• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.285-288
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• 2005

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose a neuro-adaptive controller for robot manipulators using the radial basis function network(RBFN) that is a kind of a neural network. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between the actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that the parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed neuro-adaptive controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.321-327
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• 2005

A MVC (Multi-view Video Coding) method, which uses both an illumination change-adaptive ME (Motion Estimation)/DC (Motion Compensation) and a 2D (Dimensional) direct mode, is proposed. Firstly, a new SAD (Sum of Absolute Difference) measure for ME/MC is proposed to compensate the Luma pixel value changes for spatio-temporal motion vector prediction. Illumination change-adaptive (ICA) ME/MC uses the new SAD to improve both MV (Motion Vector) accuracy and bit saving. Secondly, The proposed 2D direct mode that can be used in inter-view prediction is an extended version of the temporal direct mode in MPEG-4 AVC. The proposed MVC method obtains approximately 0.8dB PSNR (Peak Signal-to-Noise Ratio) increment compared with the MPEG-4 AVC simulcast coding.

• Journal of Astronomy and Space Sciences
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• v.30 no.4
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• pp.307-314
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• 2013

Infrared optical systems are operated at low temperature and vacuum (LT-V) condition, whereas the assembly and alignment are performed at room temperature and non-vacuum (RT-NV) condition. The differences in temperature and pressure between assembly/alignment environments and operation environment change the physical characteristics of optical and opto-mechanical parts (e.g., thickness, height, length, curvature, and refractive index), and the resultant optical performance changes accordingly. In this study, using input relay optics (IO), among the components of the Immersion GRating INfrared Spectrograph (IGRINS) which is an infrared spectrograph, a simulation based on the physical information of this optical system and an actual experiment were performed; and optical performances in the RT-NV, RT-V, and LT-V environments were predicted with an accuracy of $0.014{\pm}0.007{\lambda}$ rms WFE, by developing an adaptive fitting line. The developed adaptive fitting line can quantitatively control assembly and alignment processes below ${\lambda}/70$ rms WFE. Therefore, it is expected that the subsequent processes of assembly, alignment, and performance analysis could not be repeated.

• Journal of KIISE
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• v.43 no.7
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• pp.801-811
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• 2016

Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.371-384
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• 2019

Motion perception has been tremendously improved in neuroscience and computer vision. The baseline motion perception model is mediated by the dorsal visual pathway involving the cortex areas the primary visual cortex (V1) and the middle temporal (V5 or MT) visual area. However, few works have been done on the extension of neural models to improve the efficacy and robustness of motion perception of real sequences. To overcome shortcomings in situations, such as varying illumination and large displacement, an adaptive V1-MT motion perception (Ad-V1MTMP) algorithm enriched to deal with real sequences is proposed and analyzed. First, the total variation semi-norm model based on Gabor functions (TV-Gabor) for structure-texture decomposition is performed to manage the illumination and color changes. And then, we study the impact of image local context, which is processed in extra-striate visual areas II (V2), on spatial motion integration by MT neurons, and propose a V1-V2 method to extract the image contrast information at a given location. Furthermore, we take feedback inputs from V2 into account during the polling stage. To use the algorithm on natural scenes, finally, multi-scale approach has been used to handle the frequency range, and adaptive pyramidal decomposition and decomposed spatio-temporal filters have been used to diminish computational cost. Theoretical analysis and experimental results suggest the new Ad-V1MTMP algorithm which mimics human primary motion pathway has universal, effective and robust performance.

• Korean Journal of Ecology and Environment
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• v.53 no.4
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• pp.390-407
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• 2020

This study was carried out to evaluate the restoration effect in the restored Yangjae stream and to draw up an adaptive management plan based on the results. As the result of evaluation on the restoration effect, the restored Yangjae stream was evaluated with low naturalness in both terms of the morphology of the stream and the composition and spatial distribution of vegetation. The diverse functional groups were introduced in the vegetation restoration, but the flooding regime, which is significant in the spatial distribution of riparian vegetation, were not correctly reflected. Exotic species or species that were not ecologically suitable for the location were introduced on the embankment and thus a measure to improve those problems is required. As the ecological principle was not reflected in the restoration plan, the stream was constructed as the double terrace structure. Therefore, the width of the waterway was narrowed further, and the waterfront was not designed to accommodate changes from flooding disturbance, making the micro-topography of the stream simpler and the naturalness lower. The adaptive management plan was prepared to improve those problems, and a plan for creating an ecological network was recommended to enhance the restoration effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1666-1672
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• 2019

A closed loop wireless transmission method adaptive to mobile unit speed is proposed in this paper. A mobile communication node measures the mobile speed based on the transmitted pilot signals through Doppler frequency estimation, and it changes the transmission period of pilot signals as per estimated mobile speed adaptively. The pilot signals with the different transmission periods are transmitted using the different PN sequences with the previous ones without any explicit information about the new period. The corresponding receiver node can detect and extract the transmitted pilot signals through blind search of the transmitted PN sequences of the pilot signals, and it can demodulate and decode the transmitted information using the channel estimation results based on the detected pilot signals. The performance of the proposed method had been analyzed through the simulation under the fading channel environments and compared with the previous methods. The simulation results showed performance improvement of the proposed method over the existing ones.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3006-3016
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• 2022

In this study, the images of specific prompt gamma (PG)-rays of 719 keV emitted from proton-boron reactions were analyzed using single-photon emission computed tomography (SPECT). Quantitative evaluation of the images verified the detection of anatomical changes in tumors, one of the important factors in daily adaptive proton therapy (DAPT) and verified the possibility of application of the PG-ray images to DAPT. Six scenarios were considered based on various sizes and locations compared to the reference virtual tumor to observe the anatomical alterations in the virtual tumor. Subsequently, PG-rays SPECT images were acquired using the modified ordered subset expectation-maximization algorithm, and these were evaluated using quantitative analysis methods. The results confirmed that the pixel range and location of the highest value of the normalized pixel in the PG-rays SPECT image profile changed according to the size and location of the virtual tumor. Moreover, the alterations in the virtual tumor size and location in the PG-rays SPECT images were similar to the true size and location alterations set in the phantom. Based on the above results, the tumor anatomical alterations in DAPT could be adequately detected and verified through SPECT imaging using the 719 keV PG-rays acquired during treatment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.