• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.24-30
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• 2016

The accuracy of small and low-cost CCD cameras is insufficient to provide data for precisely tracking unmanned aerial vehicles (UAVs). This study shows how a quad rotor UAV can hover on a human targeted tracking object by using data from a CCD camera rather than imprecise GPS data. To realize this, quadcopter UAVs need to recognize their position and posture in known environments as well as unknown environments. Moreover, it is necessary for their localization to occur naturally. It is desirable for UAVs to estimate their position by solving uncertainty for quadcopter UAV hovering, as this is one of the most important problems. In this paper, we describe a method for determining the altitude of a quadcopter UAV using image information of a moving object like a walking human. This method combines the observed position from GPS sensors and the estimated position from images captured by a fixed camera to localize a UAV. Using the a priori known path of a quadcopter UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated quadcopter UAV's altitude. Since the equations are based on the geometric constraint equation, measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the quadcopter UAV. The Kalman filter scheme is applied for this method. Its performance is verified by a computer simulation and experiments.

• Journal of vocational education research
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• v.36 no.2
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• pp.23-50
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• 2017

The purpose of this study was to determine the hierarchical linear relationship among receptivity to organizational change, individual variables of professors and organizational variables in junior colleges. The population for this study was 12,920 professors in 139 junior colleges. Using random sampling method considering subject, 800 professors in 40 colleges were sampled for this study. The data were collected from May 26 to June 13. A total of 445 out of 800 questionnaires were returned of which 441 of 40 junior colleges were used for analysis after data cleaning. These data were analyzed by both descriptive statistics and One-way ANOVA with Random Effects, Ranmdom-Coefficients Regression Model, and Intercepts-and Slopes-as-Outcomes Model of hierarchical linear model(HLM). All data analysis was accomplished using the SPSS 20.0 for windows program and the HLM 6.0 for windows program. An alpha level of 0.05 was established priori for determining the significance. The findings of the study were as follows: First, the level of receptivity to organizational change of professions in junior college was 3.94. Second, 56.5% of total variance in receptivity to organizational change was individual level variance. 43.5% of total variance in receptivity to organizational change was organizational level variance. Third, personal valence about organizational change, psychological ownership, experience of assignment, years of service and job security had positive effects on receptivity to organizational change while years of service had negative effects on receptivity to organizational change. The effect of personal valence about organizational change was highest, and the effect of job security was lowest. Fourth, degree of organizational change, participative decision-making, group culture and accessibility of information related to organizational change had positive effects on receptivity to organizational change. The effect of degree of organizational change was highest, and the effect of accessibility of information related to organizational change was lowest.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.252-266
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• 2006

Many problems in computer vision are mainly based on mathematical models. Their optimal solutions can be found by estimating the parameters of each model. However, provided an input data set is involved outliers which are relative]V larger than normal noises, they lead to incorrect results. RANSAC is a representative robust algorithm which is used to resolve the problem. One major problem with RANSAC is that it needs priori knowledge(i.e. a percentage of outliers) of the distribution of data. To solve this problem, we propose a FRANSAC algorithm which improves the rejection rate of outliers and the accuracy of solutions. This is peformed by categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification at each iteration and sampling in only good sample set. In the experimental results, we show that the performance of the proposed algorithm when it is applied to the linear regression and the calculation of a homography.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.7-17
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• 2009

The development of wireless communication MAC layer is usually released as SoC which is a combination in hardware and software. In this system development environment, an environment for software development and verification is necessary because the hardware development takes a lot of time priori to completion. In integrated development of hardware and software, simulation environment of hardware and software provided by hardware modeling using HDL at RTL and ISS respectively. By increasing the development complexity of system, ESL design modeling systems at higher abstraction level than RTL has already prompted. The ESL design is divided untime model and time model. This paper present design and implementation of MCU for untime model simulation, not time model. Proposed MCU can optimize the system at early step of system development and move up the development completion time by verifying the system function easily and rapidly than part required exact time in untime model. In this paper, we present an MCU module based on SystemC and UC/OS-II Module providing real-time operate system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.179-186
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• 2008

In this paper we analyse the effects of Bayesian algorithm in teaming class imbalance problems and compare the performance evaluation methods. The teaming performance of the Bayesian algorithm is evaluated over the class imbalance problems generated by priori data distribution, imbalance data rate and discrimination complexity. The experimental results are calculated by the AUC(Area Under the Curve) values of both ROC(Receiver Operator Characteristic) and PR(Precision-Recall) evaluation measures and compared according to imbalance data rate and discrimination complexity. In comparison and analysis, the Bayesian algorithm suffers from the imbalance rate, as the same result in the reported researches, and the data overlapping caused by discrimination complexity is the another factor that hampers the learning performance. As the discrimination complexity and class imbalance rate of the problems increase, the learning performance of the AUC of a PR measure is much more variant than that of the AUC of a ROC measure. But the performances of both measures are similar with the low discrimination complexity and class imbalance rate of the problems. The experimental results show 4hat the AUC of a PR measure is more proper in evaluating the learning of class imbalance problem and furthermore gets the benefit in designing the optimal learning model considering a misclassification cost.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.11-20
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• 2019

Numerous enterprises, organizations and individual users are exposed to large DDoS (Distributed Denial of Service) attacks. DDoS attacks are performed through a BotNet, which is composed of a number of computers infected with a malware, e.g., zombie PCs and a special computer that controls the zombie PCs within a hierarchical chain of a command system. In order to detect a malware, a malware detection software or a vaccine program must identify the malware signature through an in-depth analysis, and these signatures need to be updated in priori. This is time consuming and costly. In this paper, we propose a botnet detection scheme that does not require a periodic signature update using an artificial neural network model. The proposed scheme exploits Word2Vec and accelerated hierarchical density-based clustering. Botnet detection performance of the proposed method was evaluated using the CTU-13 dataset. The experimental result shows that the detection rate is 99.9%, which outperforms the conventional method.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.135-150
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• 2002

The basic assumption of analytical Dynamic Traffic Assignment models is that traffic demand and network conditions are known as a priori and unchanging during the whole planning horizon. This assumption may not be realistic in the practical traffic situation because traffic demand and network conditions nay vary from time to time. The rolling horizon implementation recognizes a fact : The Prediction of origin-destination(OD) matrices and network conditions is usually more accurate in a short period of time, while further into the whole horizon there exists a substantial uncertainty. In the rolling horizon implementation, therefore, rather than assuming time-dependent OD matrices and network conditions are known at the beginning of the horizon, it is assumed that the deterministic information of OD and traffic conditions for a short period are possessed, whereas information beyond this short period will not be available until the time rolls forward. This paper introduces rolling horizon implementation to enable a multi-class analytical DTA model to respond operationally to dynamic variations of both traffic demand and network conditions. In the paper, implementation procedure is discussed in detail, and practical solutions for some raised issues of 1) unfinished trips and 2) rerouting strategy of these trips, are proposed. Computational examples and results are presented and analyzed.

• Geophysics and Geophysical Exploration
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• v.15 no.2
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• pp.66-74
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• 2012

This paper presents development of a three-dimensional marine controlled-source electromagnetic (mCSEM) modeling algorithm and its application to a salt and reservoir model to examine detectability of mCSEM for a reservoir under complex subsurface structures. The algorithm is based on the finite difference method, and employs the secondary field formulation for an accurate and fast calculation of modeling responses. The algorithm is verified for a two-layer model by comparing solutions not only with analytic solutions but also with those from other 3D modeling algorithm. We calculate and analyze electric and magnetic fields and their normalized responses for a salt and reservoir model due to three sources located at boundaries between a salt, a reservoir, and background. Numbers and positions of resistive anomalies are informed by normalized responses for three sources, and types of resistive anomalies can be informed when there is a priori information about a salt by seismic exploration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.9-18
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• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.438-444
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• 2004

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Since the equations are based or the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied for this method. its performance is verified by the computer simulation and the experiment.