이용한 실험 결과를 통해서 SLA 협상을 통한 가변 SLA를 이용하는 방법이 고정된 SLA를 이용하는 방법에 비해서 더 높은 SLA 보장률을 지원하고 클라우드 공급자에게 더 높은 수익을 제공함을 확인하였다.

• Environmental and Resource Economics Review
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• v.4 no.2
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• pp.243-265
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• 1995

본 논문은 Rogerson의 실질불변감모상각방식 (Real Constant Amortization Schedule)이 가격상한규제 (price-cap regulation)하에서도 사회적으로 최적인가를 규명하는 것을 목적으로 한다. 실질불변감모상각방식이란 피규제기업의 현금의 흐름 (cash flow) - 요율기저에 대한 공정보수와 감가상각액의 합계 - 의 실질가치가 매기에 일정하도록 해 주는 감모상각방식으로서, 이 방식을 사용할 경우 기업은 규제시차의 길이에 관계 없이 최척의 자본-노동비율을 달성하려 한다는 것이다. 실질불변감모상각방식은 통상 사용되는 감모상각방식보다 느린 속도로 자산의 가치를 줄여 나아가는 것으로 알려져 있다. 가격상한규제에 관한 단순한 모형을 구축하여 분석해 본 결과, 수요가 비탄력적일 경우 실질불변감모상각방식은 가격상한규제하에서도 최적자본-노동비율을 유도하며, 가격상한규제는 규제메카니즘에 있어서 투자보수율규제와 유사하다는 결론을 얻을 수 있었다. 그러나, 가격상한규제에 있어서는 소매물가지수의 상승률뿐만 아니라 피규제기업의 효율성제고 효과가 소비자가격의 인하로 나타나는 메카니즘도 중시되고 있다. 즉, RPI-X룰에서 X에 해당하는 부분으로서, 이는 내부효율성의 향상 이외에 기술진보속도도 반영하는데, 이러한 기술진보는 가격상한의 인하요인으로 작용하므로, 피규제기업의 입장에서는 감가상각속도의 적절한 설정이 없는 한 기술진보의 유인이 줄어들 수도 있다. 따라서 기술진보속도와 최적감가상각속도의 관계를 단순한 모형을 확장하여 살펴보았는데, 기술진보속도가 빠를수록 감가상각속도도 빨라져야 한다는 결과가 도출되었다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.23-28
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• 2012

The performance improvement of the secondary storage is very slow compared to the main memory and processor. The data is loaded from secondary storage to memory for the execution of an application. At this time, there is a bottleneck. In this paper, we propose an Executable Compression Method to speed up the initial loading time of application. and we examined the performance. So we implemented the two applications. The one is a compressor for Execution Binary File. and The other is a decoder of Executable Compressed application file on the Embedded System. Using the test binary files, we performed the speed test in the six files. At the result, one result showed that the performance was decreased. but others had a increased performance. the average increasing rate was almost 29% at the initial loading time. The level of compression had different characteristics of the file. And the performance level was dependent on the file compressed size and uncompress time. so the optimized compression algorithm will be needed to apply the execution binary file.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.552-557
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• 2000

In the paper, we proposed a face recognition method that uses GA-BP(Genetic Algorithm-Back propagation Network) that optimizes initial parameters such as bias values or weights. Each pixel in the picture is used for input of the neuralnetwork. The initial weights of neural network is consist of fixed-point real values and converted to bit string on purpose of using the individuals that arte expressed in the Genetic Algorithm. For the fitness value, we defined the value that shows the lowest error of neural network, which is evaluated using newly defined adaptive re-learning operator and built the optimized and most advanced neural network. Then we made experiments on the face recognition. In comparison with learning convergence speed, the proposed algorithm shows faster convergence speed than solo executed back propagation algorithm and provides better performance, about 2.9% in proposed method than solo executed back propagation algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.860-863
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• 2008

플래시 메모리는 우리 생활에 널리 사용되고 있는 휴대용 저장장치 중의 하나이다. 빠른 입출력 속도와 저전력, 무소음, 작은 크기 등의 장점을 가지나 덮어쓰기가 불가능하고 읽기/쓰기의 속도에 비해 소거 연산의 속도가 매우 느리다는 단점이 있다. 이를 보완하기 위해, 호스트와 플래시 메모리 사이에 버퍼 캐시를 두어 사용하고 있으며, 버퍼 캐시에 사용되는 교체 정책에 따라 플래시 메모리 장치의 성능이 크게 영향을 받는다. 본 논문에서는 블록 단위의 LRU 기법의 단점을 개선한 HPLRU 기법을 제안한다. HPLRU 기법은 최근에 자주 참조되었던 페이지인 핫 페이지 들을 모아 리스트를 만들어 관리하고, 이를 통해 페이지 적중률을 향상시키고 다른 페이지들로 인해 핫 페이지들이 소거되는 현상을 개선하였다. 이 알고리즘은 임의 데이터 패턴에 좋은 성능을 보이며 쓰기 발생 횟수를 많이 감소시키는 결과를 보였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.734-740
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• 2012

In this paper, we propose a method for throughput improvement in multirate multihop wireless networks with intra-session network coding. A multirate transmission supports several rates so that it has a tradeoff between transmission rate and transmission range, therefore multirate can cope with the performance degradation. We present the rate selection method with intra-session network coding in multirate nodes. The effective transmission rate is calculated by using intra-session network coding, and then its maximum effective transmission rate is selected. Concurrent transmission set is determined by selected transmission rate and then the linear programming is formulated for throughput calculation in multihop wireless networks. We evaluate the performance by using MATLAB and lp_solve programs in two topologies. It is shown that the throughput of the proposed method is improved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8C
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• pp.505-515
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• 2011

This paper describes a normalization method of Haar-like features used for object detection. Previous method which performs variance normalization on Haar-like features requires a lot of calculations, since it uses an additional integral image for calculating the standard deviation of intensities of pixels in a candidate window and increases possibility of false detection in the area where variance of brightness is small. The proposed normalization method can be performed much faster than the previous method by not using additional integral image and classifiers which are trained with the proposed normalization method show robust performance in various lighting conditions. Experimental result shows that the object detector which uses the proposed method is 26% faster than the one which uses the previous method. Detection rate is also improved by 5% without increasing false alarm rate and 45% for the samples whose brightness varies significantly.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.511-520
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• 2016

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.91-99
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• 2020

For the shadow removal technology that is the base technology of the image detection system, real-time image processing has a problem that the processing speed is reduced due to the calculation complexity and it is also sensitive to illumination or light because shadows are removed only by the difference in brightness. Therefore, in this paper, we improved real-time performance by reducing the calculation complexity through the removal of the weighting part in order to solve the problem of the conventional system. In addition, we designed and evaluated an image detection system based on a shadow removal algorithm that could improve the shadow recognition rate using a vertical histogram. The evaluation results confirmed that the average speed increased by approximately 5.6ms and the detection rate improved by approximately 5.5%p compared to the conventional image detection system.