• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• 한국정보과학회논문지:시스템및이론
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• 제31권9호
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• pp.495-502
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• 2004

최근 인터넷 사용자와 대용량 콘텐츠의 급증으로 서버와 네트워크의 부하가 가중되고 서비스의 질이 떨어지는 문제가 발생하였다. 이러한 문제의 해결로 콘텐츠를 다수의 지역 서버에 복제하여 서비스하고자 하는 기술들이 대두되고 있고, 이런 환경에서는 클라이언트의 요청에 대해 서비스할 적절한 서버를 선택하는 것이 매우 중요한 문제이다. 그러므로 본 연구에서는 클라이언트의 요청 시점에서 인터넷 토폴로지 상 가깝고 부하가 적은 서버가 서비스할 수 있도록 하는 서버 선택 메커니즘을 설계하여 제안한다. 본 연구는 계속적으로 증가하는 대용량, 실시간 멀티미디어 콘텐츠의 빠르고 안정적인 서비스를 가능하게 할 것이며 이로 인해 디지털 콘텐츠 서비스의 새로운 비즈니스 모델 창출에 크게 기여할 수 있을 것이다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.208.2-212
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• 1997

In a model based friction comensation for a frictional system,the performance of the system is inflenceed by the selection of the friction model. Especially, when a real plant have dynamic friction characteritics, the compensation of friction with a static friction model may deteriorate the perfomance. For the system we constlucted an adaptiv parameter estimation and friction compensation with a newly introduced dynamic friction model proposed by Canudas et.[1]. The model depicts varios frictional phenomena,such as Stibeck effect,frictional memory, Stick-slip motion. Parmeter identification algorithm are followed conventional RLSM adaptive rule. The stability for the closed system was proved by the Lyapunov stability. The result say that if a real system have dynamic friction property,the friction compensation with the dynamic friction model will improve the perfomance moreover static friction model based compensation may lead to the system unstable.

• ETRI Journal
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• 제42권6호
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• pp.886-898
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• 2020

Unusual data patterns or outliers can be generated because of human errors, incorrect measurements, or malicious activities. Detecting outliers is a difficult task that requires complex ensembles. An ideal outlier detection ensemble should consider the strengths of individual base detectors while carefully combining their outputs to create a strong overall ensemble and achieve unbiased accuracy with minimal variance. Selecting and combining the outputs of dissimilar base learners is a challenging task. This paper proposes a model that utilizes heterogeneous base learners. It adaptively boosts the outcomes of preceding learners in the first phase by assigning weights and identifying high-performing learners based on their local domains, and then carefully fuses their outcomes in the second phase to improve overall accuracy. Experimental results from 10 benchmark datasets are used to train and test the proposed model. To investigate its accuracy in terms of separating outliers from inliers, the proposed model is tested and evaluated using accuracy metrics. The analyzed data are presented as crosstabs and percentages, followed by a descriptive method for synthesis and interpretation.

• 한국통신학회논문지
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• 제31권11B호
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• pp.945-952
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• 2006

Hierarchical Mobile IPv6(HMIPv6) 네트워크에서 도메인간의 핸드오버(Inter-domain handover)가 발생할 때 Mobile Nodes(MNs)는 과도한 신호전달 트래픽과 긴 핸드오버 지연시간에 의해 영향을 받게 되고, 이로 인하여 유지하고 있는 연결들이 깨어질 수도 있다. 또, 전체적인 시스템 성능은 Mobile Anchor Point(MAP)의 선택 방법과 부하 상태에 따라 큰 영향을 받는다. 따라서 본 논문에서는 부하를 MAP들에 분산하면서 도메인간의 핸드오버를 감소시킬 수 있는 동적인 MAP 선택 알고리즘을 제안한다. 제안한 알고리즘의 성능 분석을 위하여 신호 전달 비용에 대한 분석적 모델을 수립하고 시뮬레이션을 수행하였다. 수치결과와 시뮬레이션 결과를 통하여 제안한 알고리즘이 IETF HMIPv6에 기반을 둔 기존의 방법에 비해 부하를 더 균형 있게 분산시키고, 도메인 간 핸드오버의 횟수와 평균 신호 전달 비용을 감소시킨다는 것을 확인할 수 있었다.

• Computers and Concrete
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• 제31권2호
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• pp.85-95
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• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.27-50
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• 2009

This paper describes an adaptive management approach for predicting, monitoring, and controlling ground movements associated with excavations in urban areas. Successful use of monitoring data to update performance predictions of supported excavations depends equally on reasonable numerical simulations of performance, the type of monitoring data used as observations, and the optimization techniques used to minimize the difference between predictions and observed performance. This paper summarizes each of these factors and emphasizes their inter-dependence. Numerical considerations are described, including the initial stress and boundary conditions, the importance of reasonable representation of the construction process, and factors affecting the selection of the constitutive model. Monitoring data that can be used in conjunction with current numerical capabilities are discussed, including laser scanning and webcams for developing an accurate record of construction activities, and automated and remote instrumentations to measure movements. Self-updating numerical models that have been successfully used to compute anticipated ground movements, update predictions of field observations and to learn from field observations are summarized. Applications of these techniques from case studies are presented to illustrate the capabilities of this approach.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.257-262
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• 2009

Image processing has been utilized for assessment of infrastructure surface coating conditions for years. However, there is no robust method to overcome the non-uniform illumination problem to date. Therefore, this paper aims to deal with non-uniform illumination problems for bridge coating assessment and to achieve automated rust intensity recognition. This paper starts with selection of the best color configuration for non-uniformly illuminated rust image segmentation. The adaptive-network-based fuzzy inference system (ANFIS) is adopted as the framework to develop the new model, the box-and-ellipse-based neuro-fuzzy approach (BENFA). Finally, the performance of BENFA is compared to the Fuzzy C-Means (FCM) method, which is often used in image recognition, to show the advantage and robustness of BENFA.

• E2M - 전기 전자와 첨단 소재
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• 제11권11호
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• 대한원격탐사학회지
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• 제33권5_1호
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• pp.455-467
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• 2017

초분광 영상을 이용한 표적 탐지를 수행할 때에는 인접한 분광 밴드의 중복성의 문제 및 고차원 데이터로 인해 발생하는 방대한 계산량의 문제점을 해결하기 위한 특징 추출 과정이 필수적이다. 본 연구는 기계 학습 분야의 특징 선택 기법을 초분광 밴드 선택에 적용하기 위해 $L_{2,1}$-norm regression 모델을 이용한 새로운 밴드 선택 기법을 제안하였으며, 제안한 밴드 선택 기법의 성능 분석을 위해 표적이 존재하는 초분광영상을 직접 촬영하고 이를 바탕으로 표적 탐지를 수행한 결과를 분석하였다. 350 nm~2500 nm 파장 대역에서 밴드 수를 164개에서 약 30~40개로 감소시켰을 때 Adaptive Cosine Estimator(ACE) 탐지 성능이 유지되거나 향상되는 결과를 보였다. 실험 결과를 통해 제안한 밴드 선택 기법이 초분광 영상에서 탐지에 효율적인 밴드를 추출해 내며, 이를 통해 성능의 감소 없이 데이터의 차원 감소를 수행할 수 있어 향후 실시간 표적 탐지 시스템의 처리 속도 향상에 도움을 줄 수 있을 것으로 보인다.