• Journal of Power Electronics
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• v.17 no.5
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• pp.1372-1381
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• 2017

This paper presents a high-side switch driver IC capable of improving the current sensing accuracy and providing reverse battery protection. Power semiconductor switches used to replace relay switches are encumbered by two disadvantages: they are prone to current sensing errors and they require additional external protection circuits for reverse battery protection. The proposed IC integrates a gate driver and current sensing blocks, thus compensating for these two disadvantages with a single IC. A p-sub-based 90-V $0.13-{\mu}m$ bipolar-CMOS-DMOS (BCD) process is used for the design and fabrication of the proposed IC. The current sensing accuracy (error ${\leq}{\pm}5%$ in the range of 0.1 A-6.5 A) and the reverse battery protection features of the proposed IC were experimentally tested and verified.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.1
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• pp.9-17
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• 2006

The thrust measurement systems(TMS) with high accuracy are required in rockery, according to develop the high precise guided space vehicle. For obtaining high accuracy, the basic concepts and the necessary technology which have been acquired through many experiences of TMS are summarized, and the design methodology for practical use in ADD is presented. In this paper, the parameters against accuracy of TMS are discussed, and the improving methods are suggested. Through this application example, the design methodology of ADD is shown its superiority in TMS.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.428-435
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• 2011

Many isolated word recognition systems may generate irrelevant words for recognition results because they use only acoustic information or small amount of language information. In this paper, I propose word similarity that is used for selecting (or removing) less common words from candidates by applying Levenshtein distance. Word similarity is obtained by using positional accuracy that reflects the frequency information along to character's alignment information. This paper also discusses various improving techniques of selection of disparate words. The methods include different loss values, phone accuracy based on confusion information, weights of candidates by ranking order and partial comparisons. Through experiments, I found that the proposed methods are effective for removing heterogeneous words without loss of performance.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• Structural Engineering and Mechanics
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• v.76 no.3
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• pp.379-393
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• 2020

In this paper, we propose an automatic procedure to improve the accuracy of finite element solutions using enriched 2D solid finite elements (4-node quadrilateral and 3-node triangular elements). The enriched elements can improve solution accuracy without mesh refinement by adding cover functions to the displacement interpolation of the standard elements. The enrichment scheme is more effective when used adaptively for areas with insufficient accuracy rather than the entire model. For given meshes, an error for each node is estimated, and then proper degrees of cover functions are applied to the selected nodes. A new error estimation method and cover function selection scheme are devised for the proposed adaptive enrichment scheme. Herein, we demonstrate the proposed enrichment scheme through several 2D problems.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.255-260
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• 2013

In this paper, we propose the effective bit expansion of FFT module for improving the accuracy of correlation result of the Daejeon correlator. The Daejeon correlator based on FPGA was implemented in order to fast data processing with the fixed-point of FFT operation. In correlation result, however, the phenomenon of phase concentration to 0 degree was appeared in lower frequency area of bandwidth due to lack of operational bit. This phenomenon has an affect on the accuracy of correlation result by introducing the effect of data loss because of excluding phase concentration during analysis of observed radio source. In order to improving the accuracy of correlation result we carried out the simulation by expanding bit-number than 16-bit operation of previous FFT module within given resource limits of FPGA. Through the simulation results, the effective bit number for FFT module within used FPGA resource limits is able to expand, and we confirmed that the operational 20-bit of FFT module is effective for improving accuracy of correlation result by comparing with experimental result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3515-3538
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• 2015

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.755-760
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• 2011

This paper presents a method for improving the positioning accuracy of the laser navigation. As a wireless navigation system, the laser navigation which is more flexible than a wired guidance system is used for the localization and control of an AGV(automatic guided vehicle). However, the laser navigation causes the large positioning error while the AGV turns or moves fast. To solve the problem, we propose the method for improving the positioning accuracy of the laser navigation using particle filter which has robust and reliable performance in non-linear/non-gaussian systems. For the experiment, we use the actual fork-type AGV. The AGV has a gyro, two encoders and a laser navigation. To verify the performance, the proposed method is compared with the laser navigation which is a product. In the experimental result, we verified that the proposed method could improve the positioning accuracy by approximately 66.5%.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.93-101
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• 2021

In this paper, a vehicle license plate detection scheme is proposed that uses the spatial attention areas to detect accurately the license plates in various real-road situations. First, the previous WPOD-NET was analyzed, and its detection accuracy is evaluated as lower due to the unnecessary noises in the wide detection candidate areas. To resolve this problem, a vehicle license plate detection model is proposed that uses the candidate area of the license plate as a spatial attention areas. And we compared its performance to that of the WPOD-NET, together with the case of using the optimal spatial attention areas using the ground truth data. The experimental results show that the proposed model has about 20% higher detection accuracy than the original WPOD-NET since the proposed scheme uses tight detection candidate areas.