• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.164-172
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• 2004

This paper presents the comparison results of some kinds of control method in circumstance of eccentric load. The plant to be controlled is a computed tomography(CT) driven by a permanent magnet synchronous motor. In a CT system, many units are attached on the rotationally part of a gantry such as x-ray tube, detector, heat exchanger, and data acquisition unit etc. Therefore keeping many components to balance which have different weight is not easy; this is inescapable problem in the all CT systems. To solve this problem against eccentric load, some kinds of control method have been compared and analysed by using protype CT. From the experimental results it is vilified that the CT drive system with model reference control method indicates higher speed regulation ability regardless of variable eccentric weight and uncertain position, and also in the limit condition of constant eccentric weight and fixed position, the compensation method with sinusoidal form is a strong candidate in view of speed ripple reduction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• Geomechanics and Engineering
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• v.11 no.1
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• pp.59-76
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• 2016

Penetration problems in geomechanics are common. Usually the soil is heavily disturbed around the penetrating bodies and large deformations and distortions can occur. The simulation of the installation of displacement piles is a good example of the interest of these types of problems for geomechanics. In this paper the Material Point Method is used to overcome the difficulties associated with the simulations of problems involving large deformation and full displacement type penetration. Recent modifications of the Material Point Method known as Generalized Interpolation Material Point and the Convected Particle Domain Interpolation are also used and evaluated in some of the examples. Herein a footing submitted to large settlements is presented and simulated, together with the processes associated to a driven pile under undrained conditions. The displacements of the soil surrounding the pile are compared with those obtained by the Small Strain Path Method. In addition, the Modified Cam Clay model is implemented in a code of MPM and used to simulate the process of driving a pile in dry sand. Good and rather encouraging agreement is found between compared data.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.723-728
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• 2022

This study is a method for evaluating and quantifying driver's dangerous driving behavior. The quantification method calculates various driving information in real time after starting the vehicle operation such as the time that the vehicle has been continuously driven without a break, overspeed, rapid acceleration, and overspeed driving time. These quantified risk of driving behavior values can be individually provided as a safe driving index, or can be used to objectify the evaluation of a group of drivers on roads, or vehicle groups such as cargo/bus/passenger vehicles.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1075-1078
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• 2015

Three kinds of typical practices to reflect the values of Agile Development Methodology were selected from a previous study. Those were Communicate using Web 2.0 collaboration tools, test-driven development (TDD, Test-Driven Development) method is adopted, and refactoring. In this study, we set up a hypothesis that the adoption of TDD project will make user satisfaction is higher. Select 100 sample projects from SourceForge(sourceforge.net), the most popular open source hosting site, the criteria is we can be determined whether operate in the project (developer least 7 people, bugs can occur more than 100, created the project since 2000). To determine whether the use of automated development tools xUnit of TDD through the CVS and SVN log analysis. Using data from the FLOSSmole and to evaluate the user experience of the project. User satisfaction of each project Rating, bug fix cycle, downloads and pageviews. Through this study, correlates of whether TDD adoption and user satisfaction, we will suggest a reflected the Agile practices new open source development methodology. As a result, it contributes to increase the maturity of the open source community.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.156-166
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• 2019

For this study, a survey was conducted with the employees of small and medium-size enterprises (SMEs) located in Seoul and in Gyeonggi province. In the end, a total of 328 valid questionnaires were received and used in the analysis. The data of this study were analyzed using two statistics programs: SPSS Statistics 22.0 and AMOS 22.0. As for the method to verify the hypothesis, we used a structural equation model. The key findings of this study are as follows. First, the results of correlation analysis between management strategy factors, innovation activities, and business performance showed that the factors that were at a higher level of correlation were the technology differentiation strategies, marketing differentiation strategies, and the cost-driven strategy. Second, the strategic management factors that influenced innovation activities were in the following order: marketing differentiation, technology differentiation, and the cost-driven strategy, while the valid factors that affected business performance with significance were only the marketing differentiation strategy and the cost-driven strategy. Third, while the analysis showed that the technology differentiation strategy did not have a direct effect on business performance, it was shown that the relationship between the technology differentiation strategy and business performance was completely mediated by innovation activities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.356-367
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• 2014

A robotic sapatial augmented reality (RSAR) system, which combines robotic components with projector-based AR technique, is unique in its ability to expand the user interaction area by dynamically changing the position and orientation of a projector-camera unit (PCU). For a moving PCU mounted on a conventional robotic device, we can compute its extrinsic parameters using a robot kinematics method assuming a link and joint geometry is available. In a RSAR system based on user-created robot (UCR), however, it is difficult to calibrate or measure the geometric configuration, which limits to apply a conventional kinematics method. In this paper, we propose a data-driven kinematics control method for a UCR-based RSAR system. The proposed method utilized a pre-sampled data set of camera calibration acquired at sufficient instances of kinematics configurations in fixed joint domains. Then, the sampled set is compactly represented as a set of B-spline surfaces. The proposed method have merits in two folds. First, it does not require any kinematics model such as a link length or joint orientation. Secondly, the computation is simple since it just evaluates a several polynomials rather than relying on Jacobian computation. We describe the proposed method and demonstrates the results for an experimental RSAR system with a PCU on a simple pan-tilt arm.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.6
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• pp.542-553
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• 2020

Virtual world and simulation need large scale map rendering. However, rendering too many vertices is a computationally complex and time-consuming process. Some game development companies have developed 3D LOD objects for high-speed rendering based on distance between camera and 3D object. Terrain physics simulation researchers need a way to recognize the original object shape from 3D LOD objects. In this paper, we proposed simply automatic LOD framework using point cloud data (PCD). This PCD was created using a 6-direct orthographic ray. Various experiments are performed to validate the effectiveness of the proposed method. We hope the proposed automatic LOD generation framework can play an important role in game development and terrain physic simulation.