• Journal of Astronomy and Space Sciences
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• v.13 no.2
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• pp.67-81
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• 1996

The attitude dynamics of KITSAT-1 are modeled including the gravity gradient stabilization method. We define the operation scenario during the initial attitude stabilization period by means of a magnetorquering control algorithm. The required constraints for the gravity gradient boom deployment are also examined. Attitude dynamics model and control laws are verified by analyzing in-orbit attitude sensor telemetry data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.

• International Journal of Automotive Technology
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• v.3 no.2
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• pp.71-78
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• 2002

A new control method far vision-based autonomous vehicle is proposed to determine navigation direction by analyzing lane information from a camera and to navigate a vehicle. In this paper, characteristic featured data points are extracted from lane images using a lane recognition algorithm. Then the vehicle is controlled using new Levenberg-Marquardt neural network algorithm. To verify the usefulness of the algorithm, another algorithm, which utilizes the geometric relation of a camera and vehicle, is introduced. The second one involves transformation from an image coordinate to a vehicle coordinate, then steering is determined from Ackermann angle. The steering scheme using Ackermann angle is heavily depends on the correct geometric data of a vehicle and a camera. Meanwhile, the proposed neural network algorithm does not need geometric relations and it depends on the driving style of human driver. The proposed method is superior than other referenced neural network algorithms such as conjugate gradient method or gradient decent one in autonomous lateral control .

• Journal of Fisheries and Marine Sciences Education
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• v.20 no.2
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• pp.156-167
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• 2008

Meorological data have been collected to monitor the wetland area in Goheung bay since 2003 and four intensive observations were conducted to study effects of the atmospheric turbulence on the energy budget and the ecological changes. We improved an algorithm to estimate the sensible heat flux with routine data. The sensible heat flux estimated by gradient method was in good agreement with that measured by precision instruments such as surface layer scintillometer and ultrasonic anemometer. Diurnal variations of sensible heat flux showed analogous tendency to those of temperature gradient. When the vertical wind shear of horizontal wind components was weak, even though temperature gradient was strong, the gradient method underestimated the sensible heat flux. A compensation for the cloud will make this gradient method be a helpful tool to monitor the ecosystem without expensive instruments except for weak wind shear and temperature gradient.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.82-88
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• 2005

A compensation scheme for a class of linearly parameterized systems is presented. The compensator consists of a typical linearizing control and an adaptive observer with integration type update law, which is based on Speed Gradient (SG) algorithm.. Instead of the intermediate functions of the compensation schemes suggested by other researchers, the proposed compensator is designed with some design functions which guarantee the growth, convexity, attainability, and pseudo gradient conditions in the update law. The scheme achieves the asymptotic stability of the tracking error and the boundedness of the estimation errors. A numerical example is given to demonstrate the validity of the proposed design.

• Annual Conference of KIPS
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• 2016.10a
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• pp.733-734
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• 2016

This study proposes a novel approach for ground segmentation of 3D point cloud. We combine two techniques: gradient threshold segmentation, and mean height evaluation. Acquired 3D point cloud is represented as a graph data structures by exploiting the structure of 2D reference image. The ground parts nearing the position of the sensor are segmented based on gradient threshold technique. For sparse regions, we separate the ground and nonground by using a technique called mean height evaluation. The main contribution of this study is a new ground segmentation algorithm which works well with 3D point clouds from various environments. The processing time is acceptable and it allows the algorithm running in real time.

• Steel and Composite Structures
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• v.46 no.2
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• Structural Engineering and Mechanics
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• v.88 no.4
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• pp.301-309
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• 2023

In this paper, a new modified conjugate gradient (MCG) method is presented which is based on a new gradient regularizer, and this method is used to identify the dynamic load on airfoil structure without and with considering random structure parameters. First of all, the newly proposed algorithm is proved to be efficient and convergent through the rigorous mathematics theory and the numerical results of determinate dynamic load identification. Secondly, using the perturbation method, we transform uncertain inverse problem about force reconstruction into determinate load identification problem. Lastly, the statistical characteristics of identified load are evaluated by statistical methods. Especially, this newly proposed approach has successfully solved determinate and uncertain inverse problems about dynamic load identification. Numerical simulations validate that the newly developed method in this paper is feasible and stable in solving load identification problems without and with considering random structure parameters. Additionally, it also shows that most of the observation error of the proposed algorithm in solving dynamic load identification of deterministic and random structure is respectively within 11.13%, 20%.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.505-514
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• 2020

Since a large amount of training data is typically needed to train Deep Neural Networks (DNNs), a parallel training approach is required to train the DNNs. The Stochastic Gradient Descent (SGD) algorithm is one of the most widely used methods to train the DNNs. However, since the SGD is an inherently sequential process, it requires some sort of approximation schemes to parallelize the SGD algorithm. In this paper, we review various efforts on parallelizing the SGD algorithm, and analyze the computational overhead, communication overhead, and the effects of the approximations.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.858-863
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• 2005

Sensor network is subject to novel problems and constraints because it is composed of thousands of tiny devices with very limited resources. The large number of motes in a sensor network means that there will be some failing nodes owing to the lack of battery in sensor nodes. Therefore, it is imperative to save the energy as much as possible. In this work, we propose energy efficient routing algorithm which is based on directed diffusion scheme. In the proposed scheme, some overloads required for reinforcing the gradient path can be effectively eliminated. Furthermore, in order to verify the usefulness of the proposed algorithm, several simulations are executed.