• Smart Structures and Systems
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• v.26 no.4
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• pp.495-506
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• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.523-528
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• 2015

In this paper, we propose a new multiresolutional and dynamic approach of the EM algorithm. EM is a very popular and powerful clustering algorithm. EM, however, has problems that indexes multiresolution data and requires a priori information on a proper number of clusters in many applications, To solve such problems, the proposed EM algorithm can impose a multiresolution kd-tree structure in the E-step and allocates a cluster based on sequential data. To validate clusters, we use a merge criteria for cluster merging. We demonstrate the proposed EM algorithm outperforms for texture image segmentation.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.185-187
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• 2013

유비쿼터스 컴퓨팅이 생활의 일부가 되어가면서 정보의 양도 급속도로 늘어나고 있으며, 이로 인해 많은 데이터 속에서 정보를 찾아내는 기술이 부각되고 있다. 고객 기반의 협력적 필터링을 이용한 고객 선호도 예측 방법에서는 아이템에 대한 사용자의 선호도를 기반으로 이웃 선정 방법을 사용하므로 아이템에 대한 내용을 반영하지 못할 뿐만 아니라 희박성 문제를 해결하지 못하고 있다. 그리고 비슷한 선호도를 가진 일부 아이템의 정보를 바탕으로 하기 때문에 아이템의 속성은 무시하는 경향이 있다. 본 논문에서는 유비쿼터스 상거래에서 RFM(Recency, Frequency, Monetary) 분석 기반의 SOM을 이용한 군집방법을 제안한다. 제안 방법은 고객의 구매 데이터 기반의 유사한 속성의 데이터끼리의 클러스터링을 통해 보다 빠른 시간 내에 고객 성향에 맞는 추천이 가능한 구매 패턴 추출이 가능하다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.396-399
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• 2012

Topic detection is a problem of discovering the topics of online publishing documents. For topic detection, it is important to extract correct topic words and to show the topical words easily to understand. We consider a topic tree-based approach to more effectively and more briefly show the result of topic detection for online text documents. In this paper, to achieve the topic tree-based topic detection, we propose a new term weighting method, called CTF-CDF-IDF, which is simple yet effective. Moreover, we have modified a conventional clustering method, which we call incremental k-medoids algorithm. Our experimental results with Reuters-21578 and Google news collections show that the proposed method is very useful for topic detection.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.169-177
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• 2009

In this paper, we propose a new shot boundary detection method which is optimized for news video story parsing. This new news shot boundary detection method was designed to satisfy all the following requirements: 1) minimizing the incorrect data in data set for anchor shot detection by improving the recall ratio 2) detecting abrupt cuts and gradual transitions with one single algorithm so as to divide news video into shots with one scan of data set; 3) classifying shots into static or dynamic, therefore, reducing the search space for the subsequent stage of anchor shot detection. The proposed method, based on singular value decomposition with incremental clustering and mercer kernel, has additional desirable features. Applying singular value decomposition, the noise or trivial variations in the video sequence are removed. Therefore, the separability is improved. Mercer kernel improves the possibility of detection of shots which is not separable in input space by mapping data to high dimensional feature space. The experimental results illustrated the superiority of the proposed method with respect to recall criteria and search space reduction for anchor shot detection.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.233-240
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• 2004

This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in Korean-to-English and Japanese-to-English cross-language information retrieval (CLIR). The main objective of this paper shows that document clusters can effectively resolve the ambiguities tremendously increased in translated queries as well as take into account the context of all the terms in a document. In the framework we propose, a query in Korean/Japanese is first translated into English by looking up bilingual dictionaries, then documents are retrieved for the translated query terms based on the vector space retrieval model or the probabilistic retrieval model. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is re-calculated by using the clusters. In the experiment based on TREC test collection, our method achieved 39.41% and 36.79% improvement for translated queries without ambiguity resolution in Korean-to-English CLIR, and 17.89% and 30.46% improvements in Japanese-to-English CLIR, on the vector space retrieval and on the probabilistic retrieval, respectively. Our method achieved 12.30% improvements for all translation queries, compared with blind feedback in Korean-to-English CLIR. These results indicate that cluster analysis help to resolve ambiguity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.781-790
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• 2013

This research focuses on development of the conceptual cost estimation models for I.L.M box girder bridge. The current conceptual cost estimation for public construction projects is dependent on governmental average unit price references which has been regarded as inaccurate and unreliable by many experts. Therefore, there have been strong demands for developing a better way of conceptual cost estimating methods. This research has proposed three different conceptual cost estimating method for a P.S.C. girder bridge built with the I.L.M method. Model (I) attempts to seek the proper breakdown of standard works that are accountable for more than 95 percentage in total cost and calculates the amount of standard work's materials from the standard section and volume of I.L.M box girder bridge. Model (II) utilizes a correlation analysis (coefficient over 0.6 or more) between breakdown of standard works and input data that would be considered available information in preliminary design phase. Model(III) obtains conceptual estimating through multiple-regression analysis between the breakdown of standard works and all of input data related to them. In order to validate the clustering of coverage in the preliminary design phase, the variation of I.L.M cost coverage from multiple-regression analysis[model(III)] has been investigated which result in between -3.76% and 11.79%, comparing with AACE(Association for the Advancement of Cost Engineering) which informs its variation between -5% and +15% in the design phase. The model proposed from this research are envisioned to be improved to a great distinct if reliable cost date for P.S.C. girder bridges can be continually collected with reasonable accuracies.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.373-379
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• 2015

In this paper, we propose an online VQ Codebook generation method for updating an existing VQ Codebook in real-time and adding to an existing cluster with newly created text data which are news paper, web pages, blogs, tweets and IoT data like sensor, machine. Without degrading the performance of the batch VQ Codebook to the existing data, it was able to take advantage of the newly added data by using a triangle inequality which modifying the VQ Codebook progressively show a high degree of accuracy and speed. The result of applying to test data showed that the performance is similar to the batch method.