• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.657-659
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• 1998

In order to optimize fuzzy modeling of nonlinear system, we proposed a optimal fuzzy model according to the characteristic of I/O relationship, HCM method, the genetic algorithm, and the objective function with weighting factor. A conventional fuzzy model has difficulty in definition of membership function. In order to solve its problem, the premise structure of the proposed fuzzy model is selected by both the partition of input space and the analysis of input-output relationship using the clustering algorithm. The premise parameters of the fuzzy model are optimized respectively by the genetic algorithm and the consequence parameters of the fuzzy model are identified by the standard least square method. Also, the objective function with weighting factor is proposed to achieve a balance between the performance results for the training and testing data.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1008-1016
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• 2015

This paper proposes a method for detecting the front side of vehicles. The method can find the car side with a license plate even with complicated and cluttered backgrounds. A convolutional neural network (CNN) is used to solve the detection problem as a unified framework combining feature detection, classification, searching, and localization estimation and improve the reliability of the system with simplicity of usage. The proposed CNN structure avoids sliding window search to find the locations of vehicles and reduces the computing time to achieve real-time processing. Multiple responses of the network for vehicle position are further processed by a weighted clustering and probabilistic threshold decision method. Experiments using real images in parking lots show the reliability of the method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.450-453
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• 1999

In this paper, the cooperative retrieval system to interface between users and DB, image data and knowledge-based database(KDB), being formed in a linguistic knowledge expression, of system is presented. Conventional database retrieval systems provide the data only in case that the data exactly corresponding with users' requirements exist in these systems, but don't in other cases. In order to resolve this problem, if the data users require are not in existence, this system shows the data and image information which are approximate with knowledge-based database materialized by fuzzy clustering and allocation of linguistic label.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.761-766
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• 2005

This paper proposes a video model for the implementation of the cultural video database system. We have utilized an efficient scene change detection method that segments cultural video into semantic units for efficient indexing and retrieval of video. Since video has a large volume and needs to be played for a longer time, it implies difficulty of viewing the entire video. To solve this Problem. the cultural video abstraction was made to save the time and widen the choices of video the video abstract is the summarization of scenes, which includes important events produced by setting up the abstraction rule.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.59-67
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• 2013

Compared to the conventional Hardware-oriented base stations, Software Defined Radio (SDR)-based base station provides various advantages especially in flexibility and expandability. It enables the multimode capability required in 4th-generation (4G) environment which aims at a convergence network of various kinds of communication standards. However, since a single base station processes all data required in various multiple waveforms, the SDR base station faces a problem of data processing speed. In this paper, we propose a new concept of SDR base station system which adopts a parallel processing technology of clustering environment. We implemented a WiMAX system with SDR concept which adopts the Message Passing Interface (MPI) technology which enables the speed-up operations. In order to maximize the efficiency of parallel processing in signal processing, we analyze how the algorithm at each of modules is related to data to be processed. Through the implemented system, we show a drastic improvement in operation time due to parallel processing using the proposed MPI technology. In addition, we demonstrate a feasibility of SDR system for 4G or even beyond-4G as well.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.131-140
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• 2010

Biclustering is a method of finding meaningful subsets of objects and attributes simultaneously, which may not be detected by traditional clustering methods. It is popularly used for the analysis of microarray data representing the expression levels of genes by conditions. Usually, biclustering algorithms do not consider a sequential relation between attributes. For time series data, however, bicluster solutions should keep the time sequence. This paper proposes a new biclustering algorithm for time series data by modifying the plaid model. The proposed algorithm introduces a parameter controlling an interval between two selected time points. Also, the pruning step preventing an over-fitting problem is modified so as to eliminate only starting or ending points. Results from artificial data sets show that the proposed method is more suitable for the extraction of biclusters from time series data sets. Moreover, by using the proposed method, we find some interesting observations from real-world time-course microarray data sets and apartment price data sets in metropolitan areas.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.56-65
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• 2020

Forecasting of coming fire accidents is quite a challenging problem cause normally fire accidents occur for a variety of reasons and seem randomness. However, if fire accidents that cause critical losses can be forecasted, it can expect to minimize losses through preemptive action. Classifications using machine learning were determined as appropriate classification criteria for the forecasting cause it classified as a constant damage scale and proportion. In addition, the analysis of the periodicity of a critical fire accident showed a certain pattern, but showed a high deviation. So it seems possible to forecast critical fire accidents using advanced prediction techniques rather than simple prediction techniques.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.614-620
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• 2019

In this paper, we propose a method to effectively determine the representative style embedding of each emotion class to improve the global style token-based end-to-end speech synthesis system. The emotion expressiveness of conventional approach was limited because it utilized only one style representative per each emotion. We overcome the problem by extracting multiple number of representatives per each emotion using a k-means clustering algorithm. Through the results of listening tests, it is proved that the proposed method clearly express each emotion while distinguishing one emotion from others.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.164-170
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• 2021

In VLSI design, partitioning is a task of clustering objects into groups so that a given objective circuit is optimized. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for partitioning include the Kernighan-Lin algorithm, Fiduccia-Mattheyses heuristic and simulated annealing. In this paper, we propose a adapted genetic algorithm searching solution space for the circuit partitioning problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of implementation. As a result, it was found that an adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.238-243
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• 2019

Recently, the importance of big data continues to be emphasized, and it is applied in various fields based on data mining techniques, which has a great influence on the health care industry. There are many healthcare industries, but only home health care is considered here. However, applying this to real problems does not always give perfect results, which is a problem. Therefore, data mining techniques are used to solve these problems, and the algorithms that affect performance are evaluated. This paper focuses on the effects of healthcare services on patient satisfaction and satisfaction. In order to use the CVParameterSelectin algorithm and the SMOreg algorithm of the classify method of data mining, it was evaluated based on the experiment and the verification of the results. In this paper, we analyzed the services of home health care institutions and the patient satisfaction analysis based on the name, address, service provided by the institution, mood of the patients, etc. In particular, we evaluated the results based on the results of cross validation using these two algorithms. However, the existence of variables that affect the outcome does not give a perfect result. We used the cluster analysis method of weka system to conduct the research of this paper.