• 대한임베디드공학회논문지
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• 제13권2호
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• pp.73-81
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• 2018

This paper describes a device platform which the blind can wear to keep path and to get surrounding information during their independent walking. Compared to the existing technologies, the proposed device could be used indoors and outdoors, and maps need not be provided in advance. It is composed of a glasses type device equipped with image sensors, and a portable device that analyzes sensor data for sighted guiding. RGB images and depth images are extracted to generate a walking map based on feature points. It also can cope with the risk of collision with bollard, color cone by applying vertical obstacle detection technology based on floor detection.

• 대한교통학회지
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• 제32권4호
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• pp.293-302
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• 2014

교통사고 및 사고로 인한 사상자수 감소를 위해 기존의 자동차에 각종 센서나 통신기술 등의 첨단기술을 융합한 첨단안전자동차에 대한 연구가 활발히 진행 중에 있다. 이러한 첨단안전자동차의 시장진입 및 관련 기술도입을 위해서는 첨단안전자동차 기술의 효과분석을 통한 도입 타당성 평가가 필요하다. 본 연구에서는 계층화분석법(AHP: Analytic Hierarchy Process)을 이용하여 첨단안전자동차 기술 중 사고예방의 기능을 가지는 첨단운전자지원시스템의 효과추정 방법론을 제시하였다. 제시한 효과추정 방법론을 이용하여 적응형순항제어장치(ACC: Adaptive Cruise Control), 자동비상제동장치(AEBS: Automatic Emergency Braking System), 차로이탈경고장치(LDWS: Lane Departure Warning System), 사각지역감시장치(BSDS: Blind Spot Detection System)의 네 가지 시스템을 통합하여 평가하였다. 분석결과, 운전자지원시스템의 효과는 약 10.18%의 사고감소 효과가 있는 것으로 나타났으며, 적응형순항제어장치는 10.43%, 자동비상제동장치는 10.17%, 차로이탈경고장치는 9.96%, 사각지역감시장치는 10.14%의 사고감소 효과가 있을 것으로 추정되었다. 본 연구의 결과는 추후 첨단안전자동차 시스템 도입시 도입타당성을 제시하는데 기초자료로써 활용이 가능할 것으로 기대된다.

• 한국항해항만학회지
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• 제30권2호
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• pp.167-172
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• 2006

In this paper, the issue of blind detection of Alamouti-type differential space-time (ST) ${\pi}/2$-shifted BPSK modulation in static Rayleigh fading channels is considered. We introduce a novel transformation to the received signal from each receiver antenna such that this binary ST modulation, which has a second-order transmit-diversity, is equivalent to QPSK modulation with second-order receive-diversity. The pre-detection combining of the result of transformation allows us to apply a low complexity detection technique specifically designed for receive-diversity, namely, scalar multiple-symbol differential detection (MSDD). With receiver complexity proportional to the observation window length, our receiver can achieve the performance 1.5dB better than that of conventional differential detection ST and 0.5dB worse than that qf a coherent maximum ratio combining receiver (with differential decoding) approximately.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.6-31
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• 2018

With the rapid development of the science and technology, it has been becoming more and more convenient to obtain abundant information via the diverse multimedia medium. However, the contents of the multimedia are easily altered with different editing software, and the authenticity and the integrity of multimedia content are under threat. Forensics technology is developed to solve this problem. We focus on reviewing the blind image forensics technologies for copy-move forgery in this survey. Copy-move forgery is one of the most common manners to manipulate images that usually obscure the objects by flat regions or append the objects within the same image. In this paper, two classical models of copy-move forgery are reviewed, and two frameworks of copy-move forgery detection (CMFD) methods are summarized. Then, massive CMFD methods are mainly divided into two types to retrospect the development process of CMFD technologies, including block-based and keypoint-based. Besides, the performance evaluation criterions and the datasets created for evaluating the performance of CMFD methods are also collected in this review. At last, future research directions and conclusions are given to provide beneficial advice for researchers in this field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.658-672
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• 2021

Since the signal with strong power need be demodulated first for successive interference cancellation (SIC) receiver in non-orthogonal multiple access (NOMA) systems, the base station (BS) need inform the near user terminal (UT), which has allocated higher power, of the far UT's modulation mode. To avoid unnecessary signaling overhead of control channel, a blind detection algorithm of NOMA signal modulation mode is designed in this paper. Taking the joint constellation density diagrams of NOMA signal as the detection features, the deep residual network is built for classification, so as to detect the modulation mode of NOMA signal. In view of the fact that the joint constellation diagrams are easily polluted by high intensity noise and lose their real distribution pattern, the wavelet denoising method is adopted to improve the quality of constellations. The simulation results represent that the proposed algorithm can achieve satisfactory detection accuracy in NOMA systems. In addition, the factors affecting the recognition performance are also verified and analyzed.

• 전자공학회논문지
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• 제54권1호
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• pp.96-110
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• 2017

기존의 차량 검출 연구들의 대부분은 일반렌즈 또는 광각렌즈를 가지는 후방 카메라를 사용하기 때문에 사각지대가 넓으며, 영상에 노이즈 및 다양한 외부 환경에 취약한 부분이 있다. 본 논문에서는 사각지대를 줄이고, 노이즈 및 가혹한 외부 환경에서도 인식이 가능한 검출 방법을 제안한다. 먼저 광각렌즈보다 더 넓은 화각을 가진 어안렌즈를 이용해 사각지대를 최소화한다. 렌즈의 화각이 커진 만큼 비선형 방사왜곡도 커지게 되므로, 정확한 영상 결과를 얻기 위해서 왜곡 상수 초기화와 최적화를 실시한 후 Calibration을 이용하였다. 그리고 Calibration과 동시에 원본 영상을 분석하여 안개가 자욱한 상황과 갑작스러운 조도 변화로 인해 생기는 명순응, 암순응 현상에 의한 시야 방해 상황에서도 인식이 가능하도록 안개 제거와 밝기 보정을 이용하였다. 안개 제거는 일반적으로 계산 시간이 매우 크다. 따라서 계산 시간을 줄이기 위해 대표적인 안개 제거 알고리즘인 Dark channel prior를 기반으로 안개를 제거하였다. 밝기 보정 시에는 Gamma correction을 이용했고, 보정에 필요한 Gamma value를 결정하기 위해 영상에 대한 밝기 및 명암 평가가 수행하였다. 평가는 영상의 전체가 아닌 일부분을 이용하여 할애되는 계산시간을 줄였다. 밝기 및 명암 값이 계산되면 그 값을 이용해 Gamma value를 결정하고 전체 영상에 보정을 실시하였다. 그리고 밝기 보정과 안개 제거로 나누어 병렬 처리한 후, 영상을 하나로 정합함으로써 전 처리 과정의 연산시간을 줄였다. 이후 보정된 영상으로부터 특징추출법인 HOG를 이용하여 차량을 검출하였다. 그 결과 본 논문에서 제안하는 방법의 영상 보정을 이용한 차량 검출을 하는데 1프레임당 0.064초가 걸렸으며, 기존의 차량 검출 방법에 비해 7.5%의 향상된 검출률을 얻었다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• ETRI Journal
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• 제41권5호
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• pp.658-669
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• 2019

For dual-channel time-frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single-source points (TF-SSP) is used to improve the TF sparsity of each source. For more distinguishability, as the ratios of the coefficients from different columns of the mixing matrix are close, a local peak-detection mechanism based on interval probability (LPIP) is proposed. LPIP utilizes uniform subintervals to optimize and classify the TF coefficient ratios of the detected TF-SSP effectively in the case of a high level of TF overlap among sources and reduces the TF interference points and redundant signal features greatly to enhance the estimation accuracy. The simulation results show that under both noiseless and noisy cases, the proposed method performs better than the selected mainstream traditional methods, has good robustness, and has low algorithm complexity.

• 방사선산업학회지
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• 제18권2호
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• pp.101-107
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• 2024

Rapid screening for internal contamination by alpha- and beta-emitting radionuclides is essential in situations involving radiation workers or radiation accidents. This study focused on the use of urine samples and liquid scintillation counting to quickly and accurately assess contamination. Calibration of the alpha and beta detection areas ensured precise measurement results. The major radionuclides recommended for surveillance during accidents were also considered. This study evaluated the effectiveness of the method by examining various parameters, including the limit of detection, linearity, sensitivity, selectivity, accuracy, ruggedness, and blind test sample analysis. The liquid scintillation counting method is an effective tool for screening urinary samples to detect alpha- and beta-emitting radionuclides, particularly during radiation emergencies, despite some limitations in precision.