• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.131-137
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• 2006

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11A
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• pp.1120-1129
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• 2006

It is very important to maximize the battery durability of mobile host in wireless ad-hoc network environments, because it extends the durability and Performance of the system. Since mobile hosts play a routing role, the network structure and the location of mobile hosts create a difference of energy consumption of mobile hosts. In this paper, each mobile host maintains energy tree and evaluates the amount of the energy of the neighboring mobile hosts by using message tree packet by periods. When mobile host sets up a routing path to send a packet, it selects the most proper path in order to consume energy effectively by using energy tree and breadth first search. In this paper, we suggest that, in wireless ad-hoc network environments, if each mobile host consumes balanced energy, mobile hosts of which energy capacity is limited can work as long as it can. Therefore, the durability and performance of the system can be extended.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.78-87
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• 2015

Content-Centric Networking(CCN) is a new networking paradigm to search for the routing information needed to find a data from the content name, unlike conventional IP networks. In CCN, the mobility management, one of the CCN challenges, is consists of consumer mobility and content provider mobility. Among both, in the case of the content provider mobility, it requires too much overhead and time to update routing information on the corresponding routers. In this paper, we propose Popularity-based Pushing CCN(PoPCoN) which considers the content popularity to support effective mobility of content provider in CCN. Our proposed algorithm shortens content download time for the consumer and reduces the network overhead during mobility as compared to the existing approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3C
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• pp.278-286
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• 2010

Motion Compensated Frame Interpolation(MCFI) has been used to reduce motion jerkiness for dynamic scenes and motion blurriness for LCD-panel display as post processing for large screen and full HD(high definition) display. Conventionally, block matching algorithms (BMA) are widely used to do motion estimation for simplicity of implementation. However, there are still several drawbacks. So in this paper, we propose a novel shape-based ME algorithm to increase accuracy and reduce ME computational cost. To increase ME accuracy, we do motion estimation based on shape of moving objects. And only moving areas are included for motion estimation to reduce computational cost. The results show that the computational cost is 25 % lower than full search BMA, while the performance is similar or is better, especially in the fast moving region.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.11 s.114
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• pp.1065-1072
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• 2006

An OFDM(Orthogonal Frequency Division Multiplexing) system has the problem of the PAPR(Peak-to-Average Power Ratio) due to the overlapping phenomena of many sub-carriers. The previously proposed GA-SPW(Genetic Sub-block Phase Weighting) method not only improved the reduction of PAPR as the number of sub-blocks increases in an OFDM symbol but also decreased the number of calculations involved in the iterative phase searching yields to depend on the number of population and generation by using genetic algorithm not on the number of sub-blocks and phase elements. In this paper, we propose the modified GA-SPW method in order to improve the performance and to decrease the complexity. It is shown that the proposed modified GA-SPW method achieves the significant performance and the reduction of search complexity comparing to the ordinary technique, iterative flipping and previously proposed GA-SPW by the experimental results and analysis.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.299-307
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• 2012

Data mining is a method of searching for an interesting relationship among items in a given database. The decision tree is a typical algorithm of data mining. The decision tree is the method that classifies or predicts a group as some subgroups. In general, when researchers create a decision tree model, the generated model can be complicated by the standard of model creation and the number of input variables. In particular, if the decision trees have a large number of input variables in a model, the generated models can be complex and difficult to analyze model. When creating the decision tree model, if there are marginally conditional variables (intervening variables, external variables) in the input variables, it is not directly relevant. In this study, we suggest the method of creating a decision tree using marginally conditional variables and apply to actual data to search for efficiency.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.1-13
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• 2009

Scene classification and concept-based procedures have been the great interest for image categorization applications for large database. Knowing the category to which scene belongs, we can filter out uninterested images when we try to search a specific scene category such as beach, mountain, forest and field from database. In this paper, we propose an adaptive segmentation method for real-world natural scene classification based on a semantic modeling. Semantic modeling stands for the classification of sub-regions into semantic concepts such as grass, water and sky. Our adaptive segmentation method utilizes the edge detection to split an image into sub-regions. Frequency of occurrences of these semantic concepts represents the information of the image and classifies it to the scene categories. K-Nearest Neighbor (k-NN) algorithm is also applied as a classifier. The empirical results demonstrate that the proposed adaptive segmentation method outperforms the Vogel and Schiele's method in terms of accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1149-1152
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• 2005

Detecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a generative neural network model: the Constrained Generative Model (CGM). To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows to apply this detector to a real word application: the indexation of face images on the Web.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.399-404
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• 2006

The container crane is one of the most important equipment in container terminal. If its working time in cycle could be reduced then container terminal efficiency and service level can be increased. So there are many efforts to reduce working time of container crane. It means how to design the controller with good performance which has small overshoot and swing motion of container crane. We, in this paper, present a state feedback controller not based on LQ theory but RCGA which means real-coded genetic algorithms. RCGA can search state feedback gains in given objective function. several cases of simulations are carried out in order to prove the control effectiveness of the proposed methods.