• Spatial Information Research
• /
• v.19 no.1
• /
• pp.127-133
• /
• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.

• IE interfaces
• /
• v.18 no.2
• /
• pp.205-220
• /
• 2005

This study presents a web log analysis model for e-CRM, which combines the on-line customer's purchasing pattern data and transaction data between companies in B2B environment of make-to-order company. With this study, the customer evaluation and the customer subdivision are available. We can forecast the estimate demands with periodical products sales records. Also, the purchasing rate per each product, the purchasing intention rate, and the purchasing rate per companies can be used as the basic data for the strategy for receiving the orders in future. These measures are used to evaluate the business strategy, the quality ability on products, the customer's demands, the benefits of customer and the customer's loyalty. And it is used to evaluate the customer's purchasing patterns, the response analysis, the customer's secession rate, the earning rate, and the customer's needs. With this, we can satisfy various customers' demands, therefore, we can multiply the company's benefits. And we presents case of the 'H' company, which has the make-to-order manufacture environment, in order to verify the effect of the proposal system.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.509-515
• /
• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• Steel and Composite Structures
• /
• v.37 no.6
• /
• pp.741-759
• /
• 2020

Steel plate-concrete composite slabs provide attractive features, such as more effective loading transfer, and more cost-effective stay-in-place forms, thereby enabling engineers to design more high-performance light structures. Although significant studies in the literatures have been directed toward designing and implementing the steel plate-concrete composite beams, there are limited data available for understanding of the composite slabs. To fill this gap, nine the composite slabs with different variables in this study were tested to unveil the impacts of the critical factors on the ultimate strength behavior. The key information of the findings included sample failure modes, crack pattern, and ultimate strength behavior of the composite slabs under either four-point or three-point loading. Test results showed that the failure modes varied from delamination to shear failures under different design factors. Particularly, the shear stud spacing and thicknesses of the concrete slabs significantly affected their ultimate load-carrying capacities. Moreover, an analytical model of the composite slabs was derived for determining their ultimate load-carrying capacity and was well verified by the experimental data. Further extensive parametric study using the proposed analytical methods was conducted for a more comprehensive investigation of those critical factors in their performance. These findings are expected to help engineers to better understand the structural behavior of the steel plate-concrete composite slabs and to ensure reliability of design and performance throughout their service life.

• Journal of Intelligence and Information Systems
• /
• v.9 no.1
• /
• pp.157-177
• /
• 2003

This paper presents a comprehensive and successful application of data mining methodologies to improve and predict wafer yield in a semiconductor wafer fabrication system. As the wafer fabrication process is getting more complex and the volume of technological data gathered continues to be vast, it is difficult to analyze the cause of yield deterioration effectively by means of statistical or heuristic approaches. To begin with this paper applies a clustering method to automatically identify AUF (Area Uniform Failure) phenomenon from data instead of naked eye that bad chips occurs in a specific area of wafer. Next, sequential pattern analysis and classification methods are applied to and out machines and parameters that are cause of low yield, respectively. Furthermore, radial bases function method is used to predict yield of wafers that are in process. Finally, this paper demonstrates an information system, Y2R-PLUS (Yield Rapid Ramp-up, Prediction, analysis & Up Support), that is developed in order to analyze and predict wafer yield in a korea semiconductor manufacturer.

• Journal of KIISE:Software and Applications
• /
• v.31 no.1
• /
• pp.61-70
• /
• 2004

It is difficult to find an appropriate web site because exponentially growing web contains millions of web documents. Personalization of web search can be realized by recommending proper web sites using user profile but more efficient method is needed for estimating preference because user's evaluation on web contents presents many aspects of his characteristics. As user profile has a property of non-linearity, estimation by classifier is needed and combination of classifiers is necessary to anticipate diverse properties. Structure adaptive self-organizing map (SASOM) that is suitable for Pattern classification and visualization is an enhanced model of SOM and might be useful for web mining. Fuzzy integral is a combination method using classifiers' relevance that is defined subjectively. In this paper, estimation of user profile is conducted by using ensemble of SASOM's teamed independently based on fuzzy integral and evaluated by Syskill ＆ Webert UCI benchmark data. Experimental results show that the proposed method performs better than previous naive Bayes classifier as well as voting of SASOM's.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.6
• /
• pp.1-12
• /
• 2009

Recently, as the importance of Economical Driving has been gradually growing up, the needs for research on automatic analysis of driving patterns that will ultimately provide drivers the methods for Economical Driving have been increasingly risen. Based on this purpose, we have executed two things in this paper. First, we have collected overall driving information such as date, distance, driving time, speed, idle time, sudden acceleration/deceleration count, and the amount of fuel consumption. Second, we have analyzed the influences of driving patterns on economical driving by employing the data mining techniques. These results can be applied in preventing bad driving patterns which will have consequently bad effects on Economical Driving in two aspects: by presenting some information on the terminal of the vehicles such as idle time, over-speed time, sudden acceleration/deceleration count continuously and by providing the drivers with alert information when the idle time ratio and the over-speed time ratio are excessive.

• International Journal of Advanced Culture Technology
• /
• v.4 no.2
• /
• pp.38-42
• /
• 2016

In the growing data saturated world, the question of "whether data can be used" has shifted to "can it be utilized effectively?" More data is being generated and utilized than ever before. As the collection of data increases, data mining techniques also must become more and more accurate. Thus, to ensure this data is effectively utilized, the analysis of the data must be efficient. Interpretation of results from the analysis of the data set presented, have their own on the basis it is possible to obtain the desired data. In the data mining method a decision tree, clustering, there is such a relationship has not yet been fully developed algorithm actually still impact of various factors. In this experiment, the classification method of data mining techniques is used with easy decision tree. Also, it is used special technology of one R and J48 classification technique in the decision tree. After selecting a rule that a small error in the "one rule" in one R classification, to create one of the rules of the prediction data, it is simple and accurate classification algorithm. To create a rule for the prediction, we make up a frequency table of each prediction of the goal. This is then displayed by creating rules with one R, state-of-the-art, classification algorithm while creating a simple rule to be interpreted by the researcher. While the following can be correctly classified the pattern specified in the classification J48, using the concept of a simple decision tree information theory for configuring information theory. To compare the one R algorithm, it can be analyzed error rate and accuracy. One R and J48 are generally frequently used two classifications${\ldots}$

• The Journal of Korean Association of Computer Education
• /
• v.17 no.1
• /
• pp.117-130
• /
• 2014

The purpose of this study is to provide useful knowledge for improving indicators that represent competitiveness and educational competency of the university by deriving a new pattern or the meaningful results from the data of information disclosure of universities using statistical analysis and data mining techniques. To achieve this, a model of decision tree was made and various factors that affect education/research performance such as employment rate, the number of technology transfer and papers per full-time faculty were explored. In addition to this, the cluster analysis of universities was conducted using attributes related to evaluation of university. According to the analysis, common factors affecting higher education/research performance are following indicators ; incoming student recruitment rate, enrollment rate, and the number of students per full-time faculty. In the cluster analysis, when performed by the entire university, the size, location of the university respectively, clusters are mainly formed by well-known universities, art physical non-science and engineering religious leaders training universities, and others. The main influencing factors of this cluster are higher education/research performance indicators such as employment rate and the number of technology transfer.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.12
• /
• pp.33-41
• /
• 2011

From Complicated graph structures, duplicated operations can be executed and the operations give low efficiency. In this paper, we propose an efficient graph mining algorithm of minimizing the extension of duplicated graph patterns in which the priorities of cyclic edges are considered. In our approach, the cyclic edges with lower priorities are first extended and so duplicated extensions can be reduced. For performance test, we implement our algorithm and compare our algorithm with a state of the art, Gaston algorithm. Finally, We show that ours outperforms Gaston algorithm.