• Smart Structures and Systems
• /
• v.10 no.1
• /
• pp.47-65
• /
• 2012

The presence of crack-like defects in mechanical and structural elements produces failures during their service life that in some cases can be catastrophic. So, the early detection of the fatigue cracks is particularly important because they grow rapidly, with a propagation velocity that increases exponentially, and may lead to long out-of-service periods, heavy damages of machines and severe economic consequences. In this work, a non-destructive method for the detection and identification of elliptical cracks in shafts based on stress wave propagation is proposed. The propagation of a stress wave in a cracked shaft has been numerically analyzed and numerical results have been used to detect and identify the crack through the genetic algorithm optimization method. The results obtained in this work allow the development of an on-line method for damage detection and identification for cracked shaft-like components using an easy and portable dynamic testing device.

• The KIPS Transactions:PartC
• /
• v.12C no.6 s.102
• /
• pp.847-854
• /
• 2005

Since the propagation speed of the Internet worms is quite fast, worm detection in early propagation stage is very important for reducing the damage. Virus throttling technique, one of many early worm detection techniques, detects the Internet worm propagation by limiting the connection requests within a certain ratio.[6, 7] The typical throttling technique increases the possibility of false detection by treating destination IP addresses independently in their delay queue managements. In addition, it uses a simple decision strategy that determines a worn intrusion if the delay queue is overflown. This paper proposes a two dimensional delay queue management technique in which the sessions with the same destination IP are linked and thus a IP is not stored more than once. The virus throttling technique with the proposed delay queue management can reduce the possibility of false worm detection, compared with the typical throttling since the proposed technique never counts the number of a IP more than once when it chicks the length of delay queue. Moreover, this paper proposes a worm detection algorithm based on weighted average queue length for reducing worm detection time and the number of worm packets, without increasing the length of delay queue. Through deep experiments, it is verified that the proposed technique taking account of the length of past delay queue as well as current delay queue forecasts the worn propagation earlier than the typical iuぉ throttling techniques do.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.8C
• /
• pp.522-528
• /
• 2011

The iterative detection and decoding (IDD) has been shown to dramatically improve the bit error rate (BER) performance of the multiple-input multiple-output (MIMO) communication systems. However, these techniques require a high computational complexity since it is required to compute the soft decisions for each bit. In this paper, we show IDD comprised of sphere decoder with low-density parity check (LDPC) codes and present the tree search strategy, called a layer symbol search (LSS), to obtain soft decisions with a low computational complexity. In addition, an adaptive early termination is proposed to reduce the computational complexity during an iteration between an inner sphere decoder and an outer LDPC decoder. It is shown that the proposed approach can achieve the performance similar to an existing algorithm with 70% lower computational complexity compared to the conventional algorithms.

• The Journal of Bigdata
• /
• v.1 no.1
• /
• pp.19-22
• /
• 2016

Many Studies have been actively conducted on the early diagnosis of the crop pest utilizing IT technology. The purpose of the paper is to discuss on the image processing method capable of detecting the crop leaf pest prematurely by analyzing the image of the leaf received from the camera sensor. This paper proposes an algorithm of diagnosing leaf infection by utilizing an improved K means clustering method. Leaf infection grouping test showed that the proposed algorithm illustrated a better performance in the qualitative evaluation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.6A
• /
• pp.910-917
• /
• 2000

Because the random early detection(RED) algorithm deals all flows with the same best-effort traffic characteristic, it can not correctly control the output link bandwidth for the flows with different traffic characteristics. To remedy this problem, several per-flow algorithms have been proposed. In this paper, we propose a new per-flow type Fair Droptail algorithm which can fairly allocate bandwidth among flows over a shared output link. By evenly allocating buffers per flow, the Fair Droptail can restrict a flow not to use more bandwidth than others. In addition, it can be simply implemented even if it employs the per-flow state mechanism, because the Fair Droptail only keeps each information of flow in active state.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.04a
• /
• pp.280-282
• /
• 2002

기존의 네트워크에서는 혼잡상황이 감지된 이후에 네트워크 성능이 급격하게 저하된다. 이러한 문제를 해결하고자 RED(Random Early Detection)기법이 소개되어 게이트웨이에서 혼잡상황에 대하여 능동적으로 대처할 수 있는 알고리즘이 제시되었다. 하지만, RED는 매개변수 설정이라는 문제가 남아있다. 그리하여, 잘못된 변수값 설정으로 인한 네트워크 성능 저하가 현저하게 발생한다. 본 논문에서는 기존의 RED를 개선한 Effective RED를 제안한다. Effective RED는 RED 알고리즘의 문제점을 개선하여 네트워크의 상황에 맞추어 동적으로 매개 변수 값을 조정하는 알고리즘이다. 그리고, ns를 이용하여 Effective RED의 성능을 검증하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10c
• /
• pp.427-429
• /
• 2001

최근 라우터에서는 정체를 회피하고 전송률을 향상시키기 위한 능동적 큐 관리와 패킷 스케줄링에 대한 많은 논의가 이루어지고 있다. 본 논문은 라우터에서의 전송률 향상을 위한 Random Early Detection (RED) 알고리즘과 최근가지 변형된 RED알고리즘들의 특징을 살펴보고, RED라우터에 적용하여 실제로 종단 호스트(End-to-end)에서 전송 받는 패킷의 양을 창상하기 위한 선별적 처리 알고리즘을 제안한다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10c
• /
• pp.490-492
• /
• 2000

기존의 인터넷 라우터는 Drop tail 방식으로 패킷을 관리한다. 따라서 네트워크 트래픽의 지수적인 증가로 인한 혼잡 상황으로 발생하는 패킷 손실을 해결할 수 없다. 이 문제를 해결하기 위해 IETF(Internet Engineering Task Force)에서는 RED(Random Early Detection)와 같은 능동적인 큐 관리 알고리즘을 제시하였다. 하지만 RED는 동적으로 변화하는 인터넷 트래픽에 대하여 단지 큐 크기의 변화 정보를 얻어 혼잡 상황을 제어하기 때문에 성능에 있어는 매우 비효율적이다. 본 논문에서는 기존의 RED를 개선한 MRED를 제안했다. MRED는 RED에 비하여 휴리스틱한 방법을 이용하여 폐기 확률 값을 계산하고, 이를 실험을 통하여 MRED의 성능을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.5006-5022
• /
• 2017

Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.1
• /
• pp.33-40
• /
• 2012

This paper proposes a method to implement biological early warning systems(BEWS). This system generates periodically data event using a monitoring daemon and it extracts the feature parameters from this data sets. The feature parameters are derived with 6 variables, x/y coordinates, distance, absolute distance, angle, and fractal dimension. Specially by using the fractal dimension theory, the proposed algorithm define the input features represent the organism characteristics in non-toxic or toxic environment. And to find a moderate algorithm for learning the extracted feature data, the system uses an extended learning algorithm(UChoo) popularly used in machine learning. And this algorithm includes a learning method with the extended data expression to overcome the BEWS environment which the feature sets added periodically by a monitoring daemon. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression. Experimental results show that the proposed BEWS is available for environmental toxicity detection.