• The KIPS Transactions:PartA
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• v.8A no.3
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• pp.209-216
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• 2001

The effective way to apply incremental attribute grammar to a complex language process is the use of optimized algorithm. In optimized algorithm for incremental attribute grammar, the new input attribute tree should be exactly compared with the previous input attribute tree, in order to determine which subtrees from the old should be used in constructing the new one. In this paper the new optimized algorithm was reconstructed by analyzing the algorithm suggested by Carle and Pollock, and a generation process of new attribute tree d’copy was added. Through the performance evaluation for the suggested matching algorithm, the run time is approximately improved by 19.5%, compared to the result of existing algorithm.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.127-132
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• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.63-70
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• 2009

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.163-173
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• 2016

Recently, massive amounts of stream data have been studied for distributed processing. In this paper, we propose an incremental stream data processing method based on in-memory in big data environments. The proposed method stores input data in a temporary queue and compare them with data in a master node. If the data is in the master node, the proposed method reuses the previous processing results located in the node chosen by the master node. If there are no previous results of data in the node, the proposed method processes the data and stores the result in a separate node. We also propose a job scheduling technique considering the load and performance of a node. In order to show the superiority of the proposed method, we compare it with the existing method in terms of query processing time. Our experimental results show that our method outperforms the existing method in terms of query processing time.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.387-398
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• 2003

Materialized views have received much attention for query performance improvement. They need to be refreshed whenever their underlying data sources are updated. They could be recomputed from scratch or they could be incrementally refreshed by reflecting only those portions of updates that affect them. With emergence of XML as the standard for data exchange on the Web, active research is under way on effectively storing and retrieving XML documents. In this paper, we describe a performance study on the incremental refresh of XML materialized views for the case where XML documents are stored in a relational DBMS, and XML materialized views are maintained with incremental refresh. We describe implementation of a prototype XML storage system that supports XML materialized views and their incremental refresh, and report the performance results obtained with the implemented system through a detailed set of experiments on the incremental refresh of XML materialized views. The results show that the XML view maintenance with incremental refresh outperforms the ordinary view recomputation.

• Transactions of Materials Processing
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• v.28 no.6
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• pp.328-335
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• 2019

Demand for ultra-thin materials is increasing due to their light-weight and versatile properties. In this work, the formability of the ultra-thin stainless steel sheets of various thicknesses in the incremental sheet forming (ISF) process is investigated. The effects of the thickness on formability were evaluated with forming experiments of the truncated cone shape with 10° intervals. As the thickness of the material decreased, the maximum forming angle decreased and wrinkles also occurred quickly. The maximum forming angles in the truncated cone shape without the wrinkles for the thickness of 0.05 mm, 0.08 mm, and 0.1mm were 30°, 40°, and 50°, respectively. Wrinkles occurred in a twisted shape along the moving direction of the tool. As the material thickness increased, the size of the wrinkles increased.

• Transactions of Materials Processing
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• v.7 no.3
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• pp.233-245
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• 1998

A new approach has been proposed for the incremental analysis of the nonsteady state large deformation of planar anisotropic elastic-plastic sheet forming. A mathematical brief review of a constitutive law for the incremental deformation theory has been presented from flow theory using the minimum plastic work path for elastic-plastic material. Since the material embedded coordinate system(Lagrangian quantity) is used in the proposed theory the stress integration procedure is completely objective. A new return mapping algorithm has been also developed from the general midpoint rule so as to achieve numerically large strain increment by successive control of yield function residuals. Some numerical tests for the return mapping algorithm were performed using Barlat's six component anisotropic stress potential. Performance of the proposed algorithm was shown to be good and stable for a large strain increment, For planar anisotropic sheet forming updating algorithm of planar anisotropic axes has been newly proposed. In order to show the effectiveness and validity of the present formulation earing simulation for a cylindrical cup drawing and front fender stamping analysis are performed. From the results it has been shown that the present formulation can provide a good basis for analysis for analysis of elastic-plastic sheet metal forming processes.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.327-338
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• 2001

The view can provide the user an appropriate portion of the database through data integration and filtering. Views can be materialized for query performance improvement, and in that cse, their consistency needs to be maintained against the updates of the underlying data. They can be either recomputed or incrementally refreshed by reflecting the relevant updates. Since XML could represent the structural information of the documents, for the XML materialized views, new techniques that differ from the previous ones for incrementally refreshing the relational views are required. In this paper, we propose a framework of XML materialized view management where the XML view derived from the underlying XML documents are materialized and incrementally refreshed against the updates of the underlying documents.

• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.295-302
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• 2006

An intrusion detection system writes a lot of alarms against attack behaviors in real time. These alarms contain not only actual attack alarms, but also false alarms that are mistakes made by the intrusion detection system. False alarms are the main reason that reduces the efficiency of the intrusion detection system, and we propose framework for false alarms analysis in the paper. Also, we apply an incremental data mining method for pattern analysis of false alarms increasing continuously. The framework consists of GUI, DB Manager, Alert Preprocessor, and False Alarm Analyzer. We analyze the false alarms increasingly through the experiment of the proposed framework and show that false alarms are reduced by applying the analyzed false alarm rules in the intrusion detection system.