• Bulletin of the Korean Mathematical Society
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• v.55 no.5
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• pp.1483-1490
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• 2018

In this note, it is proven that the slice Bergman kernels for some axially symmetric slice domains are zero-free by a simple method.

• Honam Mathematical Journal
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• v.43 no.4
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• pp.691-700
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• 2021

In this paper, we compute directly the Hankel matrix representation of the Hankel operator on the Hardy space of the unit disc without using any classical kernel functions with respect to special orthonormal bases for the Hardy space and its orthogonal complement. This gives an elementary proof for the formula.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• Journal of Korea Game Society
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• v.1 no.1
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• pp.61-67
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• 2001

This paper describes a program which learns good strategies for two-poison, deterministic, zero-sum board games of perfect information. The program learns by simply playing the game against either a human or computer opponent. The results of the program's teaming of a lot of games are reported. The program consists of search kernel and a move generator module. Only the move generator is modified to reflect the rules of the game to be played. The kernel uses a temporal difference procedure combined with a backpropagation neural network to team good evaluation functions for the game being played. Central to the performance of the program is the search procedure. This is a the capture tree search used in most successful janggi playing programs. It is based on the idea of using search to correct errors in evaluations of positions. This procedure is described, analyzed, tested, and implemented in the game-teaming program. Both the test results and the performance of the program confirm the results of the analysis which indicate that search improves game playing performance for sufficiently accurate evaluation functions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1528-1534
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• 2007

This paper describes the environment configuration for the development of an embedded gadget system and the architecture and operation of Linux kernel for embedded system applications, which is used for a gadget. It shows and analyzes the operations of Linux kernel to investigate the functions and components for new intelligent embedded gadget systems. The requested functions and operations adaptable for the new intelligent embedded system will be applicable to develop a new small size operating system that supports intelligent operations for the embedded gadget system used for intelligent personal information services. We configure the environment of development for an embedded gadget system and its application.

• Steel and Composite Structures
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• v.53 no.1
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• pp.1-27
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• 2024

This work presents the effectiveness of differential quadrature shape functions (i.e., Lagrange interpolation polynomial, Cardinal sine function, Delta Lagrange kernel and Regularized Shannon kernel) in the solution of nonlinear vibration of multilayers piezoelectric plates with nonlinear elastic support. A piezoelectric composite laminated plate is rested on nonlinear Winkler and Visco-Pasternak elastic foundations problems. Based on 3D elasticity theory and piezoelectricity, the governing equations of motion are derived. Differential quadrature methods based on four shape functions are presented as numerical techniques for solving this problem. The perturbation method is implemented to solve the obtained nonlinear eigenvalue problem. A MATLAB code is written for each technique for solving this problem and extract the numerical results. To validate these methods, the computed results are we compare with the previous exact results. In addition, parametric analyses are offered to investigate the influence of length to thickness ratio, elastic foundation parameters, various boundary conditions, and piezoelectric layers thickness on the natural frequencies and mode shapes. Consequently, it is discovered that the obtained results via the proposed schemes can be applied in structural health monitoring.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• Honam Mathematical Journal
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• v.46 no.2
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• pp.313-334
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• 2024

In the current study, our aim is to define new generalized extended beta and hypergeometric types of functions. Next, we methodically determine several integral representations, Mellin transforms, summation formulas, and recurrence relations. Moreover, we provide log-convexity, Turán type inequality for the generalized extended beta function and differentiation formulas, transformation formulas, differential and difference relations for the generalized extended hypergeometric type functions. Also, we additionally suggest a generating function. Further, we provide the generalized extended beta distribution by making use of the generalized extended beta function as an application to statistics and obtaining variance, coefficient of variation, moment generating function, characteristic function, cumulative distribution function, and cumulative distribution function's complement.

• Journal of IKEEE
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• v.13 no.4
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• pp.1-7
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• 2009

A video segmentation algorithm, which can extract objects even with non-stationary backgrounds, is proposed in this work. The proposed algorithm is composed of three steps. First, we perform an initial segmentation interactively to build the probability density functions of colors per each macro block via kernel density estimation. Then, for each subsequent frame, we construct a coherence strip, which is likely to contain the object contour, by exploiting spatio-temporal correlations. Finally, we perform the segmentation by minimizing an energy function composed of color, coherence, and smoothness terms. Experimental results on various test sequences show that the proposed algorithm provides accurate segmentation results.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.4
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• pp.373-380
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• 1997

In order to implement the real-time operating systems for robot controller, this paper proposes a systematic method for implementing the real-time kernel under the DOS environment. So far, we designed the robot control software and its own operating system simultaneously. Though robot operating systems have simple structure, it allows the developer to have a surplus time and effort to implement complete robot systems. In addition to this, in most cases of this type, operating systems does not support multitasking function, thus, low level hardware interrupts are used for real-time execution. Subsequently, some kinds of real-time tasks are hard to implement under this environment. Nowadays, the operating systems for robot controller requires multitasking functions, intertask communication and task synchronization mechanism, and rigorous real-time responsiveness. Thus, we propose an effective and low costs real-time systems for robot controller satisfying the various real-time characteristics. The proposed real-time systems are verified through real implementation.