• Smart Structures and Systems
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• v.15 no.5
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• pp.1215-1232
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• 2015

In the present paper, a method for identifying damage in a multi storeyed shear building structure is presented using minimum number of modal parameters of the structure. A damage at any level of the structure may lead to a major failure if the damage is not attended at appropriate time. Hence an early detection of damage is essential. The proposed identification methodology requires experimentally determined sparse modal data of any particular mode as input to detect the location and extent of damage in the structure. Here, the first natural frequency and corresponding partial mode shape values are used as input to the model and results are compared by changing the sensor placement locations at different floors to conclude the best location of sensors for accurate damage identification. Initially experimental data are simulated numerically by solving eigen value problem of the damaged structure with inclusion of random noise on the vibration characteristics. Reliability of the procedure has been demonstrated through a few examples of multi storeyed shear structure with different damage scenarios and various noise levels. Validation of the methodology has also been done using dynamic data obtained through experiment conducted on a laboratory scale steel structure.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.159-162
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• 2005

Random Early Detection (RED), one of the most well-known Active Queue Management (AQM), has been designed to substitute Tail Drop and is nowadays widely implemented in commercially available routers. RED algorithm provides high throughput and low delay as well as a solution of global synchronization. However RED is sensitive to parameters setting, so the performance of RED, significantly depends on the fixed parameters. To solve this problem, the Adaptive RED (ARED) algorithm is suggested by S. Floyd. But, ARED also uses fixed parameters like target-queue length; it is hard to respond to bursty traffic actively. In this paper, we proposed AQM algorithm based on the variation of current queue length in order to improve adaptability about burst traffic. We measured performance of proposed algorithm through a throughput, marking-drop rate and bias phenomenon. In experimentation, we raised a packet throughput as reduced packet drop rate, and we confirmed to reduce a bias phenomenon about bursty traffic.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.539-544
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• 2011

In this paper, a parameter optimization method of RIO-DC (RED (Random Early Detection) with In and Out-De-Coupled Queues) scheme for Assured Service (AS) in Differentiated Services (DiffServ) is proposed. In order to optimize QoS (Quality of Service) performance of the RIO-DC policy for AS in terms of maximum tolerable latency, link utilization, fairness, etc., we should design router nodes with proper RIO-DC operating parameter values. Therefore, we propose a RIO-DC configuration method and the admission control criterion, considering the allocated bandwidth to each subclass and the corresponding buffer size, to increase throughput for In-profile traffic and link utilization. Simulation results show that RIO-DC with the proposed parameter values guarantees QoS performance comparable with the RIO scheme and it improves fairness between AS flows remarkably.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.121.1-121
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• 2001

Congestion control is a critical issue in TCP networks, Recently, active queue management (AQM) was proposed for congestion control at routers. The random early detection RED algorithm is widely known in the AQM algorithms, We present an adaptive nonlinear RED (NRED) algorithm, which has nonlinear drop probability profile. The proposed algorithm enhanced the performance of the RED algorithm by the self-parameterization based on the traffic load Furthermore, the proposed algorithm can effectively adapt itself between he RED and the drop-tail queue management by adopting proper nonlinearity in the drop probability profile. Through simulation, we show the effectiveness of the proposed algorithm comparing with the drop-tail and the original RED algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10c
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• pp.409-411
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• 2001

기존의 인터넷 라우터는 Drop tail 방식으로 패킷을 관리한다. 따라서 네트워크 트래픽의 지수적인 증가로 인한 혼잡 상황으로 발생하는 패킷 손실을 해결 한 수 없다. 이 문제를 해결하기 위해 IETF (Internet Engineering Task Force)에서는 REU(Random Early Detection) 알고리즘과 같은 능동적인 큐 관리 알고리즘을 제시하였다. 하지만 RED 알고리즘은 네트워터 환경에 따른 매개 변수의 설정의 어려움을 가지고 치어 잘못 된 매개변수 설정으로 인하여 네트워크 성능이 저하되는 문제점을 가지고 있다. 본 논문에서는 기존의 RED를 개선한 ARED를 제안했다. ARED는 RED 알고리즘의 문제점을 개선하여 네트워크 특성에 맞추어 동적으로 매개변수글 조절하는 알고리즘이다. ns를 이용한 실험을 통하여 ARED의 성능을 검증하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10e
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• pp.181-183
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• 2002

현재의 인터넷 라우터는 Drop tail 방식으로 큐 안의 패킷을 관리한다. 따라서 네트워크 트래픽의 지수적인 증가로 인해 발생하는 혼잡 상황을 명시적으로 해결 한 수 없다. 이 문제를 해결하기 위해 IETF (Internet Engineering Task Force)에서는 RED(Random Early Detection)알고리즘과 같은 능동적인 큐 관리 알고리즘(AQM: Active Queue Algorithm)을 제시하였다. 하지만 RED 알고리즘은 네트워크 환경에 따른 매개 변수의 설정의 어려움을 가지고 있어 잘못된 매개변수 설정으로 인하여 네트워크 성능을 저하시키는 문제를 발생시키며 전체 망에 불안정한 혼잡제어를 야기 시킨다. 본 논문에서는 기존의 AQM를 개선한 SOQuM(Stabilized Operation of Queue Management) 알고리즘을 제안하였다. 제안한 알고리즘의 성능을 검증하기 위해 기존의 방법과 시뮬레이션을 이용하여 비교하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.6A
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• pp.1060-1066
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• 2001

레이어2 스위칭과 레이어3 라우팅의 통합 모델로써 MPLS(Multi-Protocol Label Switching) 환경에서 ATM LSR(Label-Switching Routers)은 백본망에서의 고속 전송이 가능하여 현재의 라우터 구조로써 제안되어지고 있다. MPLS가 코어 라이터로써 적용이 될 경우 확장성을 위해 label merging이라는 기술이 필요하다. VC(Virtual Circuit) merging은 ATM LSR에서 많은 IP 라우터를 하나의 라벨로 매핑을 시키며 수천 개의 목적지에 전송할 수 있는 확장성 있는 매핑 기술이다. VC merging은 같은 목적지인 다른 패킷들 간의 셀들의 섞임을 방지하기 위해 재 조합 버퍼가 요구된다. 재 조합 버퍼 사용시 일시적인 체증 현상이 발생하며 Non-VC merging과 비교시 많은 셀 손실과 많은 버퍼를 요구하게 된다. 본 논문에서는 RED(Random Early Detection) 알고리즘을 적용하여 VC merging이 필요한 버퍼의 요구량과 셀 손실을 줄였다.

• Journal of Communications and Networks
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• v.6 no.2
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• pp.133-146
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• 2004

Recently, many active queue management (AQM) algorithms have been proposed to address the performance degradation. of end-to-end congestion control under tail-drop (TD) queue management at Internet routers. However, these AQM algorithms show performance improvement only for limited network environments, and are insensitive to dynamically changing network situations. In this paper, we propose an adaptive queue management algorithm, called PID-controller, that uses proportional-integral-derivative (PID) feedback control to remedy these weak-Dalles of existing AQM proposals. The PID-controller is able to detect and control congestion adaptively and proactively to dynamically changing network environments using incipient as well as current congestion indications. A simulation study over a wide range of IP traffic conditions shows that PID-controller outperforms other AQM algorithms such as Random Early Detection (RED) [3] and Proportional-Integral (PI) controller [9] in terms of queue length dynamics, packet loss rates, and link utilization.