• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.813-818
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• 2007

This paper is concerned with the problem of recognizing definition sentences. Given a definition question like "Who is the person X?", we are to retrieve the definition sentences which capture descriptive information correspond variously to a person's age, occupation, of some role a person played in an event from the collection of news articles. In order to retrieve as many relevant sentences for the definition question as possible, we adopt a centroid based statistical approach which has been applied in summarization of multiple documents. To improve the precision and recall performance, the weight measure of centroid words is supplemented by using external knowledge resource such as Wikipedia and redundant candidate sentences are removed from candidate definitions. We see some improvements obtained by our approach over the baseline for 20 IT persons who have high document frequency.

• Journal of Institute of Convergence Technology
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• v.12 no.1
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• pp.31-35
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• 2022

This study has been carried out in order to improve the rolling problem by enhancing steering stability compared to the 2021 Student Car of the KNUT_EV team for KSAE. Among the various factors affecting steering performances, it was focused on the height of the centroid of weight, the motion ratio, and the spring deflection. In the 2022 Car, a pull rod suspension was used to reduce the height of the centroid of weight and designed with a structure of the rod and rocker to satisfy the target motion ratio. The spring deflection was testified by ADAMS and ABAQUS analysis, and the spring stiffness was selected at 350lb/inch and 450lb/inch for the front and rear wheels, respectively. As a result, the rolling angle of the 2022 Car was reduced compared to the 2021 Car, and the rolling phenomenon was improved.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.2
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• pp.260-265
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• 2012

Satellite devices for fine attitude control of the Science & Technology Satellite-2 (STSAT-2). Based on the mission requirements of STSAT-2, the conventional analog-type sun sensors were found to be inadequate, motivating the development of a compact, fast and fine digital sun sensor (FDSS). The FDSS uses a CMOS image sensor and has an accuracy of less than 0.03degrees, an update rate of 5Hz and a weight of less than 800g. A pinhole-type aperture is substituted for the optical lens to minimize its weight. The target process speed is obtained by utilizing the Field Programmable Gate Array (FPGA), which acquires images from the CMOS sensor, and stores and processes the image data. The sensor accuracy is maintained by a rigorous centroid algorithm. This paper describes the FDSS designs, realizations, tests and calibration results.

• Journal of the Korean Society for information Management
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• v.18 no.3
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• pp.159-178
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• 2001

This paper presents an automatic text summarization model which selects representative sentences from sentence clusters to create a summary. Summary generation experiments were performed on two sets of test documents after learning the optimum environment from a training set. Centroid clustering method turned out to be the most effective in clustering sentences, and sentence weight was found more effective than the similarity value between sentence and cluster centroid vectors in selecting a representative sentence from each cluster. The result of experiments also proves that inverse sentence weight as well as title word weight for terms and location weight for sentences are effective in improving the performance of summarization.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1787-1790
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• 2005

We have developed satellite devices for fine attitude control of the Science & Technology Satellite-2 (STSAT-2) scheduled to be launched in 2007. The analog sun sensors which have been continuously developed since the 1990s are not adequate for satellites which require fine attitude control system. From the mission requirements of STSAT-2, a compact, fast and fine digital sensor was proposed. The test of the fine attitude determination for the pitch and roll axis, though the main mission of STSAT-2, will be performed by the newly developed FDSS. The FDSS use a CMOS image sensor and has an accuracy of less than 0.01degrees, an update rate of 20Hz and a weight of less than 800g. A pinhole-type aperture is substituted for the optical lens to minimize the weight while maintaining sensor accuracy by a rigorous centroid algorithm. The target process speed is obtained by utilizing the Field Programmable Gate Array (FPGA) in acquiring images from the CMOS sensor, and storing and processing the data. This paper also describes the analysis of the optical performance for the proper aperture selection and the most effective centroid algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.616-619
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• 2000

The optimum design of a structure requires to determine economical member size and shape of a structure which satisfies the design conditions and functions. In this study, it is attempted to minimize a dead weight of the bogie frame. Therefore, shape optimization is performed for a bolster rib at first and then size optimization for the thickness of top and bottom plate. For the efficient reduction of a weight of a bogie frame, various ellipses centered at a centroid of a bolster rib are made and tried. For the shape optimization, a major axis and an eccentricity of an ellipse are chosen as design variables. From the numerical results of shape and size optimization of a bogie frame, it is known that the weight can be reduced up to 12.476 Y4717.21 kg) with displacement and stress constraints.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.450-458
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• 2019

Passive cavitation imaging method is used to observe the ultrasonic waves generated when a group of bubbles collapses. A problem with passive cavitation imaging is a low resolution and large side lobe levels. Since ultrasound signals generated by passive cavitation take the form of a pulse, the amplitude distribution of signals received across the receive channels varies depending on the direction of incidence. Both the centroid and flatness were calculated to determine weights at imaging points in order to discriminate between the main and side lobe signals from the signal amplitude distribution of the received channel data and to reduce the side lobe levels. The centroid quantifies how the channel data are distributed across the receive channel, and the flatness measures the variance of the channel data. We applied the centroid weight and the flatness to the passive cavitation image constructed using the delay-and-sum focusing and minimum variance beamforming methods to improve the image quality. Using computer simulation and experiment, we show that the application of weighting in delay-and-sum and minimum variance beamforming reduces side lobe levels.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.229-236
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• 2017

An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.137-140
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• 2000

We provide an useful method to design codebooks with better performance than conventional methods. In the proposed method, new codevectors obtained by learning iterations are not the centroid vectors which is the representatives of partitions, but the vectors manipulated by the distance between new codevectors and old codevectors in the early stages of learning iteration. Experimental results show that the codevectors in the obtained by the proposed method converge to a better locally optimal codebook.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.