• Proceedings of the IEEK Conference
• /
• 2000.11a
• /
• pp.77-80
• /
• 2000

Adaptive filter based code acquisition scheme offers a fast acquisition with a low error probability. However, it has been studied only under a coherent environment. In this paper, the noncoherent adaptive code acquisition scheme employing a differential detection technique is proposed. For the proposed scheme, system probabilities and the mean acquisition time are analyzed numerically. Simulation results show that the proposed system outperforms over the conventional matched filter by 2-4 ㏈ under AWGN channel for 16 taps.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.710-713
• /
• 2005

In this paper, a design method to detect faults in plants with uncertainties is proposed. When a plant has faults, the plant will be corrupted by an unknown fault signal. In addition, the plant also includes uncertainties, such as disturbances and plant parameter deviations. In this case, the proposed method estimates the fault signal by using an adaptive observer. Numerical examples are given to demonstrate the validity of the proposed method.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.6
• /
• pp.585-591
• /
• 2020

The adaptive transmission techniques are efficient method for underwater acoustic communication to improve the system efficiency by varying transmission parameters according to channel conditions. In this paper, we construct four transmission modes with different data rates using the convolutional codes, which is freely set to size of information bits. On the receiver side, one critical component of adaptive system is to find which mode has best performance. In this paper, we proposed threshold detection algorithm to decide appropriate mode and applied turbo equalization method based on BCJR decoder in order to improve performance. We analyzed the performance of four modes based on threshold detection algorithm through the lake experiment.

• Smart Structures and Systems
• /
• v.14 no.3
• /
• pp.285-305
• /
• 2014

This study employs a novel beam-type wavelet finite element model (WFEM) to fulfill an adaptive-scale damage detection strategy in which structural modeling scales are not only spatially varying but also dynamically changed according to actual needs. Dynamical equations of beam structures are derived in the context of WFEM by using the second-generation cubic Hermite multiwavelets as interpolation functions. Based on the concept of modal strain energy, damage in beam structures can be detected in a progressive manner: the suspected region is first identified using a low-scale structural model and the more accurate location and severity of the damage can be estimated using a multi-scale model with local refinement in the suspected region. Although this strategy can be implemented using traditional finite element methods, the multi-scale and localization properties of the WFEM considerably facilitate the adaptive change of modeling scales in a multi-stage process. The numerical examples in this study clearly demonstrate that the proposed damage detection strategy can progressively and efficiently locate and quantify damage with minimal computation effort and a limited number of sensors.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.8 no.5
• /
• pp.1135-1140
• /
• 2007

This paper proposes an edge-based text region detection algorithm using the adaptive character-edge maps which are independent of the size of characters and the orientation of character string in natural images. First, labeled images are obtained from edge images and in order to search for characters, adaptive character-edge maps by way grammar are applied to labeled images. Next, selected label images are clustered as for distance of its neighbors. And then, text region candidates are obtained. Finally, text region candidates are verified by using the empirical rules and horizontal/vertical projection profiles based on the orientation of text region. As the results of experiments, a text region detection algorithm turned out to be robust in the matter of various character size, orientation, and the complexity of the background.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.11B
• /
• pp.1289-1295
• /
• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.2
• /
• pp.210-217
• /
• 1990

For the failure detection of dynamic systems, processing the residuals from the observer of the estimator is the most general method. A failure detection method which use an adaptive predictor to separate the effect of sensor failure from the additive noise in the residuals of a Kalman filter that is employed as an estimator of a dynamic system is addressed here. In the method, the property of the residuals of an optimal Kalman estimator is exploited. The simulation results of this method shows that the proposed method is superior to the sequential probability ratio test for a small failure magnitude.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.5
• /
• pp.632-639
• /
• 2012

Ad-hoc networks can be used various environment, which it is difficult to construct infrastructures, such as shadowing areas, disaster areas, war area, and so on. In order to support to considerable and various wireless services, more spectrum resources are needed. However, efficient utilization of the frequency resource is difficult because of spectrum scarcity and the conventional frequency regulation. Ad-hoc networks employing cognitive radio(CR) system that guarantee high spectrum utilization provide effective way to increase the network capacity. In conventional CR based ad-hoc network, it uses constant threshold value to detect primary user signal, so the results become not reliable. In this paper, to solve this problem, we apply adaptive threshold value to the CR based ad-hoc network, and adaptive threshold is immediately changed by SNR(Signal to Noise Ratio). From the simulation results, we confirmed that proposed algorithm has the greatly better detection probabilities than conventional CR based ad-hoc network.

• Journal of Broadcast Engineering
• /
• v.26 no.2
• /
• pp.184-196
• /
• 2021

This paper proposes statistics adaptive linear regression-based object size prediction method for object detection. YOLOv2 and YOLOv3, which are typical deep learning-based object detection algorithms, designed the last layer of a network using statistics adaptive exponential regression model to predict the size of objects. However, an exponential regression model can propagate a high derivative of a loss function into all parameters in a network because of the property of an exponential function. We propose statistics adaptive linear regression layer to ease the gradient exploding problem of the exponential regression model. The proposed statistics adaptive linear regression model is used in the last layer of the network to predict the size of objects with statistics estimated from training dataset. We newly designed the network based on the YOLOv3tiny and it shows the higher performance compared to YOLOv3 tiny on the UFPR-ALPR dataset.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.89-96
• /
• 2016

The performance of anti-submarine sonar detection is required to improve by the development of submarine noise reduction technology. because of the need of an anti-submarine detection ability, known for superior beamforming performance, adaptive beamforming algorithms have been considered as an alternative beamforming algorithm of a conventional beamforming algorithm. In order to achieve improved performance by applying an adaptive beamforming algorithm to the sonar system, the adaptive beamforming algorithm applicability of system must be verified, To do this, the performance index for the system applicability must be established. In this paper, a tracking initiation probability of the adaptive beamforming algorithm and the conventional beamforming algorithm was calculated and the performance of both techniques was quantified, a system applicability of the adaptive beamforming algorithm was reviewed.