• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1441-1446
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• 2022

Modern people can access various information about food recipes very easily on the Internet or social media. As the supply of food recipes increases, it is difficult to find a suitable recipe for each user in the overflowing information. As such, the need to provide information by reflecting users' requirements has increased, and research related to food recipes and cooking recommendations is becoming active. In addition, the Internet, video, and application markets using this are also rapidly activating. In this study, in order to classify recipes from the user's perspective of food recipe users, the user's review data was applied with the k-mean clustering technique, which is unsupervised learning, and a "food recipe classification model" was derived. As a result, it was classified into a total of 25 clusters including information needed by many users, such as specific purposes and cooking stages.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.15-20
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• 2016

This paper describes the construction of knowledge data retrieval management system based on medical image CT. The developed system is aimed to improve the efficiency of the hospital by reading the medical images using the intelligent retrieval technology and diagnosing the patient 's disease name. In this study, the medical image DICOM file of PACS is read, the image is processed, and feature values are extracted and stored in the database. We have implemented a system that retrieves similarity by comparing new CT images required for medical treatment with the feature values of other CTs stored in the database. After converting 100 CT dicom provided for academic research into JPEG files, Code Book Library was constructed using SIFT, CS-LBP and K-Mean Clustering algorithms. Through the database optimization, the similarity of the new CT image to the existing data is searched and the result is confirmed, so that it can be utilized for the diagnosis and diagnosis of the patient.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.586-591
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• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.37-38
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• 2011

본 논문은 상대적으로 대비도 차이가 크게 나타나는 역광 이미지에 대해서 Retinex 알고리즘을 적용하여 보정 했을 경우 발생하는 밝은 영역에서의 컬러 성분의 손실을 개선하기 위한 새로운 기법을 제안한다. 역광 이미지의 경우 밝은 영역과 어두운 영역에 대한 밝기 차이가 매우 크게 발생하기 때문에 Retinex 알고리즘을 이용하여 영상의 대비도를 향상시킬 경우 밝은 영역에서의 컬러 성분이 손실되는 현상이 발생한다. 이러한 손실을 보완하기 위해서 원본 영상의 밝은 영역에 해당하는 컬러 성분을 Retinex 알고리즘으로 보정된 영상에 추가해준다. Fuzzy c-means 군집화 알고리즘을 이용하여 원본 영상에서의 밝은 영역과 어두운 영역에 대하여 모든 화소의 소속 정도를 나타내는 퍼지 소속 함수를 구한다. 밝은 영역에 대해서의 컬러 성분은 원본 영상 값에 밝은 영역 퍼지 소속 함수를 적용하고, 어두운 영역에 대해서의 컬러 성분은 Retinex 복원 영상 값에 어두운 영역 퍼지 소속 함수를 이용한다. 제안하는 알고리즘의 성능 평가를 위해 역광 현상이 강하게 나타나는 자연영상들을 대상으로 적용하여 기존의 Retinex 알고리즘(MSRCR) 보다 우수한 성능을 가지고 있음을 보였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• Journal of IKEEE
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• v.15 no.3
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• pp.191-197
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• 2011

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.57-68
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• 2001

It is matter of common knowledge to give impetus to the water resources development to cope with increasing demand and supply for the water utilization project including agricultural living and industrial water owing to the economic and civilization development in recent years. Regional design rainfall is necessary or the design of the dam reservoir levee and drainage facilities for the development of various kinds of essential waters including agricultural water. For the estimation of the regional design rainfall classification of the climatologically an geographically homogeneous regions should be preceded preferentially This study was mainly conducted to derive the optimal regionalization of the precipitation data which can be classified by the climatologically and geographically homogeneous regions all over the regions except Cheju and Wulreung islands in Korea. A total of 65 rain gauges were used to regional analysis of precipitation. Annual maximum series for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr were used for various statistical analysis. Both K-means clustering and mean annual precipitation methods are used to identify homogeneous regions all over the regions. Nine and five homogeneous regions for the precipitation were classified by the K-means clustering and mean annual methods, respectively. Finally, Five homogeneous regions were established by the trial and error method with homogeneity test using statistics of $\chi$$^2$ distribution.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1271-1282
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• 2022

The optimal design of Water Distribution System (WDS) is used in various ways according to the purpose set by the user. The optimal design of WDS has various purposes, such as minimizing costs and minimizing energy generated when manufacturing pipes. In this study, based on the Modified Hybrid Vision Correction Algorithm (MHVCA), a cost-optimal design was conducted for various WDSs. We also propose a new evaluation index, Best Rate (BR). BR is an evaluation index developed based on the K-mean Clustering Algorithm. Through BR, a comparison was made on the possibility of searching for the optimal design of each algorithm used in the optimal design of WDS. The results of MHVCA for WDS were compared with Vision Correction Algorithm (VCA) and Hybrid Vision Correction Algorithm (HVCA). MHVCA showed a lower cost design than VCA and HVCA. In addition, MHVCA showed better probability of lower cost designs than VCA and HVCA. MHVCA will be able to show good results when applied to the optimal design of WDS for various purposes as well as the optimal design of WDS for cost minimization applied in this study.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.35-42
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• 1999

In this paper, a modified clustering algorithm is proposed to improve the discrimination of discrete HMM(Hidden Markov Model), so that it has increased recognition rate of 2.16% in comparison with the original HMM using the K-means or LBG algorithm. And, for preventing the decrease of recognition rate because of insufficient training data at the training scheme of HMM, a modified probabilistic smoothing method is proposed, which has increased recognition rate of 3.07% for the speaker-independent case. In the experiment applied the two proposed algorithms, the average rate of recognition has increased 4.66% for the speaker-independent case in comparison with that of original VQ/HMM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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• pp.2456-2469
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• 1996

A digital satellite communication channel has a nonlinearity with memory due to saturation characeristis of the high poer amplifier in the satellite and transmitter/receiver linear filter used in the overall system. In this paper, we propose a complex radial basis function network(CRBFN) based adaptive equalizer for compensation of nonlinearities in digital satellite communication channels. The proposed CRBFN untilizes a complex-valued hybrid learning algorithm of k-means clustering and LMS(least mean sequare) algorithm that is an extension of Moody Darken's algorithm for real-valued data. We evaluate performance of CRBFN in terms of symbol error rates and mean squared errors nder various noise conditions for 4-PSK(phase shift keying) digital modulation schemes and compare with those of comples pth order inverse adaptive Volterra filter. The computer simulation results show that the proposed CRBFN ehibits good equalization, low computational complexity and fast learning capabilities.