• Nuclear Engineering and Technology
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• v.41 no.6
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• pp.813-820
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• 2009

A rapid pneumatic transfer system (PTS) for an instrumental neutron activation analysis (INAA) is developed as an automatic irradiation facility involving the measurement of a short half-life nuclide and a delayed neutron counting system. Three new PTS designs with improved functions were constructed at the HANARO research reactor in 2006. The new system is composed of a manual system and an automatic system for both an INAA and a delayed neutron activation analysis (DNAA). The design and basic conception of a modified PTS are described, and the functions of system operation and control, radiation protection and emissions of radioactive gas are improved. In addition, a form of capsule transportation of these systems is tested. The experimental results pertaining to the irradiation characteristics with variation of the neutron flux and the temperature of the irradiation position with the irradiation time are presented, as is an analysis of the reference material for analytical quality control and uncertainty assessments.

• Nuclear Engineering and Technology
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• v.41 no.9
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• pp.1181-1190
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• 2009

Knowing more about the Local Power Density (LPD) at the hottest part of a nuclear reactor core can provide more important information than knowledge of the LPD at any other position. The LPD at the hottest part needs to be estimated accurately in order to prevent the fuel rod from melting in a nuclear reactor. Support Vector Machines (SVMs) have successfully been applied in classification and regression problems. Therefore, in this paper, the power peaking factor, which is defined as the highest LPD to the average power density in a reactor core, was estimated by SVMs which use numerous measured signals of the reactor coolant system. The SVM models were developed by using a training data set and validated by an independent test data set. The SVM models' uncertainty was analyzed by using 100 sampled training data sets and verification data sets. The prediction intervals were very small, which means that the predicted values were very accurate. The predicted values were then applied to the first fuel cycle of the Yonggwang Nuclear Power Plant Unit 3. The root mean squared error was approximately 0.15%, which is accurate enough for use in LPD monitoring and for core protection that uses LPD estimation.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.617-626
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• 2022

To effectively monitor the variety of distributions of neutron flux, fuel power or temperatures in the reactor core, usually the ex-core and in-core neutron detectors are employed. The thermocouples for temperature measurement are installed in the coolant inlet or outlet of the respective fuel assemblies. It is necessary to reconstruct the measurement information of the whole reactor position. However, the reading of different types of detector in the core reflects different aspects of the 3D power distribution. The feasibility of reconstruction the core three-dimension power distribution by using different combinations of in-core, ex-core and thermocouples detectors is analyzed in this paper to synthesize the useful information of various detectors. A comparison of multilayer perceptron (MLP) network and radial basis function (RBF) network is performed. RBF results are more extreme precision but also more sensitivity to detector failure and uncertainty, compare to MLP networks. This is because that localized neural network could offer conservative regression in RBF. Adding random disturbance in training dataset is helpful to reduce the influence of detector failure and uncertainty. Some convolution neural networks seem to be helpful to get more accurate results by use more spatial layout information, though relative researches are still under way.

• Journal of Korean Port Research
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• v.11 no.2
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• pp.191-202
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• 1997

As the suitability of berth allocation will ultimately have a significant influence on the performance of a berth, a great deal of attention should be given to berth allocation. Generally, a berth allocation problem has conflicting factors between servers and users. In addition, there is uncertainty in great extent caused by various factors such as departure delay, inclement weather on route, poor handling equipment, a lack of storage space, and other factors contribute to the uncertainty of arrival and berthing time. Thus, it is necessary to establish berth allocation planning which reflects the positions of interested parties and the ambiguity of parameters. For this, a berth allocation problem is formulated by fuzzy 0-1 integer programming introducing the concept of maximum Position Shift(MPS). But, the above approach has limitations in terms of computational time and computer memory when the size of problem is increased. It also has limitations with respect to the integration of other sub-systems such as ship planning system and yard planning system. For solving such problem, this paper focuses particularly on developing an efficient heuristic algorithm as a new technique of getting an effective solution. And also the suggested algorithm is verified through the illustrative examples and empirical appalicaton to BCTOC.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.113-118
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• 2013

In this paper, spoofing effects on a GNSS receiver were analyzed. The spoofer (spoofing device) was classified to two categories. One is an active spoofer and the other is a passive spoofer. The active spoofer was considered for analysis. For the analysis of spoofing effects on a GNSS receiver, a real-time GNSS spoofing simulator was developed. The simulator was consisted with two parts which are a baseband signal generation part and a RF up-conversion part. The first GNSS baseband signal was generated according to spoofing parameters such as range, range rate, GNSS navigation data, spoofing to GNSS signal ratio, and etc. The generated baseband signal was up-converted to GNSS L1 band. Then the signal transmitted to a GNSS signal. For a perfect spoofing, a spoofer knew an accurate position and velocity of a spoofing target. But, in real world, that is not nearly possible. Although uncertainty of position and velocity of the target was existed, the spoofer was operated as an efficient jammer.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.803-811
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• 2002

Machine vision systems are usually used to identify traffic lanes and then determine the steering angle of an autonomous vehicle in real time. The steering angle is calculated using a geometric model of various parameters including the orientation, position, and hardware specification of a camera in the machine vision system. To find the accurate values of the parameters, camera calibration is required. This paper presents a new camera-calibration algorithm using known traffic lane features, line thickness and lane width. The camera parameters considered are divided into two groups: Group I (the camera orientation, the uncertainty image scale factor, and the focal length) and Group II(the camera position). First, six control points are extracted from an image of two traffic lines and then eight nonlinear equations are generated based on the points. The least square method is used to find the estimates for the Group I parameters. Finally, values of the Group II parameters are determined using point correspondences between the image and its corresponding real world. Experimental results prove the feasibility of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1099-1104
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• 2003

In this paper, the position control of a car-like mobile robot using neural network is proposed. The positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as references, the robot posture by localization is corrected by a cascaded controller. In order to improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The remotely located neural network filter modifies the reference trajectories to minimize the positional errors by wireless communication. A car-like mobile robot is built as a test-bed and experimental studies of proposed several control algorithms are performed. It turns out that the best position control can be achieved by a cascaded controller with neural network.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.393-400
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• 2002

Teleoperation enables an operator to manipulate remote objects. One of the main goals in teleoperation researches is to provide the operator with the fueling of the telepresence, being present at the remote site. For these purposes, a master robot must be designed as a bilateral control system that can transmit position/force information to a slave robot and feedback the interaction force. A newly proposed impedance algorithm is applied for the control of a haptic interface that was developed as a master robot. With the movements of the haptic interface for position/force commands, impedance parameters are always varying. When the impedance parameters between an operator and the haptic interface and the dynamic model are known precisely, many model based control theories and methods can be used to control the device accurately. However, due to the parameters'variations and the uncertainty of the dynamic model, it is difficult to control haptic interfaces precisely. This paper presents a robust adaptive impedance control algorithm for haptic interfaces.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3186-3198
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• 1996

When a robot is to have contact with its enviornment, such as a medi-care robot, it would be advantageous for the robot to have a high compliance. For this reason, a robot having not only a flexible link but also an actuator with compliance, is desirable. This paper is concerned with the position and vibration control of 1 degree of freedom flexible robot using a pneumatic artificial muscle actuator. The dynamics of the manipulator assumed to be and Euler-Bernoulli beam are derived on the basis of the linear mathematical modle. Although this pneumatic artifical muscle actuator has many merits for the compliance robot, it is difficult to make an effective control scheme of this system because of ths nonlinearity and uncertainty on the dynamics of the actuator. By designing a controller using .mu.-synthesis, robust performance against measurement noise, various modeling uncertainties on the dynamics of the servo valve, actuator and mainpulator, is achieved. The effectiveness of the proposed control method is illustrated through simulations and experiments.