• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.31-38
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• 2015

In this paper, we propose the ensemble-based classification model which extracts just new data patterns from the streaming-data by using clustering and generates new classification models to be added to the ensemble in order to reduce the number of data labeling while it keeps the accuracy of the existing system. The proposed technique performs clustering of similar patterned data from streaming data. It performs the data labeling to each cluster at the point when a certain amount of data has been gathered. The proposed technique applies the K-NN technique to the classification model unit in order to keep the accuracy of the existing system while it uses a small amount of data. The proposed technique is efficient as using about 3% less data comparing with the existing technique as shown the simulation results for benchmarks, thereby using clustering.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.77-85
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• 2017

Due to the expansion of accommodation space caused by increase of population along with lifestyle changes, most of people spend their time indoor except for the travel time. Because of this, environmental change of indoor is very important, and it affects people's health and economy in resources. But, most of people don't acknowledge the importance of indoor environment. Thus, monitoring system for sustaining and managing indoor environment systematically is needed, and big data clusters should be used in order to save and manage numerous sensor data collected from many spaces. In this paper, we design a big data cluster for the indoor environment monitoring in order to store the sensor data and monitor unit of the huge building Implementation design big data cluster-based system for the analysis, and a distributed file system and building a Hadoop, HBase for big data processing. Also, various sensor data is saved for collection, and effective indoor environment management and health enhancement through monitoring is expected.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.141-157
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• 1997

The structure and dimensionality of the User Information Satisfaction (UIS) construct is an important theoretical issue that received considerable attentions. The acceptance of UIS as a standardized instrument requires confirmation that it explains and measures the user information satisfaction construct and its component. Based on a simple of 670 respondents who participated in dealing with the Public Management Information System (PMIS), this research used a confirmatory factor analysis to test the alternavtive models of underlying factor structure and assessed the reliability and validity of these factors and items in the PMIS. The result provided a support for a revised UIS model with four first-order factors and one PMIS The result provided a support for a revised UIS model with four first-order factors and one second-order (higher-order) factor in PMIS. To cross-validata these results, the author reexamined two prior data sets. The results showed that the revised model provides better model-data fit in all three data sets.

• Bulletin of the Korean Chemical Society
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• v.34 no.11
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• pp.3391-3398
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• 2013

The carboxylated multiwall carbon nanotubes (MWCNT-COOH) and functionalized with isatin derivative (MWCNT-isatin) have been used as efficient adsorbents for the removal of lead (Pb) from aqueous solutions. The influence of variables including pH, concentration of the lead, amount of adsorbents and contact time was investigated by the batch method. The adsorption of the lead ions from aqueous solution by modified MWCNTs was studied kinetically using different kinetic models. The kinetic data were fitted with pseudo-first-order, pseudo-second-order, and intra-particle diffusion models. The sorption process with MWCNT-COOH and MWCNT-isatin was well described by pseudo-second-order and pseudo-first-order kinetics, respectively which it was agreed well with the experimental data. Also, it involved the particle-diffusion mechanism. The values of regression coefficient of various adsorption isotherm models like Langmuir, Freundlich and Tempkin to obtain the characteristic parameters of each model have been carried out. The Langmuir isotherm was found to best represent the measured sorption data for both adsorbent.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.94-106
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• 2014

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.104-108
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• 2021

In order to improve performance in IoT, edge computing system, a memory is usually configured in a hierarchical structure. Based on the distance from CPU, the access speed slows down in the order of registers, cache memory, main memory, and storage. Similar to the change in performance, energy consumption also increases as the distance from the CPU increases. Therefore, it is important to develop a technique that places frequently used data to the upper memory as much as possible to improve performance and energy consumption. However, the technique should solve the problem of cache performance degradation caused by lack of spatial locality that occurs when the data access stride is large. This study proposes a technique to selectively place data with large data access stride to a software-controlled cache. By using the proposed technique, data spatial locality can be improved by reducing the data access interval, and consequently, the cache performance can be improved.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.13-20
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• 2009

The most of navigation services these days, are designed in order to just provide a shortest path from current position to destination for a user. Several navigation services provides not only the path but some fragmentary information about its point, but, the data tends to be highly restricted because it's quality and quantity totally depends on service provider's providing policy. In this paper, we describe the folksonomy POI(Point of interest) recommender system using mashup in order to provide the information that is more useful to the user. The POI recommender system mashes-up the user's folksonomy data that stacked by user with using external folksonomy service(like Flickr) with others' in order to provide more useful information for the user. POI recommender system recommends others' tag data that is evaluated with the user folksonomy similarity. Using folksonomy mahup makes the services can provide more information that is applied the users' karma. By this, we show how to deal with the data's restrictions of quality and quantity.

• Journal of the Korean Society of Safety
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• v.15 no.1
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• pp.43-52
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• 2000

Generally, photoelastic experimental data were measured in the closed vicinity of crack tip to determine stress intensity factors of a crack with photoelastic experiment method. In this case, only the first order term has been considered in the equation of stress field. But because it is very difficult to measure the correct photoelastic data in the closed vicinity of crack, the accuracy of experimental results was very poor. By including the high order terms in the stress field equation we could obtain the accurate S.I.F values by using clear photoelastic data in the distant region from crack tip instead of unclear photoelastic data in the vicinity of crack tip.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2002.02a
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• pp.26-33
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• 2002

Die Factory follows typical order adaptive manufacturing, and delaying delivery affects directly product development of customer, Manufacturing Management System is tried to comply with the appointed date of delivery. It acquires running signal from NC milling, calculates manufacturing results, and offers the basic data to manage the operation ratio. Thus it offers Production data necessary to accomplish the objective of progress improvement for Unmanned Manufacturing. Manufacturing Management System runs on Web Environment, and is composed of electronic work order, operation ratio data acquisition and totaling module.