• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.187-187
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• 1999

in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.1-6
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• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.262-267
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• 2001

Jeha Ryu Department of Mechatronics, Kwangju Institute of Science and Technology This paper presents an application of a robust adaptive control strategy to HexaSlide type six degrees-of-freedom parallel manipulators. The HexaSlide type parallel manipulators are characterized as an architecture with constant link lengths that are attached to moving sliders on the ground and to a mobile platform. The proposed control law is developed based on a simplified second order system dynamic equation in joint space with uncertain mass, damper, spring, and Coulomb friction terms. These uncertain parameters are updated by an adaptation law that is derived by Lyapunov stability theorem. A robust adaptive control law by using the boundary layer is designed for the purpose of compensating for the neglected dynamic effects of the mobile platform and the six moving links that are modeled as a disturbance term. Experimental results show good and fast tracking performance.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.323-326
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• 2007

The various studies of combining neural network and hidden Markov models within a single system are done with expectations that it may potentially combine the advantages of both systems. With the influence of these studies, tandem approach was presented to use neural network as the classifier and hidden Markov models as the decoder. In this paper, we applied the trend information of segmental features to tandem architecture and used posterior probabilities, which are the output of neural network, as inputs of recognition system. The experiments are performed on Aurora2 database to examine the potentiality of the trend feature based tandem architecture. The proposed method shows the better results than the baseline system on very low SNR environments.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.121-126
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.120-121
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• 2013

Recently, our country is to be diversification a leisure activity and tourism form by income level rise and traffic circumstance improvement. But Basic facilities expropriated these leisure activities have built an insufficient condition. Also, Building a facility, Introduced tools and materials import the whole quantity. Therefore, In this paper, it builds a tension mooring unit to use EPDM and Kevlar R29 and it develops a domestic marine circumstance through reliability based on robust design. It contributes to marine leisure culture activation and industry development.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.540-548
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• 2005

This paper concerns the application and demonstration of sliding mode observer for aeroelastic system, which is robust to model uncertainty including mass and stiffness of the system and various disturbances. The performance of a sliding mode observer is compared with that of a conventional Kalman filter to demonstrate robustness and disturbance decoupling characteristics. Aeroelastic instability may occur when an elastic structure is moving even in subcritical flow speed region. Simulation results using sliding mode observer are presented to control aeroelastic response of flapped wing system due to various external excitations as well as model uncertainty and sinusoidal disturbances in subcritical incompressible flow region.

• Journal of Ocean Engineering and Technology
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• v.27 no.2
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• pp.93-99
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• 2013

This paper describes a robust algorithm for an integrated underwater navigation system based on VKF (velocity Kalman filter). The proposed approach relies on a VKF, augmented by the altitude from an echo-sounder-based switching architecture to yield robust performance, even when DVL (Doppler velocity log) exceeds the measurement range and the measured value cannot be valid. The proposed approach relies on three parts: 1) PINS (pure inertial navigation system), 2) VKF design, and 3) VKF-aided integrated navigation filter design. To evaluate the proposed method, we compare the results of the VKF-aided navigation system with the simulation result from a PINS and conventional INS-DVL method.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.620-626
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• 2019

With the development of robot technology, the expectation of autonomous mission operations has increased, and the research on robot control architectures and mission planners has continued. A scalable and robust control architecture is required for unmanned surface vehicles (USVs) to perform a variety of tasks, such as surveillance, reconnaissance, and search and rescue operations, in unstructured and time-varying maritime environments. In this paper, we propose a robot control architecture along with a new utility function that can be extended to various applications for USVs. Also, an additional structure is proposed to reflect the operator's command and improve the performance of the autonomous mission. The proposed architecture was developed using a robot operating system (ROS), and the performance and feasibility of the architecture were verified through simulations.

• Land and Housing Review
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• v.1 no.1
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• pp.1-7
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• 2010

Case-based reasoning (CBR) method can make estimators understand the estimation process more clearly. Thus, CBR is widely used as a methodology for cost estimation. In CBR, the quality of case retrieval affects the relevance of retrieved cases and hence the overall quality of the reminding capability of CBR system. Thus, it is essential to retrieve relevant past cases for establishing a robust CBR system. Case retrieval needs the following tasks to obtain appropriate case(s); indexing, search, and matching (Aamodt and Plaza 1994). However, the previous CBR researches mostly deal with matching process that has limits such as accuracy and efficiency of case retrieval. In order to address this issue, this research presents a CBR cost model for building projects that has two-step retrieval process: decision tree and nearest neighbor methods. Specifically, the proposed cost model has indexing, search and matching modules. Features in the model are divided into shape-based and scale-based attributes. Based on these, decision tree is established for facilitating the search task and nearest neighbor method was utilized for matching task. In regard to applying nearest neighbor method, attribute weights are assigned using GA optimization and similarity is calculated using the principle of distance measuring. Thereafter, the proposed CBR cost model is developed using 174 cases and validated using 12 test cases.