• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.3
• /
• pp.37-45
• /
• 2004

In this paper we propose Clustered Group Multicast by Clustering Algorithm in Wireless Mobile Ad-hoc Network. The proposed scheme applies to Weighted Cluster 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 muting 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 seeped 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.15 no.4
• /
• pp.197-204
• /
• 2020

The ImageNet dataset is a large scale dataset and contains various natural scene images. In this paper, we propose a convolutional neural network (CNN)-based weighted ensemble technique for the ImageNet classification task. First, in order to fuse several models, our technique uses weights for each model, unlike the existing average-based ensemble technique. Then we propose an algorithm that automatically finds the coefficients used in later ensemble process. Our algorithm sequentially selects the model with the best performance of the validation set, and then obtains a weight that improves performance when combined with existing selected models. We applied the proposed algorithm to a total of 13 heterogeneous models, and as a result, 5 models were selected. These selected models were combined with weights, and we achieved 3.297% Top-5 error rate on the ImageNet test dataset.

• Smart Structures and Systems
• /
• v.15 no.1
• /
• pp.169-189
• /
• 2015

Convergence difficulty and available complete measurement information have been considered as two primary challenges for the identification of large-scale engineering structures. In this paper, a time domain substructural identification approach by combining a weighted adaptive iteration (WAI) algorithm and an extended Kalman filter method with a weighted global iteration (EFK-WGI) algorithm was proposed for simultaneous identification of physical parameters of concerned substructures and unknown external excitations applied on it with limited response measurements. In the proposed approach, according to the location of the unknown dynamic loadings and the partially available structural response measurements, part of structural parameters of the concerned substructure and the unknown loadings were first identified with the WAI approach. The remaining physical parameters of the concerned substructure were then determined by EFK-WGI basing on the previously identified loadings and substructural parameters. The efficiency and accuracy of the proposed approach was demonstrated via a 20-story shear building structure and 23 degrees of freedom (DOFs) planar truss model with unknown external excitation and limited observations. Results show that the proposed approach is capable of satisfactorily identifying both the substructural parameters and unknown loading within limited iterations when both the excitation and dynamic response are partially unknown.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.3
• /
• pp.273-283
• /
• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• The KIPS Transactions:PartA
• /
• v.8A no.4
• /
• pp.299-310
• /
• 2001

In this paper, we study the methodology of distributing the requests of clients based on the state of real server in the LVS(Linux virtual server) clustering system. The WLC(weighted least connection) algorithm in the LVS cluster system is studied. The load distributing algorithm which assigns a weight into each real server is devised by considering the load of real servers. The load test is executed to estimate the load of real servers using a load generating tool. Throughout the result of the experiment, we suggest new load distributing algorithm based on the usage of physical memory of each real server. It is shown that the correction potentiality of new algorithm is somewhat better than that of the WLC algorithm.

• Journal of Korean Institute of Industrial Engineers
• /
• v.26 no.2
• /
• pp.183-192
• /
• 2000

This paper considers the problem of scheduling a set of n jobs on m parallel machines to minimize total weighted tardiness. For the problem a genetic algorithm is proposed, in which solutions are encoded using the random key method suggested by Bean and new crossover operators are employed to increase performance of the algorithm. The algorithm is compared with the Modified Due-Date (MDD) algorithm after series of tests to find appropriate values for genetic parameters. Results of computational tests on randomly generated test problems show that the suggested algorithm performs better than the MDD algorithm and gives good solutions in a reasonable amount of computation time.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.12
• /
• pp.985-992
• /
• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.2911-2913
• /
• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• The KIPS Transactions:PartB
• /
• v.10B no.7
• /
• pp.757-768
• /
• 2003

This paper proposes a weighted fuzzy reasoning algorithm for rule-based systems based on weighted fuzzy Pr/T nets, where the certainty factors of the fuzzy production rules, the truth values of the predicates appearing in the rules and the weights representing the importance of the predicates are represented by the fuzzy numbers. The proposed algorithm is more flexible and much closer to human intuition and reasoning than other methods : $\circled1$ calculate the certainty factors using by the simple min and max operations based on the only certainty factors of the fuzzy production rules without the weights of the predicates[10] : $\circled2$ evaluate the belief of the fuzzy production rules using by the belief evaluation functions according to fuzzy concepts in the fuzzy rules without the weights of the predicates[12], because this algorithm uses the weights representing the importance of the predicates in the fuzzy production rules.

• Journal of information and communication convergence engineering
• /
• v.11 no.4
• /
• pp.258-267
• /
• 2013

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.