• Proceedings of the KSME Conference
• /
• 2000.11a
• /
• pp.177-184
• /
• 2000

In this work, some inaccuracies and limitation of prior indentation theory, which is based on the deformation theory of plasticity and experimental observations, are first investigated. Then effects of major material properties on the configuration of indentation load-deflection curve are examined via incremental plasticity theory based finite element analyses. It is confirmed that subindenter deformation and stress-strain distribution from the deformation theory of plasticity are quite dissimilar to those from incremental theory of plasticity. We finally suggest the optimal data acquisition location, where the strain gradient is the least and the effect of friction is negligible. This data acquisition point increases the strain range by a factor of five.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.5
• /
• pp.833-842
• /
• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.25 no.8
• /
• pp.1010-1018
• /
• 1988

This paper proposes a MOS circuit extractor which extracts a netlist from the hierarchical mask information, for the verification tools. To utilize the regularity and the simple representation of the hierarchical circuit, and to reduce the debug cycle of design, verification, and modification, we propose a hierarvhical and incremental circuit extraction algorithm. In flat circuit extraction stage, the multiple storage quad tree is used as an internal data structure. Incremental circuit extraction using the hierarchical structure is made possible, to reduce the re-extraction time of the modified circuit.

• Architectural research
• /
• v.18 no.4
• /
• pp.121-128
• /
• 2016

Low-income housing policies in South Korea have been pursued mostly by providing public rental housing for less privileged social groups. In contrast to this notion of housing, this article argues for housing ownership by low-income families. Two examples of this ownership policy are found in Chile. Incremental housing involves an open-ended housing platform, which requires home-dwellers to complete the construction process themselves. This article aims to examine structural, spatial and formal characteristics of the incremental housing projects. Taking the perspective of the home-dweller during the incremental construction process, we evaluate the houses before and after customization. Thus, we use data from field-work conducted for seven months in the Santiago Metropolitan Area of Chile. Using qualitative methods such as observation, semi-structured interviews, and surveys we focus on the Elemental Lo Espejo incremental housing project and then compare it with the Las Higuera housing project. The latter is representative of the incremental houses delivered by the Chilean government. In comparing these two projects, we aim to articulate the lessons of incremental housing with the intention of suggesting possible future developments for a wider-reaching incremental housing program.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.635-638
• /
• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of the Korea Safety Management & Science
• /
• v.19 no.2
• /
• pp.135-146
• /
• 2017

The purpose of this study is to review the relationship between managerial system and incremental innovation, and the mediating effect of knowledge transfer in small business. In order to verify and achieve the purposes mentioned above, questionnaire data were gathered and analysed from 255 enterprise managers in western Kangwon-do province. Empirical survey's findings are as follows; First, CEO's support and education/training appeared to be positively related with knowledge transfer. Second, managerial system and knowledge transfer appeared to be positively related with incremental innovation. Third, knowledge transfer had mediating effect on the relationships of CEO's support-incremental innovation and education/training-incremental innovation.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.3
• /
• pp.268-272
• /
• 2009

In this paper, we propose a Gaussian Mixture Model (GMM) based incremental robust adaptation with a forgetting factor for the speaker verification. Speaker recognition system uses a speaker model adaptation method with small amounts of data in order to obtain a good performance. However, a conventional adaptation method has vulnerable to the outlier from the irregular utterance variations and the presence noise, which results in inaccurate speaker model. As time goes by, a rate in which new data are adapted to a model is reduced. The proposed algorithm uses an incremental robust adaptation in order to reduce effect of outlier and use forgetting factor in order to maintain adaptive rate of new data on GMM based speaker model. The incremental robust adaptation uses a method which registers small amount of data in a speaker recognition model and adapts a model to new data to be tested. Experimental results from the data set gathered over seven months show that the proposed algorithm is robust against outliers and maintains adaptive rate of new data.

• Proceedings of the KSME Conference
• /
• 2000.11a
• /
• pp.185-192
• /
• 2000

A novel indentation theory is proposed by examining the data from the incremental plasticity theory based finite element analyses. First the optimal data acquisition location is selected, where the strain gradient is the least and the effect of friction is negligible. This data acquisition point increases the strain range by a factor of five. Numerical regressions of obtained data exhibit that strain hardening exponent and yield strain are the two main parameters which govern the subindenter deformation characteristics. The new indentation theory successfully provides the stress-strain curve with an average error less than 3%.

• International Journal of Contents
• /
• v.15 no.3
• /
• pp.32-38
• /
• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Journal of KIISE:Software and Applications
• /
• v.34 no.12
• /
• pp.1045-1055
• /
• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.