• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Smart Structures and Systems
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• v.16 no.6
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• pp.1003-1021
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• 2015

MR dampers have been proposed for the control of cable vibration of cable-stayed bridge in recent years due to their high performance and low energy consumption. However, the highly nonlinear feature of MR dampers makes them difficult to be designed with efficient semi-active control algorithms. Simulation study has previously been carried out on the cable-MR damper system using a semi-active control algorithm derived based on the universal design curve of dampers and a bilinear mechanical model of the MR damper. This paper aims to verify the effectiveness of the MR damper for mitigating cable vibration through a full-scale experimental test, using the same semi-active control strategy as in the simulation study. A long stay cable fabricated for a real bridge was set-up with the MR damper installed. The cable was excited under both free and forced vibrations. Different test scenarios were considered where the MR damper was tuned as passive damper with minimum or maximum input current, or the input current of the damper was changed according to the proposed semi-active control algorithm. The effectiveness of the MR damper for controlling the cable vibration was assessed through computing the damping ratio of the cable for free vibration and the root mean square value of acceleration of the cable for forced vibration.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.9-18
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• 2005

In order to systematically and visually understand well-known but qualitative and rotatively complicated relationships between synoptic fields in the BAIU season and heavy rainfall events in Japan, these synoptic fields were classified using the Self-Organizing Map (SOM) algorithm. This algorithm can convert complex nonlinear features into simple two-dimensional relationships, and was followed by the application of the clustering techniques of the U-matrix and the K-means. It was assumed that the meteorological field patterns be simply expressed by the spatial distribution of wind components at the 850 hPa level and Precipitable Water (PW) in the southwestern area including Kyushu in Japan. Consequently, the synoptic fields could be divided into eight kinds of patterns (clusters). One of the clusters has the notable spatial feature represented by high PW accompanied by strong wind components known as Low-Level Jet (LLJ). The features of this cluster indicate a typical meteorological field pattern that frequently causes disastrous heavy rainfall in Kyushu in the rainy season. From these results, the SOM technique may be an effective tool for the classification of complicated non-linear synoptic fields.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.202-209
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• 2008

Application of wind turbine generator system (WTGS) needs researches for performance prediction, pitch control, and optimal operation method. Recently a new type WTGS is developed and under testing. The notable feature of this WTGS is that it consists of two rotor systems positioned horizontally at upwind and downwind locations, and a generator installed vertically inside the tower. In this paper, a nonlinear simulation software developed for the performance prediction of the Dual Rotor WTGS and testing of various control algorithm is introduced. This software is hybrid in the sense that FORTRAN is extensively used for the purpose of computation and Matlab/Simulink provides a user friendly GUI-like environment.

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1193-1208
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• 2014

A simulation of failures on responsible elements is only one form of the extreme structural behavior analysis. By understanding the dynamic behavior in incidental situations, it is possible to make a special structural design from the point of the largest axial force, stress and redundancy. The numerical realization of one such simulation analysis was performed using FEM in this paper. The boundary parameters of transient analysis, such as overall structural damping coefficient, load accelerations, time of load fall and internal forces in the responsible structural elements, were determined on the basis of the dynamic experimental parameters. The structure eigenfrequencies were determined in modal analysis. In the study, the basic incidental models were set. The models were identified by many years of monitoring incidental situations and the most frequent human errors in work with heavy structures. The combined load models of structure are defined in the paper since the incidents simply arise as consequences of cumulative errors and failures. A feature of a combined model is that the single incident causes the next incident (consecutive timing) as well as that other simple dynamic actions are simultaneous. The structure was observed in three typical load positions taken from the crane passport (range-load). The obtained dynamic responses indicate the degree of structural sensitivity depending on the character of incident. The dynamic coefficient KD was adopted as a parameter for the evaluation of structural sensitivity.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.835-841
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• 2014

In this paper we propose a swing-up strategy for a single inverted pendulum. The proposed method has a feature whereby can handle the input and output constraint of a pendulum in a systematic way. For the swing-up of a pendulum, we adopt a 2-DOF control structure that combines the feedforward and feedback control. In order to generate the swing-up feedforward trajectories that satisfy the input and output constraint, we formulate the problem of generating feedforward trajectories as a nonlinear optimal control problem subject to constraints. We illustrate that the proposed method is more flexible than the existing method and provides great freedom in choosing the actuator of the inverted pendulum. Through an experiment, we show that the proposed method can swing a pendulum upward effectively while satisfying all the imposed constraints.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.228-234
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• 2004

In this paper, we proposed the compensation technique that overcomes the limitations of the conventional approaches through summing up the bias terms between world's codebook and individual codebook vectors of feature parameters. But, mean compensation without condition can bring higher false acceptance. Therefore, the proposed technique compensates the channel mis-match condition by weighted bias sum using nonlinear function regarding to the distortion between speech and silence. The simulation results show that the FRR (flase reject rate) is decreased 14.95% when the proposed algorithm was applied.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.69.2-69.2
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• 2014

We present space-based near-IR (NIR) and optical photometry of globular clusters (GCs) of 16 early-type galaxies in the Fornax and Virgo Clusters. The NIR imaging data for the nearby galaxies was acquired with the IR Channel of the Wide Field Camera 3 (WFC3/IR) in the F110W ($J_{110}$) and F160W ($H_{160}$) bandpasses. We introduce the full sample of our WFC3/IR program, describe data reductions and photometric measurements including GC candidate selection criteria, and then show selected GCs' color-magnitude diagrams. The tilted features in the diagrams related to the morphological types of host galaxies are discussed in the context of galaxy formation and evolution histories. Combining F475W ($g_{475}$) and F850LP ($z_{850}$) data taken from the Advanced Camera for Surveys Virgo and Fornax Cluster Surveys with our NIR data, we investigate the bimodality in optical-NIR color distribution and the nonlinear feature of the optical-NIR color relation as a function of optical color for these extragalactic GC systems.