• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.583-588
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• 2024

With the emergence of Brain-Computer Interface (BCI) technology, analyzing mirror neurons has become more feasible. However, evaluating the accuracy of BCI systems that rely on human thoughts poses challenges due to their qualitative nature. To harness the potential of BCI, we propose a new approach to measure accuracy based on the characteristics of mirror neurons in the human brain that are influenced by speech speed, depending on the ultimate goal of movement. In Chapter 2 of this paper, we introduce mirror neurons and provide an explanation of human posture estimation for mirror neurons. In Chapter 3, we present a powerful pose estimation method suitable for real-time dynamic environments using the technique of human posture estimation. Furthermore, we propose a method to analyze the accuracy of BCI using this robotic environment.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.33-40
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• 2020

This work tackles the problem of conventional reinforcement learning scheme which has a relatively long training time to reduce energy consumption in underwater network. The enhanced scheme adjusts the learning range of reinforcement learning based on distance estimation. It can be reduce the scope of learning. To take account the fact that the distance estimation may not be accurate due to the underwater wireless network characteristics. this research added noise in consideration of the underwater environment. In simulation result, the proposed AQ-NAV scheme has completed learning much faster than existing method. AQ-NAV can finish the training process within less than 40 episodes. But the existing method requires more than 120 episodes. The result show that learning is possible with fewer attempts than the previous one. If AQ-NAV will be applied in Underwater Networks, It will affect energy efficiency. and It will be expected to relieved existing problem and increase network efficiency.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.367-371
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• 2015

In this paper, a novel localization method for a monocular camera is proposed by using a feature-based probabilistic map. The localization of a camera is generally estimated from 3D-to-2D correspondences between a 3D map and an image plane through the PnP algorithm. In the computer vision communities, an accurate 3D map is generated by optimization using a large number of image dataset for camera pose estimation. In robotics communities, a camera pose is estimated by probabilistic approaches with lack of feature. Thus, it needs an extra system because the camera system cannot estimate a full state of the robot pose. Therefore, we propose an accurate localization method for a monocular camera using a probabilistic approach in the case of an insufficient image dataset without any extra system. In our system, features from a probabilistic map are projected into an image plane using linear approximation. By minimizing Mahalanobis distance between the projected features from the probabilistic map and extracted features from a query image, the accurate pose of the monocular camera is estimated from an initial pose obtained by the PnP algorithm. The proposed algorithm is demonstrated through simulations in a 3D space.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.94-101
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• 2009

We introduce a user authentication system using distance estimation and a simple challenge response protocol based on a pre-established key. Using the time difference of arrival between an RF signal and an ultrasonic signal, an authenticator verifies if a user's authentication token is within its threshold distance, and it also verifies if the token's response to its random challenge is valid. We implement our authentication system and we analyze the success rates for authentication according to the variations in the distances and facing angles between the authenticator and the token. Our experimental results show that the token is authenticated with very high probability in reasonable settings.

• Computers and Concrete
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• v.12 no.2
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• pp.151-167
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• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.822-826
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• 2013

In this paper, we propose an AFDO (Adaptive Fault Diagnosis Observer) and a fault tolerant controller for a class of nonlinear continuous-time system under the nonlinear abrupt actuator faults. Together with its estimation laws, the AFDO which estimates that the actuator faults is designed by using the Lyapunov analysis. Then, based on the designed AFDO, an adaptive sliding mode controller is proposed as the fault tolerant controller. Using Lyapunov stability analysis, we also prove the uniform boundedness of the state, the output and the fault estimation errors, and the asymptotic stability of the tracking error under the nonlinear time-varying faults. Finally, we illustrate the effectiveness of the proposed diagnosis method and the control scheme thorough computer simulations.

• Nuclear Engineering and Technology
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• v.47 no.5
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• pp.596-607
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• 2015

In this paper, we describe a computer program, iBEST (inverse Burnup ESTimator), that we developed to accurately estimate the burnup histories of spent nuclear fuels based on sample measurement data. The burnup history parameters include initial uranium enrichment, burnup, cooling time after discharge from reactor, and reactor type. The program uses algebraic equations derived using the simplified burnup chains of major actinides for initial estimations of burnup and uranium enrichment, and it uses the ORIGEN-S code to correct its initial estimations for improved accuracy. In addition, we newly developed a stable bisection method coupled with ORIGEN-S to correct burnup and enrichment values and implemented it in iBEST in order to fully take advantage of the new capabilities of ORIGEN-S for improving accuracy. The iBEST program was tested using several problems for verification and well-known realistic problems with measurement data from spent fuel samples from the Mihama-3 reactor for validation. The test results show that iBEST accurately estimates the burnup history parameters for the test problems and gives an acceptable level of accuracy for the realistic Mihama-3 problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.49-54
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• 2007

Structural integrity assessment technique for pavement system is studied considering the uncertainties among the material properties. The artificial neural networks technique is applied for the inverse analysis to estimate the elastic modulus based on the measured deflections from the FWD test. A computer code based on the spectral element method was developed for the accurate and fast analysis of the multi-layered soil structures, and the developed program was used for generating the training and testing patterns for the neural network. Neural networks was applied to estimate the elastic modulus of pavement system using the maximum deflections with and without the uncertainties in the material properties. It was found that the estimation results by the conventiona1 neural networks were very poor when there exist the uncertainties and the estimation results could be significantly improved by adopting the proposed method for generating training patterns considering the uncertainties among material properties.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.899-910
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• 2013

This paper presents a novel lateral stability control system for electric vehicle based on sideslip angle estimation through Kalman filter using the integration of a single antenna GPS receiver and yaw rate sensor. Using multi-rate measurements including yaw rate and course angle, time-varying parameters disappear from the measurement equation of the proposed Kalman filter. Accurate sideslip angle estimation is achieved by treating the combination of model uncertainties and external disturbances as extended states. Active front steering and direct yaw moment are integrated to manipulate sideslip angle and yaw rate of the vehicle. Instead of decoupling control design method, a new control scheme, "two-input two-output controller", is proposed. The extended states are utilized for disturbance rejection that improves the robustness of lateral stability control system. The effectiveness of the proposed methods is verified by computer simulations and experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.756-758
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• 2010

Fixed frame size is used in the RFID system with the frame-based slot ALOHA algorithm. Therefore, it is anticipated that the tag identification performances will highly depend on the number of tags within the reader's identification range and the frame size. In this paper, we propose a tag number estimation scheme and analyze the performance with the computer simulations. The proposed scheme is based on the status of slots that the tags respond during a query round as well as the probabilistic calculations.