• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.743-758
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• 2005

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

• Asia pacific journal of information systems
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• v.11 no.1
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• pp.61-73
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• 2001

Predicting a variable using other variables in a large data set is a very difficult task. It involves selecting variables to include in a model and determining the shape of the relationship between variables. Nonparametric regression such as smoothing splines and neural networks are widely-used methods for such a task. We propose an alternative method based on a genetic algorithm(GA) to solve this problem. We applied GA to regression splines, a nonparametric regression method, to estimate functional forms between variables. Using several simulated and real data, our technique is shown to outperform traditional nonparametric methods such as smoothing splines and neural networks.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.215-219
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• 2005

We consider penalized likelihood regression with exponential family responses. Parallel to recent development in Gaussian regression, the fast computation through asymptotically efficient low-dimensional approximations is explored, yielding algorithm that scales much better than the O($n^3$) algorithm for the exact solution. Also customizations of the direct cross-validation strategy for smoothing parameter selection in various distribution families are explored and evaluated.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.7-10
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• 1991

In a text-to-speech system, sound units (phonemes, words, or phrases, etc.) can be concatenated together to produce required utterance. The quality of the resulting speech is dependent on factors including the phonological/prosodic contour, the quality of basic concatenation units, and how well the units join together. Thus although the quality of each basic sound unit is high, if occur the discontinuity in the concatenation part then the quality of synthesis speech is decrease. To solve this problem, a smoothing operation should be carried out in concatenation parts. But a major problem is that, as yet, no method of parameter smoothing is available for joining the segment together. Thus in this paper, we proposed a new aigorithm that smoothing the unnatural discountinuous parts which can be occured in speech waveform editing. This algorithm used the exponential smoothing method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.6
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• pp.545-556
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• 2016

JPDAF is a method of updating target's state estimation by using posterior probability that measurements are originated from existing target in multi-target tracking. In this paper, we propose a multi-target tracking algorithm for falling cluster bombs separated from a mother bomb based on JPDAS method which is obtained by applying fixed-interval smoothing technique to JPDAF. The performance of JPDAF and JPDAS multi-target tracking algorithm is compared by observing the average of the difference between targets' state estimations obtained from 100 independent executions of two algorithms and targets' true states. Based on this, results of simulations for a radar tracking problem that show proposed JPDAS has better tracking performance than JPDAF is presented.

• Journal of Korea Game Society
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• v.7 no.2
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• pp.33-40
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• 2007

Smoothing is a transmission plan where variable rate video data is converted to a constant bit rate stream. Among them are CBA, MCBA, MVBA and others. However, these smoothing algorithms produce a transmission plan where extra bandwidth in server is not considered. This may cause difficulty in providing videos to many clients in a server. In this paper, we propose the smoothing algorithm with monotonic transmission rate increase and abrupt transmission rate decrease, in order to provide videos to as many clients in server with limited network bandwidth as possible. In order to show the performance of our proposed algorithm, various evaluation factors were used such as the maximum number of clients, the average number of clients, and so on. Experiments demonstrated that the proposed algorithm outperformed other algorithms in evaluation factors such as the maximum number of clients and the average number of clients.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.11-17
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• 2023

Smoothing is to creating a transmission plan consisting of sections of frames that can be sent at the same transmission rate for compressed and stored video data. Various algorithms have been studied for the smoothing to minimize the number of transmission rate changes, the number of transmission rate changes, and the amount of transmission rate increase. This study evaluates the performance of a smoothing algorithm that minimizes the increase in transmission rates and maximizes the increase in transmission rates when the transmission rate is required to maximize the excess bandwidth to be secured by the server in an environment with limited server bandwidth. The available transmission rates and buffer sizes available in the server are set in various ways and evaluated by the number of fps changes, the minimum fps, the average fps, and fps variability. As a result of the comparison, the proposed algorithm showed excellent average fps and fps variability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.75-79
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• 1998

It is well known that traveling salesman problem (for short, TSP) is one of mot important problems for optimization, and almost all optimization problems result in TSP. This paper describes on an effective solution of TSP using genetic algorithm. The features of our method are summarized as follows : (1) By using division and unification method, a large problem is replaced with some small ones. (2) Smoothing method proposed in this paper enables us to obtain a fine approximate solution globally. Accordingly, demerits caused by division and unification method are decreased. (3) Parallel operation is available because all divided problems are independent of each other.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.2
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• pp.171-179
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• 2010

A fundamental technology of UGV(Unmanned Ground Vehicle) to perform a given mission with success in various environment is a path planning method which generates a safe and optimal path to the goal. In this paper, we suggest a local path-planning method of UGV based on the binary map using world model data which is gathered from terrain perception sensors. In specially, we present three core algorithms such as shortest path computation algorithm, path optimization algorithm and path smoothing algorithm those are used in the each composition module of LPP component. A simulation is conducted with M&S(Modeling & Simulation) system in order to verify the performance of each core algorithm and the performance of LPP component with scenarios.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.6
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• pp.71-78
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• 1997

In this paper, a new neural network training algorithm will be devised for function approximator with good generalization characteristics and tested with the time series prediction problem using santaFe competition data sets. To enhance the generalization ability a constraint term of hidden neuraon activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. A hybrid learning algorithm of the error-back propagation and Hebbian learning algorithm with weight decay constraint will be naturally developed by the steepest decent algorithm minimizing the proposed cost function without much increase of computational requriements.