• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.217-227
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• 2022

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of the Korean Geotechnical Society
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• v.23 no.1
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• pp.67-76
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• 2007

Flat DMT penetration was analyzed using a finite element model based on a large strain formulation. The ABAQUS/Explicit, a commercial finite element method, was used to study the flat DMT penetration in soils. Then, because the very large mesh distortion occurred due to the penetration of flat DMT, the adaptive meshing technique was utilized to maintain a high quality mesh configuration. The undrained shear strength obtained from the flat DMT is estimated using only the horizontal stress index ($K_{D}$) and so it became necessary to examine using the analysis results obtained from the penetration of the flat DMT. Analysis results show that in normally consolidated region of $K_{D}=2$, the results obtained from the correlations proposed by Marchetti show good agreement with those estimated from the finite element method. The present analysis also shows that in overconsolidated region of $K_{D}>2$, the results obtained from the relationships proposed by Kamei and Iwasaki show good agreement with those provided by the penetration analysis.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.105-115
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• 2023

In this paper, we describe the process and method of converting tens of TB of time-varying AMR (adaptive mesh refinement) volume data generated as a result of numerical model simulation into optimized data that can be used for various XR devices. AMR volume data is a useful data format for complex modeling and simulation, and it can efficiently express materials such as star clusters and gases that exist in the very wide outer space used in this study. we analyzes the metadata of AMR data, samples it at low resolution, optimizes information in important areas, and converts it into a data set that can be used even on relatively low performance XR devices. Finally, we introduces how the optimized data was utilized and visualized through the development of immersive XR content using the data set.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.743-750
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• 2008

Due to unusual climate change and global warming, drought and flood happen frequently not only in Korea but also in all over the world. It leads to the serious damages and injuries in urban areas as well as rural areas. Since the concentration time is short and the flood flows increase urgently in urban stream basin, the chances of damages become large once heavy storm occurs. A decision support system for flood forecasting and warning in urban stream is developed as an alternative to alleviate the damages from heavy storm. It consists of model base management system based on ANFIS (Adaptive Neuro Fuzzy Inference System), database management system with real time data building capability and user friendly dialog generation and management system. Applying the system to the Tanceon river basin, it can forecast and warn the stream flows from the heavy storm in real time and alleviate the damages.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.522-527
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• 2022

Object tracking is a field of signal processing that sequentially tracks the location of an object based on the previous-time location estimations and the present-time observation data. In this paper, we propose an adaptive scaling neural network that can track and adjust the scale of the input data with three recursive neural network (RNN) submodules. To evaluate object tracking performance, we compare the proposed system with the Kalman filter and the maximum likelihood object tracking scheme under an one-dimensional object movement model in which the object moves with piecewise constant acceleration. We show that the proposed scheme is generally better, in terms of root mean square error (RMSE) performance, than maximum likelihood scheme and Kalman filter and that the performance gaps grow with increased observation noise.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.307-316
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• 2004

Wireless local area networks have known an increasing popularity during the past few years. However, as new user applications diverge from the traditional data-centric model, the introduction of efficient, QoS aware medium access methods becomes of utmost importance. EY-NPMA is a medium access protocol belonging to the contention paradigm that provides support for service differentiation and low collision rates. In this paper, we address a shortcoming of EY-NPMA as indicated by previous studies, namely the insensitivity of the protocol to different working conditions. In this work, we study and evaluate a mechanism that allows a network employing EY-NPMA to adapt its operating parameters according to the offered load. Simulation studies further document and confirm the positive characteristics of the proposed mechanism.

• ETRI Journal
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• v.18 no.2
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• pp.53-60
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• 1996

Blind equalization is a technique for adaptive equalization of a communication channel without the aid of training sequences. This paper proposes a new blind equalization algorithm. The advantage of the new algorithm is that it has the lower residual error than the GA (proposed by Godard) and Sign_GA (proposed by Weerackody et al.). The superior performance of the proposed algorithm is illustrated for the 16-QAM signal constellation. A Rummler channel model is assumed as a transmission medium. The performance of the proposed algorithm is compared to the GA, Sign_GA and Stop & Go Algorithm (SGA). The simulation results demonstrate that an improvement in performance is achieved with the proposed equalization algorithm.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.