• IE interfaces
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• v.22 no.2
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• pp.126-134
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• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques 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 stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. 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 stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• International Journal of Contents
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• v.11 no.1
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• pp.69-72
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• 2015

Generally, existing parallel main-memory spatial index structures to avoid the trade-off between query freshness and CPU cost uses light-weight locking techniques. However, still, the lock based methods have some limits such as thrashing which is a well-known problem in lock based methods. In this paper, we propose a distributed index structure for moving objects exploiting the parallelism in multiple machines. The proposed index is a lock free multi-version concurrency technique based on the D-Stream model of Spark Stream. The proposed method exploits the multiversion nature of D-Stream of Spark Streaming.

• Journal of Korean Society of Rural Planning
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• v.25 no.1
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• pp.21-38
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• 2019

Korean agricultural industry has weakened as demand for domestic agricultural products has declined due to accelerating market liberalization, aging and shrinking of rural population, and stagnating rural households' incomes. On the other hand, as the forth industrial revolution unfolds in earnest, tremendous changes are expected, and those changes won't be confined to certain industries but would shaken the world we know of entirely. Smart farm, which is one example of the fourth industrial revolution, is increasingly being recognized as a new growth engine for the future as smart farm and the science and technology behind it, not the size of arable land, will determine competitiveness of the agricultural industry and drive agricultural productivity and managerial efficiency. In consideration that John W. Kingdon's Multiple Streams Framework has recently been presented as an important theoretical model in the policy field, this study analyzed problem stream, policy stream, and political stream in the process of forming the smart farm policy, and looked into what role the government played as policy entrepreneur in policy window. The smart farm policy was put on policy agenda by the government and was approved when the government announced the Smart Farm Plan together with relevant ministries at the 5th Economy-Related Ministers' Meeting held in April 2018. This suggests that change of the government is the most critical factor in political stream, and explicitly indicates the importance of politics in formation of an agricultural policy. In addition, actual outcome of the policy and how policy alternatives that will enhance people's understanding will support it seem to be the key to success. It also shows that it is important that policy alternatives be determined based on sufficient discussion amongst stakeholders.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1076-1080
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• 2015

An inventory control system was developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process. The receipt of customer orders by the warehouse proceeded in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm was developed using a periodic square wave model to represent the material flows. The adaptive concept incorporated a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The effectiveness of the scheme was demonstrated using simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1385-1402
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• 2019

Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value. Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper. The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams. Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology. At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1243-1258
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• 2012

The objective of this study is to develop the relational formula to estimate the streamflow depletion due to groundwater pumping near stream, which has been statistically derived by using the simulated data. The integrated surface water and groundwater model, SWAT-MODFLOW was applied to the Sinduncheon and Juksancheon watersheds to obtain the streamflow depletion data under various pumping conditions. Through the multiple regression analyses for the simulated streamflow depletion data, the relational formula between the streamflow depletion rate and various factors such as pumping rate, distance between well and stream, hydraulic properties in/near stream, amount of rainfall was obtained. The derived relational formula is easy to apply for assessing the effects of groundwater pumping on near stream, and is expected to be a tool for estimate the streamflow contribution to the pumped water.

• Journal of Environmental Impact Assessment
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• v.23 no.3
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• pp.187-196
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• 2014

The objective of this study is to evaluate and analyze Osu stream watershed water environment system. The data were collected from January 2009 to December 2011 including water temperature, pH, DO, EC, BOD, COD, TOC, SS, T-N, T-P and discharge. The data were used for principle component analysis and factor analysis. The results are as followes. The primary factors obtained from both the principal component analysis and the factor analysis were BOD, COD, TOC, SS and T-P. Once principal component analysis and factor analysis have been performed with the collected data and then the results will be applied to both simple regression model and multiple regression model. The regression model was developed into case 1 using concentrations of water quality parameters and case 2 using delivery loads. The value of the coefficient of determination on case 1 fell between 0.629 and 0.866; this was lower than case 2 value which fell between 0.946 and 0.998. Therefore, case 2 model would be a reliable choice.The coefficient of determination between the estimated figure using data which was developed to the regression model in 2012 and the actual measurement value was over 0.6, overall. It can be safely deduced that the correlation value between the two findings was high. The same model can be applied to get TOC concentrations in future.

• Journal of Environmental Science International
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• v.24 no.6
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• pp.743-753
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• 2015

In this study, it is an object to develop a regression model for the estimation of TOC (total organic carbon) concentration using investigated data for three years from 2010 to 2012 in the Gam Stream unit watershed, and applied in 2009 to verify the applicability of the regression model. TOC and $COD_{Mn}$ (chemical oxygen demand) were appeared to be derived the highest correlation. TOC was significantly correlated with 5 variables including BOD (biological oxygen demand), discharge, SS (suspended solids), Chl-a (chlorophyll a) and TP (total phosphorus) of p<0.01. As a result of PCA (principal component analysis) and FA (factor analysis), COD, TOC, SS, discharge, BOD and TP have been classified as a first factor. TOCe concentration was estimated using the model developed as an independent variable $BOD_5$ and $COD_{Mn}$. R squared value between TOC and measurement TOC is 0.745 and 0.822, respectively. The independent variable were added step by step while removing lower importance variable. Based on the developed optimal model, R squared value between measurement value and estimation value for TOC was 0.852. It was found that multiple independent variables might be a better the estimation of TOC concentration using the regression model equation(in a given sites).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.875-886
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• 1994

This study is to develop an advanced storm runoff analysis program which takes geomorphological characteristics of watershed into consideration in determining model parameters. Basic concept of storm runoff modelling is based upon the kinematic wave theory. And numerical solution is obtained by the characteristic curve method. The storm runoff analysis program developed by this study is composed of multiple equivalent roughness sub-basins, each of which has two equivalent catchments on both side of a stream. Because it is based upon the stream-order of the Strahler system, the equivalent catchment-stream network reflects the stochastic geomorphological characteristics in the model parameter. Applicability and reliability of the storm runoff analysis program is evidenced by model calibration and verification process utilizing geographical and hydrological data of the Bocheong-river area which is a representative watershed of IHP projects in Korea. This study will hopefully contribute to hydrological calculation essentially required to understand water quality effect caused by regional development.