• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.890-903
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• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.179-187
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• 2011

Multiple roadmap DB providers are already available in these days, and try to reduce unknown roads in their own roadmaps. However, cooperation models or Win-Win approaches between roadmap providers are not considered yet. Thus, In this paper, We proposed a cloud-oriented real-time roadmap generation and update method between heterogeneous navigation systems for unknown roads. With the proposed method, the roadmap DB providers update the own roadmap DB for navigation systems in real time. Also, they can provide the complete roadmap without unknown roads to users instantly. Therefore, the proposed method can reduce the costs of an actual traveling test and the maintenance for the roadmap DB provides. Thus, the cloud-oriented roadmap generation method can more efficiently update the unknown road information.

• Analytical Science and Technology
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• v.25 no.2
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• pp.133-145
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• 2012

The quantitative composition of total suspended particulates (TSP) in the atmosphere is identified to consist mainly of ions, organic carbon (OC), element carbon (EC), and metals. In terms of environmental analysis, the rest of the TSP composition may be defined as unknown fraction (${\Sigma}X$) which is yet difficult to analyze both quantitatively and qualitatively. In this study, the major components of TSP were measured at an urban residential area (Gang Seo) in Seoul, Korea from February to December 2009. These TSP data were analyzed in various respects to explain the relationship between known and unknown constituents. During this study period, TSP was comprised mainly of unknown compounds (48.6%) followed by ions, OC, EC, and metals. The results of this study indicate that the distribution of ${\Sigma}X$ exhibits a strong similarity with ${\Sigma}Anions$, as they both increase with increasing TSP levels. However, if the concentrations of ${\Sigma}X$ and ${\Sigma}Anion$ are normalized against TSP, they exhibit a strong inverse correlation with each other due probably to larges differences in solubility. To establish a better strategy for air quality control in urban atmosphere, more efforts are needed to characterize unidentified proportion of particulate matters.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.41-41
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• 2000

biped robots are expected to robustly traverse terrain with various unknown surfaces. The robot will occasionally encounter the unexpected events in made-for human environments. The slipping is a very real and serious problem in the unexpected events. The robot system must respond to the unexpected slipping after it has occurred and before control is lost. This paper proposes a reflex control method for biped robots to recover from slipage. Computer simulations with the 6-DOF environment model which consists of nonlinear dampers, nonlinear springs, and linear springs, show that the proposed method is effective in preventing fall-down due to slippage.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.82.1-82
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• 2002

$\textbullet$ Nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter incorporating numbers of validated measurements $\textbullet$ Probability density function of the NDS $\textbullet$ Simulation results in a clutter environment to verify the performances $\textbullet$ Sensitivity analysis for the unknown spatial clutter density

• Steel and Composite Structures
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• v.41 no.6
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• pp.899-910
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• 2021

A 4-unknown shear deformation theory is applied to investigate the vibration of functionally graded plates under thermal environment. The plate is fabricated from a functionally graded material mixed of ceramic and metal with continuously varying material properties through the plate thickness. Three types of thermal loadings, uniform, linear and nonlinear temperature rises along the plate thickness are taken into account. The present theory contains four unknown functions as against five or more in other higher order shear deformation theories. The through-the-thickness distributions of transverse shear stresses of the plate are considered to vary parabolically and vanish at upper and lower surfaces. The present model does not require any problem dependent shear correction factor. Analytical solutions for the free vibration analysis are derived based on Fourier series that satisfy the boundary conditions (Navier's method). Benchmark solutions are firstly considered to evaluate the accuracy of the proposed model. Comparisons with the solutions available in literature revealed the good capabilities of the present model for the simulations of vibration responses of FG plates. Some parametric studies are carried out for the frequency analysis by varying the volume fraction profile and the temperature distribution across the plate thickness.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.85-94
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• 2011

Spectrum sensing is one of the most important technologies in cognitive radio. Although many studies have considered energy detection technique as the spectrum sensing technique, noise variance in practical systems is difficult to estimate accurately. Thus, in the real system, the probability of false alarm will not be maintained constant. In this paper, with considering that the cognitive radio does not know the primary user's signal, we propose a new spectrum sensing scheme which can operate without the information of noise variance. Through simulations, we show that the proposed scheme can detect spectrum with the condition of unknown noise information and have robustness for the change of noise variance.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1819-1826
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• 2009

To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.14-25
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• 2021

This paper presents a RLS based adaptive sliding mode observer (A-SMO) for unknown fault reconstruction in longitudinal autonomous driving. Securing the functional safety of autonomous vehicles from unexpected faults of sensors is essential for avoidance of fatal accidents. Because the magnitude and type of the faults cannot be known exactly, the RLS based A-SMO for unknown acceleration fault reconstruction has been designed with relationship function in this study. It is assumed that longitudinal acceleration of preceding vehicle can be obtained by using the V2V (Vehicle to Vehicle) communication. The kinematic model that represents relative relation between subject and preceding vehicles has been used for fault reconstruction. In order to reconstruct fault signal in acceleration, the magnitude of the injection term has been adjusted by adaptation rule designed based on MIT rule. The proposed A-SMO in this study was developed in Matlab/Simulink environment. Performance evaluation has been conducted using the commercial software (CarMaker) with car-following scenario and evaluation results show that maximum reconstruction error ratios exist within range of ±10%.