• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2003.05a
• /
• pp.400-409
• /
• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• Journal of Biosystems Engineering
• /
• v.27 no.3
• /
• pp.227-234
• /
• 2002

This study was conducted to nondestructively estimate the volumetric information of peach and pit and to visualize the 3D information of internal structure from magnetic resonance imaging(MRI) data. Bruker Biospec 7T spectrometer operating at a proton reosonant frequency of 300 MHz was used for acquisition of MRI data of peach. Image processing algorithms and visualization techniques were implemented by using MATLAB (Mathworks) and Visualization Toolkit(Kitware), respectively. Thresholding algorithm and Kohonen's self organizing map(SOM) were applied to MRI data fur region segmentation. Volumetric information were estimated from segemented images and compared to the actual measurements. The average prediction errors of peach and pit volumes were 4.5%, 26.1%, respectively for the thresholding algorithm. and were 2.1%, 19.9%. respectively for the SOM. Although we couldn't get the statistically meaningful results with the limited number of samples, the average prediction errors were lower when the region segmentation was done by SOM rather than thresholding. The 3D visualization techniques such as isosurface construction and volume rendering were successfully implemented, by which we could nondestructively obtain the useful information of internal structures of peach.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.3
• /
• pp.223-230
• /
• 2001

Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

• Proceedings of the KIEE Conference
• /
• 2004.05b
• /
• pp.109-112
• /
• 2004

In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• The Journal of the Korea Contents Association
• /
• v.8 no.5
• /
• pp.9-18
• /
• 2008

Web-services have originally been devised to share information as open services. In connection with it, hacking incidents have surged. Currently, Web-log analysis plays a crucial clue role in detecting Web-hacking. A growing number of cases are really related to perceiving and improving the weakness of Web-services based on Web-log analysis. Such as this, Web-log analysis plays a central role in finding out problems that Web has. Hence, Our research thesis suggests Web-DoS-hacking detective technique In the process of detecting such problems through SOM algorithm, the emergence frequency of BMU(Best Matching Unit) was studied, assuming the unit with the highest emergence frequency, as abnormal, and the problem- detection technique was recommended through the comparison of what's called BMU as input data.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.301-306
• /
• 2006

본 연구에서는 강우의 시 공간적 분포의 불규칙한 변동성을 고려한 강우-유출예측을 위해 인공신경망(Artificial Neural Networks: ANNs)의 기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론과 역전파 학습 알고리즘(Back Propagation Algorithm: BPA) 이론을 복합적으로 이용하였다. 기존의 인공신경망 연구에서 야기된 저..갈수기의 유출량에 대한 과대평가, 홍수기의 유출량에 대한 과소평가, 예측값이 선행 유출량의 지속성을 갖는 Persistence 현상을 해결하기 위하여 패턴분류 성능을 지닌 SOM 이론을 도입하여 예측모형의 전처리 과정으로 이용하였다. 이는 기존의 인공신경망 모형이 하나의 모형을 구성하여 유출량의 전 범위에 해당하는 자료를 예측하는 방법을 개선한 것으로 SOM에 의해 패턴이 분류된 강우-유출관계의 각 패턴별 예측모형을 통해 분류된 자료들의 예측을 수행하는 방법이다. 이와 같이 SOM을 강우-유출예측모형의 전처리과정으로 이용함으로서 기존의 인공신경망 연구에서 야기된 현상들을 해결할 수 있었고, 예측력 또한 기존의 인공신경망 모형의 결과에 비해 우수하였다.

• Journal of Korea Multimedia Society
• /
• v.10 no.6
• /
• pp.699-706
• /
• 2007

This article is an approach to improving the clustering of Web images by using high-level semantic features from text information relevant to Web images as well as low-level visual features of image itself. These high-level text features can be obtained from image URLs and file names, page titles, hyperlinks, and surrounding text. As a clustering algorithm, a self-organizing map (SOM) proposed by Kohonen is used. To evaluate the clustering efficiencies of SOMs, we propose a simple but effective measure indicating the accumulativeness of same class images and the perplexities of class distributions. Our approach is to advance the existing measures through defining and using new measures accumulativeness on the most superior clustering node and concentricity to evaluate clustering efficiencies of SOMs. The experimental results show that the high-level text features are more useful in SOM-based Web image clustering.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.1B
• /
• pp.61-67
• /
• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Journal of Intelligence and Information Systems
• /
• v.15 no.2
• /
• pp.49-59
• /
• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• The Journal of Korean Association of Computer Education
• /
• v.9 no.2
• /
• pp.101-109
• /
• 2006

We propose an adaptive digital watermarking algorithm using HVS(human visual system) and SOM(Self-Organizing Map) among neural networks. This method adjusts adaptively the strength of the watermark which is embedded in different blocks according to block classification in DCT(Discrete Cosine Transform) domain. All blocks in 3 classes out of 4 are selected to embed a watermark. Watermark sequences are embedded in 6 lowest frequency coefficients of each block except the DC component. The experimental results are excellent.