• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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• pp.45-49
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• 2022

In this paper, we propose a method for estimating the error of the Ultra-Wideband (UWB) distance measurement using the channel impulse response (CIR) of the UWB signal based on machine learning. Due to the recent demand for indoor location-based services, wireless signal-based localization technologies are being studied, such as UWB, Wi-Fi, and Bluetooth. The constructive obstacles constituting the indoor environment make the distance measurement of UWB inaccurate, which lowers the indoor localization accuracy. Therefore, we apply machine learning to learn the characteristics of UWB signals and estimate the error of UWB distance measurements. In addition, the performance of the proposed algorithm is analyzed through experiments in an indoor environment composed of various walls.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.666-676
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• 1992

In this paper, algorithm and testable circuit to find all PSF(Pattern Sensitive Fault ) occured in RAM were proposed. Conventional test circuit and algorithm took much time in testing because consecutive test for RAM cells or f-dimensional memory struciure was not employed. In this paper, methodology for parallel RAM-testing was proposed by compensating additional circuit for test to conventional RAM circuit. Additional circuits are parallel comparator, error detector, group selector circuit and a modified decoder used for parallel testing. And also, the constructive method of Eulerian path to obtain efficient test pattern was performed. Consequently, If algorithm proposed in this paper Is used, the same operations as 32sxwor4 lines will be needed to test b x w=n matrix RAM. Circuit simulation was performerd, and 10 bits x :If words testable RAM was designed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.65-74
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• 2009

In optimizing the neural network structure, there are two methods of the pruning scheme and the constructive scheme. In this paper we use the pruning scheme to optimize neural network structure, and the genetic algorithm to find out its optimum node pruning. In the conventional researches, the input and hidden layers were optimized separately. On the contrary we attempted to optimize the two layers simultaneously by encoding two layers in a chromosome. The offspring networks inherit the weights from the parent. For teaming, we used the existing error back-propagation algorithm. In our experiment with various databases from UCI Machine Learning Repository, we could get the optimal performance when the network size was reduced by about $8{\sim}25%$. As a result of t-test the proposed method was shown better performance, compared with other pruning and construction methods through the cross-validation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.203-209
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• 2019

We will be establish the new $q(1{\leq}q{\leq}p-1)$ stores of firm $F_B$ to gain pop/(p+q) over rival firm $F_A$ that has already operate with p stores in a city of population pop. Han proposes inclusion-exclusion algorithm(IEA) that searches maximal pop top 5 location and select the maximum location take account of locate variation with increasing of $q=1,2,{\cdots},p-1$. This paper reduced the orignal graph into partial graph initially and search only q=1 node continually reduced in accordance with increasing $q=1,2,{\cdots},p-1$. If the final result is shown in the case of steel customer between q, the q locations farther separate in order to improve of solution. For the eleven experimental data, this algorithm is a relative simplicity and more optimal solution than Han's IEA.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.55-65
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• 2004

When a neural network is used to solve a given problem it is necessary to match the complexity of the network to that of the problem because the complexity of the network significantly affects its learning capability and generalization performance. Thus, it is desirable to have an algorithm that can find appropriate network structures in a self-organizing way. This paper proposes algorithms which automatically organize feed forward multi-layer neural networks with sigmoid hidden neurons for given problems. Using both constructive procedures and pruning procedures, the proposed algorithms try to find the near optimal network, which is compact and shows good generalization performance. The performances of the proposed algorithms are tested on four function regression problems. The results demonstrate that our algorithms successfully generate near-optimal networks in comparison with the previous method and the neural networks of fixed topology.

• Genomics & Informatics
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• v.14 no.4
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.6
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• pp.15-24
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• 2000

This paper presents an output feedback robust H$\infty$ control problem for a class of uncertain nonlinear systems, which can be represented by an fuzzy dynamic model. The nonlinear system is represented by Takagi-Sugeno fuzzy model, and the control design is carried out on the basis of the fuzzy model. Using a single quadratic Lyapunov function, the globally exponential stability and disturance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust H$\infty$ controllers are given in terms of linear matrix inequalities(LMIs). Constructive algorithm for design of robust H$\infty$ controller is also developed. The resulting controller is nonlinear and automatically tuned based on fuzzy operation.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1053-1056
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• 2008

An application of CSG (Constructive Solid Geometry) modeling technique in Machining Simulation is introduced in this paper. The current CSG model is based on z-buffer CSG Rendering Algorithm. In order to build a CSG model, frame buffers of VGA (Video Graphic Accelerator) should be used in term of color buffer, depth buffer, and stencil buffer. In addition to using CSG model in machine simulation Stock and Cutter Swept Surface (CSS) should be solid. Method to create a solid Cuboid stock and Ball-end mill CSS are included in the present paper. Boolean operations are used to produce the after-cut part, especially the Difference operation between Stock and CSS as the cutter remove materials form stock. Finally, a small program called MaSim which simulates one simple cut using this method was created.

• Journal of the Optical Society of Korea
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• v.9 no.4
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• pp.162-168
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• 2005

Extraordinary phenomena related to the transmission of light via metallic films with subwavelength holes and grooves are known to be due to resonant excitation and interference of surface waves. These waves make various surface structures to have optically effective responses. Further, a related study subject involves the control of light transmitted from a single hole or slit by surrounding it with diffractive structures. This paper reports on the effects of controlling light with a periodic groove structure with Fresnel-type chirping. In Fresnel-type chirping, diffracted surface waves are coherently converged into a focus, and it is designed considering the conditions of constructive interference and angular spectrum optimization under the assumption that the surface waves are composite diffracted evanescent waves with a well-defined in-plane wavenumber. The focusing ability of the chirped periodic structures is confirmed experimentally by two-beam attenuated total reflection coupling. Critical factors for achieving subwavelength foci and bounds on size of focal spots are discussed in terms of the simulation, which uses the FDTD algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.144-154
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• 2003

In this paper we present Logical Evolution method which is a new teaming algorithm for the concepts expressed as binary logic function. We try to solve some problems of Inductive Learning algorithms through Logical Evolution. First, to be less affected from limited prior knowledge, it generates features using the gained informations during learning process and learns the concepts with these features. Second, the teaming is done using not the whole example set but the individual example, so even if new problem or new input-output variables are given, it can use the previously generated features. In some cases these old features can make the teaming process more efficient. Logical Evolution method consists of 5 operations which are selected and performed by the logical evaluation procedure for feature generation and learning process. To evaluate the performance of the present algorithm, we make experiments on MONK data set and a newly defined problem.