• International Journal of Automotive Technology
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• 제6권2호
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• pp.171-181
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• 2005

The purpose of this paper is two-fold: 1) A novel algorithm in order to extract lane-related information from road images is presented; 2) Design specifications of an image processing onboard unit capable of extracting lane­related information in real-time is also presented. Obtaining precise information from road images requires many features due to the effects of noise that eventually leads to long processing time. By exploiting a FPGA and DSP, we solve the problem of real-time processing. Due to the fact that image processing of road images relies largely on edge features, the FPGA is adopted in the hardware design. The schematic configuration of the FPGA is optimized in order to perform 3 $\times$ 3 Sobel edge extraction. The DSP carries out high-level image processing of recognition, decision, estimation, etc. The proposed algorithm uses edge features to define an Edge Distribution Function (EDF), which is a histogram of edge magnitude with respect to the edge orientation angle. The EDF enables the edge-related information and lane-related to be connected. The performance of the proposed system is verified through the extraction of lane-related information. The experimental results show the robustness of the proposed algorithm and a processing speed of more than 25 frames per second, which is considered quite successful.

• International Journal of Automotive Technology
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• 제7권3호
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• pp.361-367
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• 2006

This paper describes a novel stereo vision based localization of free parking site, which recognizes the target position of automatic parking system. Pixel structure classification and feature based stereo matching extract the 3D information of parking site in real time. The pixel structure represents intensity configuration around a pixel and the feature based stereo matching uses step-by-step investigation strategy to reduce computational load. This paper considers only parking site divided by marking, which is generally drawn according to relevant standards. Parking site marking is separated by plane surface constraint and is transformed into bird's eye view, on which template matching is performed to determine the location of parking site. Obstacle depth map, which is generated from the disparity of adjacent vehicles, can be used as the guideline of template matching by limiting search range and orientation. Proposed method using both the obstacle depth map and the bird's eye view of parking site marking increases operation speed and robustness to visual noise by effectively limiting search range.

• Journal of Information Processing Systems
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• 제10권4호
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• pp.602-617
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• 2014

The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.

• 정보처리학회논문지B
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• 제15B권3호
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• pp.229-236
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• 2008

본 논문은 화자의 입술 움직임으로부터 음성을 인식하는 자동 독순에서 회색조 영상 대신 컬러 영상을 사용하는 것의 유용성에 대해 고찰한다. 먼저 인간의 독순 실험을 통해 컬러 정보가 인식 성능에 어떠한 영향을 미치는지 확인한다. 다음으로 주성분분석을 이용한 자동 독순에서 회색조 또는 컬러 입술영상을 사용하는 경우에 대해 인식 성능을 비교한다. 다양한 컬러 좌표계에 대한 실험을 통해 컬러 영상의 사용으로 인식율이 향상됨을 보인다. 특히 RGB 좌표계를 사용했을 때 가장 좋은 성능을 얻으며, 회색조의 경우에 비해 잡음이 없는 환경에서는 4.7%, 잡음이 있는 경우 평균 13.0%의 상대적 오인식율 감소를 얻을 수 있음을 확인한다.

• 정보처리학회논문지C
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• 제8C권6호
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• pp.703-710
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• 2001

본 논문에서는 영상의 저작권 보호를 위하여 하나의 컨텐츠에 복수개의 서로 다른 워터마크를 삽입하는 다중 워터마킹(multiple watermarking)에 대하여 고찰한다. 대역확산(spread spectrum)을 이용한 다중 워터마킹에서 워터마크 삽입시 사용되는 키 사이의 직교성(orthogonality)이 유지되어야 삽입된 워터마크의 정확한 추출이 가능하게 된다. 랜덤계열 사이의 직교성을 유지하기 위하여 Gram-schmidt 직교화를 이용하여 워터마크를 삽입하는 방식을 제안한다. 제안 방식은 삽입된 워터마크를 정확하게 추출할 수 있을 뿐 아니라, 가우시안 노이즈 추가, 히스토그램 평활화, 감마 보정, 샤프닝, 명도/대비조정 등의 다양한 공격에 대하여 강인함을 보인다.

• 한국산업융합학회 논문집
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• 제20권2호
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• pp.115-124
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• 2017

This paper aims to develop an SMPS topology for handling a high range of input voltages based on a DC-DC flyback converter circuit. For this purpose, 2 capacitors of the same specifications were serially connected on the input terminal side, with a flyback converter of the same circuit configuration serially connected to each of them, so as to achieve high input voltage and an effect of dividing input voltage. The serially connected flyback converters have the transformer turn ratio of 1:1, so that each coil is used for the winding of a single transformer, which is a characteristic of doubly-fed configuration and enables the correction of input capacitor voltage imbalance. In addition, a pulse transformer was designed and fabricated in a way that can achieve the isolation and noise robustness of the PWM output signal of the PWM controller that applies gate voltage to individual flyback converter switches. PSIM simulation was carried out to verify such a structure and confirm its feasibility, and a 100W class stack was fabricated and used to verify the feasibility of the proposed high voltage SMPS topology.

• Wind and Structures
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• 제16권6호
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• pp.561-577
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• 2013

Rational Functions are used to express the self-excited aerodynamic forces acting on a flexible structure for use in time-domain flutter analysis. The Rational Function Approximation (RFA) approach involves obtaining of these Rational Functions from the frequency-dependent flutter derivatives by using an approximation. In the past, an algorithm was developed to directly extract these Rational Functions from wind tunnel section model tests in free vibration. In this paper, an algorithm is presented for direct extraction of these Rational Functions from section model tests in forced vibration. The motivation for using forced-vibration method came from the potential use of these Rational Functions to predict aerodynamic loads and response of flexible structures at high wind speeds and in turbulent wind environment. Numerical tests were performed to verify the robustness and performance of the algorithm under different noise levels that are expected in wind tunnel data. Wind tunnel tests in one degree-of-freedom (vertical/torsional) forced vibration were performed on a streamlined bridge deck section model whose Rational Functions were compared with those obtained by free vibration for the same model.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.604-615
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• 2019

Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.

• Smart Structures and Systems
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• 제24권2호
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• pp.233-242
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• 2019

In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

• Smart Structures and Systems
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• 제23권5호
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.