• Steel and Composite Structures
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• 제35권1호
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• pp.29-41
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• 2020

Cross-beams of modern through truss bridges are connected to truss chord at its nodes and between them. It results in variable rotational end restraint for cross-beams, thus variable bending moment distribution. This feature is captured in three-dimensional modelling of through truss bridge structure. However, for preliminary design or rapid assessment of service load effects such technique of analysis may not be available. So an analytical method of assessment of rotational end restraint for cross-beam of through truss bridges was worked out. Two cases - nodal cross-beam and inter-nodal cross-beam - were analyzed. Flexural and torsional stiffness of truss members, flexural stiffness of deck members and axial stiffness of wind bracing members in the vicinity of the analyzed cross-beam were taken into account. The provision for reduced stiffness of the X-type wind bracing was made. Finally, general formula for assessment of rotational end restraint was given. Rotational end restraints for cross-beams of three railway through truss bridges were assessed basing on the analytical method and the finite element method (three-dimensional beam-element modelling). Results of both methods show good agreement. The analytical method is able to reflect effects of some structural irregularities. On the basis of the obtained results the general values of rotational end restraint for nodal and inter-nodal cross-beams of railway through truss bridges were suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1047-1062
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• 2018

In this paper the problem of massive multiple input multiple output (MIMO) channel estimation with sparsity aware adaptive algorithms for $5^{th}$ generation mobile systems is investigated. These channels are shown to be non-stationary along with being sparse. Non-stationarity is a feature that implies channel taps change with time. Up until now most of the adaptive algorithms that have been presented for channel estimation, have only considered sparsity and very few of them have been tested in non-stationary conditions. Therefore we investigate the performance of several newly proposed sparsity aware algorithms in these conditions and finally propose an enhanced version of RZA-LMS/F algorithm with variable threshold namely VT-RZA-LMS/F. The results show that this algorithm has better performance than all other algorithms for the next generation channel estimation problems, especially when the non-stationarity gets high. Overall, in this paper for the first time, we estimate a non-stationary Rayleigh fading channel with sparsity aware algorithms and show that by increasing non-stationarity, the estimation performance declines.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제27권4A호
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• pp.356-363
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• 2002

Video scene segmentation is fundamental role for content based video analysis and many kinds of scene segmentation schemes have been proposed in previous researches. However, there is a problem, which is to find optimal threshold value according to various kinds of movies and its content because only fixed single threshold value usually used for cut detection. In this paper, we proposed the variable dynamic threshold method, which change the threshold value by a probability distribution of cut detection interval and information of frame feature differences and cut detection interval in previous cut detection is used to determine the next cut detection. For this, we present a cut detection algorithm and a parameter generation method to change the threshold value in runtime. We also show the proposed method, which can minimize fault alarm rate than the existing methods efficiently by experimental results.

• Journal of Korea Game Society
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• 제11권6호
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• pp.95-102
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• 2011

At present, with the rise of smart-phone market supported the Wi-Fi, smart-phone based network content has propagated rapidly. But, network content service is difficult in feature phone and multimedia device not supporting the Wi-Fi or supporting only the bluetooth. This paper was used the zigbee technology supporting low power, low cost, availability, multi channel, broadcasting compared to the bluetooth or Wi-Fi. It will provide a variable network game development by utilizing the zigbee technology. Also, it is provide the development of variable mobile network contents using the zigbee.

• Proceedings of the SAREK Conference
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• 대한설비공학회 2007년도 동계학술발표대회 논문집
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• pp.328-333
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• 2007

The purpose of this study is modelling of system air-conditioner for TRNSYS. System air-conditioner is operated by a variable capacity compressor and accommodated by multiple evaporators. By reason of these feature, realizing performance of system air-conditioner for TRNSYS was incomplete. In this study performance data of system air-conditioner and control logics are used to make system air-conditioner module for TRNSYS. Performance data contains total capacity, power input and capacity index of system air-conditioner. The simulation was carried out in a mode of temperature level control using TRNSYS 16. The simulation shows that the system air-conditioner model operate variable capacity and can compute capacity index and power input of system.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제43권2호
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• Proceedings of the KIEE Conference
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.797-799
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• 1996

This paper presents a simple method for evaluating of voltage stability using the line flow equation. Line flow equations($P_{ij}$, $Q_{ij}$) are comprised of state variable, $V_i$, ${\delta}_i$, $V_j$ and ${\delta}_j$, and line parameter, r and x. Using the feature of polar coordinate, these equations become one equation with two variables, $V_i$ and $V_j$. Moreover, if bus j is slack bus or generator bus, which is specified voltage magnitude, it becomes One equation with one variable $V_i$, that is, may be formulated with the second-order equation for $V_i^2$. Therefore, solutions are obtained with simple computation. Solutions obtained are used for evaluating of voltage stability through sensitivity analysis. Also, considering of reactive power source, method for evaluating the voltage stability is introduced. The proposed method was validated to 2-bus and IEEE 6-bus system.

• Journal of the Korea Institute of Information Security & Cryptology
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• 제15권2호
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• pp.13-22
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• 2005

To improve the anomaly IDS using system calls, this study focuses on Neural Networks Learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm with fuzzy membership function. The back-propagation neural networks and Neuro-Fuzzy technique are applied for anomaly intrusion detection of system calls using Sendmail Data of UNM to demonstrate its aspect of he complexity of time, space and MDL performance.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.617-627
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• 2021

Underwater Acoustic Channels (UAC) have inherent sparse characteristics. The traditional adaptive equalization techniques do not utilize this feature to improve the performance. In this paper we consider the Variable Adaptive Subgradient Projection (V-ASPM) method to derive a new sparse equalization algorithm based on the Minimum Symbol Error Rate (MSER) criterion. Compared with the original MSER algorithm, our proposed scheme adds sparse matrix to the iterative formula, which can assign independent step-sizes to the equalizer taps. How to obtain such proper sparse matrix is also analyzed. On this basis, the selection scheme of the sparse matrix is obtained by combining the variable step-sizes and equalizer sparsity measure. We call the new algorithm Sparse-Control Proportional-MSER (SC-PMSER) equalizer. Finally, the proposed SC-PMSER equalizer is embedded into a turbo receiver, which perform turbo decoding, Digital Phase-Locked Loop (DPLL), time-reversal receiving and multi-reception diversity. Simulation and real-field experimental results show that the proposed algorithm has better performance in convergence speed and Bit Error Rate (BER).

• Advances in nano research
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• 제11권6호
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• pp.621-640
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• 2021

Effect of thickness stretching on mechanical behavior of functionally graded (FG) carbon nanotubes reinforced composite (CNTRC) laminated nanoplates resting on elastic foundation is analyzed in this paper using a novel quasi 3D higher-order shear deformation theory. The key feature of this theoretical formulation is that, in addition to considering the thickness stretching effect, the number of unknowns of the displacement field is reduced to four, and which is more than five in the other models. Single-walled carbon nanotubes (SWCNTs) are the reinforced elements and are distributed with four power-law functions which are, uniform distribution, V-distribution, O-distribution and X-distribution. To cover various boundary conditions, an analytical solution is developed based on Galerkin method to solve the governing equilibrium equations by considering the nonlocal strain gradient theory. A modified two-dimensional variable Winkler elastic foundation is proposed in this study for the first time. A parametric study is executed to determine the influence of the reinforcement patterns, power-law index, nonlocal parameter, length scale parameter, thickness and aspect ratios, elastic foundation, thermal environments, and various boundary conditions on stresses, displacements, buckling loads and frequencies of the CNTRC laminated nanoplate.