• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.243-260
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• 2018

Detection of damages in fibre reinforced plastic (FRP) composite structures is important from the safety and serviceability point of view. Usually, damage is realized as a local reduction of stiffness and if dynamic responses of the structure are sensitive enough to such changes in stiffness, then a well posed inverse problem can provide an efficient solution to the damage detection problem. Usually, such inverse problems are solved within the framework of pattern recognition. Support Vector Machine (SVM) Algorithm is one such methodology, which minimizes the weighted differences between the experimentally observed dynamic responses and those computed using the finite element model- by optimizing appropriately chosen parameters, such as stiffness. A damage detection strategy is hereby proposed using SVM which perform stepwise by first locating and then determining the severity of the damage. The SVM algorithm uses simulations of only a limited number of damage scenarios and trains the algorithm in such a way so as to detect damages at unknown locations by recognizing the pattern of changes in dynamic responses. A rectangular fiber reinforced plastic composite plate has been investigated both numerically and experimentally to observe the efficiency of the SVM algorithm for damage detection. Experimentally determined modal responses, such as natural frequencies and mode shapes are used as observable parameters. The results are encouraging since a high percentage of damage cases have been successfully determined using the proposed algorithm.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.79-93
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• 2005

Despite the fact that the sourcing process in B2B e-Marketplaces is one of the most important tasks, the evaluation and selection process of suppliers have been ad-hoc based and mainly dependent on the experience of sourcing managers' subjective knowledge. To remedy the problem, we developed a decision support System (called Wise - I) that helps sourcing managers evaluate suppliers in a more systematic way. The system reflects company's strategy and know-how by adopting company enforced weighted scores for different factors and employing a more scientific method of considering factors other than price and on-time delivery rate, utilizing the AHP method. This paper reports the effectiveness of the system as well as the detailed description of the system. To investigate the effectiveness of the system, we collected information through interview and questionnaire survey. The information was also augmented through the firm key index system, which monitors average delivery lead time and on-time delivery rate. The result indicates that the system leads to the efficiency of purchasing section and the transparency of buying process, therefore reduces delivery time and cost.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.829-835
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• 2012

When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The $F_{\infty}$-norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the $F_{\infty}$-norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive $F_{\infty}$-norm ($AF_{\infty}$-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise $L_{\infty}$-norm penalty. The $AF_{\infty}$-norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the $F_{\infty}$-norm SVM. The simulation studies show that the proposed $AF_{\infty}$-norm SVM improves upon the $F_{\infty}$-norm SVM in terms of classification accuracy and factor selection performance.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.471-476
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• 2009

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.87-100
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• 2011

In this paper, we address a video summarization task as generating both visually salient and semantically important video segments. In order to find salient data points, one can use the OC-SVM (One-class Support Vector Machine), which is well known for novelty detection problems. It is, however, hard to incorporate into the OC-SVM process the importance measure of data points, which is crucial for video summarization. In order to integrate the importance of each point in the OC-SVM process, we propose a fuzzy version of OC-SVM. The Importance-based Fuzzy OC-SVM weights data points according to the importance measure of the video segments and then estimates the support of a distribution of the weighted feature vectors. The estimated support vectors form the descriptive segments that best delineate the underlying video content in terms of the importance and salience of video segments. We demonstrate the performance of our algorithm on several synthesized data sets and different types of videos in order to show the efficacy of the proposed algorithm. Experimental results showed that our approach outperformed the well known traditional method.

• Journal of Family Resource Management and Policy Review
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• v.19 no.3
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• pp.101-119
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• 2015

The purpose of this study is to evaluate the current performance of the public services for families in crisis by analyzing the family empowerment service in Healthy Families Support centers. We analyzed performance data of the family empowerment service provided by 25 Healthy Families Support Centers from 2011 to 2013. The results are as follows; First, the number of families in crisis which received public services from the family empowerment service by the Healthy Family Support Centers in 2013 were less than the number in 2011, but increased from 2012. Second, according to the types of crisis, school violence was the most service needed family crisis in 2011, and it was suicide in 2012 and 2013. Third, in the specific services in emergency support for families and family function recovery program, Psychological and emotional support services were the most offered services during 3 years. Accordingly, efficiency of the programs and services in terms of budget is higher than that of any other services. Fourth, analysing the evaluation results of amily empowerment services in 2014, we found that its network is still heavily weighted in certain side by the lack of the utilization and the foundation of the network.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.6
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• pp.497-502
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• 2009

Exercise-mediated physical treatment has attracted much recent interest. In particular, swimming is a representative exercise treatment method recommended for patients experiencing muscular and cardiovascular diseases. The present study sought to design a swimming-based exercise treatment management system. A survey questionnaire was completed by participants to assess the prevalence of muscular and cardiovascular diseases among adult males and females participating in swimming programs at sport centers in metropolitan regions of country. Using the Fuzzy Analytic Hierarchy Process (AHP) technique, weighted values of indices were determined, to maximize participant clarity. A patient management system model was devised using information technology. The favorable results are evidence of the validity of this approach. Additionally, the swimming-based exercise management system can be supplemented together with analyses of weighted values considering connectivity between established indices.

• Journal of Digital Contents Society
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• v.17 no.2
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• pp.81-88
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• 2016

The general frequent pattern mining considers frequency and support of items. To extract useful information, it is necessary to consider frequency and weight of items that reflects the changing of user interest as time passes. The suitable services considering time or location is requested by user so that the weighted mining method is necessary. We propose a method of weighted frequent pattern mining based on service ontology. The weight considering time and location is given to service items and it is applied to association rule mining method. The extracted rule is combined with stored service rule and it is based on timely service to offer for user.

• ETRI Journal
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• v.42 no.6
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• pp.976-986
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• 2020

Internet of Things (IoT) is considered the future network to support wireless communications. To realize an IoT network, sufficient spectrum should be allocated for the rapidly increasing IoT devices. Through cognitive radio, unlicensed IoT devices exploit cooperative spectrum sensing (CSS) to opportunistically access a licensed spectrum without causing harmful interference to licensed primary users (PUs), thereby effectively improving the spectrum utilization. However, an open access cognitive IoT allows abnormal IoT devices to undermine the CSS process. Herein, we first establish a hard-combining attack model according to the malicious behavior of falsifying sensing data. Subsequently, we propose a weighted sequential hypothesis test (WSHT) to increase the PU detection accuracy and decrease the sampling number, which comprises the data transmission status-trust evaluation mechanism, sensing data availability, and sequential hypothesis test. Finally, simulation results show that when various attacks are encountered, the requirements of the WSHT are less than those of the conventional WSHT for a better detection performance.

• The KIPS Transactions:PartC
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• v.10C no.2
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• pp.163-170
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• 2003

In this paper we propose a robust clustered croup Multicast in Ad-hoc network. The proposed scheme applies to weighted clustered Algorithm. Ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration or reliable support services such as wired network and base station. In ad hoc network routing protocol because of limited bandwidth and high mobility robust, simple and energy consume minimal. WCGM method uses a base structure founded on combination weighted value and applies combination weight value to cluster header keeping data transmission by scoped flooding, which is the advantage of the exiting FGMP method. Because this method has safe and reliable data transmission, it shows the effect to decrease both overhead to preserve transmission structure and overhead for data transmission.