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  • Title/Summary/Keyword: multiple target detection

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Region Defense Technique Using Multiple Satellite Navigation Spoofing Signals

  • Lee, Chi-Hun;Choi, Seungho;Lee, Young-Joong;Lee, Sang Jeong
    • Journal of Positioning, Navigation, and Timing
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    • v.11 no.3
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    • pp.173-179
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    • 2022
  • The satellite navigation deception technology disturbs the navigation solution of the receiver by generating a deceptive signal simulating the actual satellite for the satellite navigation receiver mounted on the unmanned aerial vehicle, which is the target of deception. A single spoofing technique that creates a single deceptive position and velocity can be divided into a synchronized spoofing signal that matches the code delay, Doppler frequency, and navigation message with the real satellite and an unsynchronized spoofing signal that does not match. In order to generate a signal synchronized with a satellite signal, a very sophisticated and high precision signal generation technology is required. In addition, the current position and speed of the UAV equipped with the receiver must be accurately detected in real time. Considering the detection accuracy of the current radar technology that detects small UAVs, it is difficult to detect UAVs with an accuracy of less than one chip. In this paper, we assume the asynchrony of a single spoofing signal and propose a region defense technique using multiple spoofing signals.

Enhancing Automated Multi-Object Tracking with Long-Term Occlusions across Consecutive Frames for Heavy Construction Equipment

  • Seongkyun AHN;Seungwon SEO;Choongwan KOO
    • International conference on construction engineering and project management
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    • 2024.07a
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    • pp.1311-1311
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    • 2024
  • Recent advances in artificial intelligence technology have led to active research aimed at systematically managing the productivity and environmental impact of major management targets such as heavy equipment at construction sites. However, challenges arise due to phenomena like partial occlusions, resulting from the dynamic working environment of construction sites (e.g., equipment overlapping, obstruction by structures), which impose practical constraints on precisely monitoring heavy equipment. To address these challenges, this study aims to enhance automated multi-object tracking (MOT) in scenarios involving long-term occlusions across consecutive frames for heavy construction equipment. To achieve this, two methodologies are employed to address long-term occlusions at construction sites: (i) tracking-by-detection and (ii) video inpainting with generative adversarial networks (GANs). Firstly, this study proposes integrating FairMOT with a tracking-by-detection algorithm like ByteTrack or SMILEtrack, demonstrating the robustness of re-identification (Re-ID) in occlusion scenarios. This method maintains previously assigned IDs when heavy equipment is temporarily obscured and then reappears, analyzing location, appearance, or motion characteristics across consecutive frames. Secondly, adopting video inpainting with GAN algorithms such as ProPainter is proposed, demonstrating robustness in removing objects other than the target object (e.g., excavator) during the video preprocessing and filling removed areas using information from surrounding pixels or other frames. This approach addresses long-term occlusion issues by focusing on a single object rather than multiple objects. Through these proposed approaches, improvements in the efficiency and accuracy of detection, tracking, and activity recognition for multiple heavy equipment are expected, mitigating MOT challenges caused by occlusions in dynamic construction site environments. Consequently, these approaches are anticipated to play a significant role in systematically managing heavy equipment productivity, environmental impact, and worker safety through the development of advanced construction and management systems.

Improvement of KOMPSAT-5 Image Resolution for Target Analysis (객체 분석을 위한 KOMPSAT-5 영상의 해상도 향상 성능 분석)

  • Lee, Seung-Jae;Chae, Tae-Byeong
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.30 no.4
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    • pp.275-281
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    • 2019
  • A synthetic aperture radar(SAR) satellite is more effective than an optical satellite for target analysis because an SAR satellite can provide two-dimensional electromagnetic scattering distribution of a target during all-weather and day-and-night operations. To conduct target analysis while considering the earth observation interval of an SAR satellite, observing a specific area as wide as possible would be advantageous. However, wider the observation area, worse is the resolution of the associated SAR satellite image. Although conventional methods for improving the resolution of radar images can be employed for addressing this issue, few studies have been conducted for improving the resolution of SAR satellite images and analyzing the performance. Hence, in this study, the applicability of conventional methods to SAR satellite images is investigated. SAR target detection was first applied to Korea Multipurpose Satellite-5(KOMPSAT-5) SAR images provided by Korea Aerospace Research Institute for extracting target responses. Extrapolation, RELAX, and MUSIC algorithms were subsequently applied to the target responses for improving the resolution, and the corresponding performance was thereby analyzed.

A Study on Multiple Target Tracking Using Self-Organizing Neural Network (자기조직화 신경망을 이용한 다중 표적 추적에 관한 연구)

  • 서창진;김광백
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.7 no.6
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    • pp.1304-1311
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    • 2003
  • Target tracking in a real world situation is difficult problem because of continuous variations in images, huge amounts of data, and high processing speed demands. The problem becomes even harder in the case of sea background. This paper presents an initial study of neural network based method for target detection and tracking in cluttering environment. The approach uses a combination of differential motion analysis, Kohonen self-organizing network and region growing method. The network is capable of detecting the mass-centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment showed promising results.

K-Band Radar Development for the Ground Moving Vehicle (지상 이동 차량용 K-대역 레이다 개발)

  • Lee, Jong-Min;Cho, Byung-Lae;Sun, Sun-Gu;Lee, Jung-Soo;Park, Sang-Soon
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.22 no.3
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    • pp.362-370
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    • 2011
  • This paper presents a K-band radar system installed on the ground moving vehicle to detect and track a high-speed target. The presented radar is separated into three search regions to satisfy a wide area detection and a limitation of the installing space of the radar, and each region performs detecting the target independently and tracking the detected target automatically. The presented radar radiating K-band FMCW waveform acquires range and velocity information of the target at the every dwell and receiving antenna of the radar is applied the multiple baseline interferometer to extract the precise angle information of the target. 3-dimensional tracking accuracy of the radar is 0.25 m RMSE measured actually through a fire experiment of an imitation target.

Acceleration-based neural networks algorithm for damage detection in structures

  • Kim, Jeong-Tae;Park, Jae-Hyung;Koo, Ki-Young;Lee, Jong-Jae
    • Smart Structures and Systems
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    • v.4 no.5
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    • pp.583-603
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    • 2008
  • In this study, a real-time damage detection method using output-only acceleration signals and artificial neural networks (ANN) is developed to monitor the occurrence of damage and the location of damage in structures. A theoretical approach of an ANN algorithm that uses acceleration signals to detect changes in structural parameters in real-time is newly designed. Cross-covariance functions of two acceleration responses measured before and after damage at two different sensor locations are selected as the features representing the structural conditions. By means of the acceleration features, multiple neural networks are trained for a series of potential loading patterns and damage scenarios of the target structure for which its actual loading history and structural conditions are unknown. The feasibility of the proposed method is evaluated using a numerical beam model under the effect of model uncertainty due to the variability of impulse excitation patterns used for training neural networks. The practicality of the method is also evaluated from laboratory-model tests on free-free beams for which acceleration responses were measured for several damage cases.

Naval ship's susceptibility assessment by the probabilistic density function

  • Kim, Kwang Sik;Hwang, Se Yun;Lee, Jang Hyun
    • Journal of Computational Design and Engineering
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    • v.1 no.4
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    • pp.266-271
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    • 2014
  • The survivability of the naval ship is the capability of a warship to avoid or withstand a hostile environment. The survivability of the naval ship assessed by three categories (susceptibility, vulnerability and recoverability). The magnitude of susceptibility of a warship encountering with threat is dependent upon the attributes of detection equipment and weapon system. In this paper, as a part of a naval ship's survivability analysis, an assessment process model for the ship's susceptibility analysis technique is developed. Naval ship's survivability emphasizing the susceptibility is assessed by the probability of detection, and the probability of hit. Considering the radar cross section (RCS), the assessment procedure for the susceptibility is described. It's emphasizing the simplified calculation model based on the probability density function for probability of hit. Assuming the probability of hit given a both single-hit and multiple-hit, the susceptibility is accessed for a RCS and the hit probability for a rectangular target is applied for a given threat.

Design and Implementation of A Scan Detection Management System with real time Incidence Response (실시간 e-mail 대응 침입시도탐지 관리시스템의 설계 및 구현)

  • Park, Su-Jin;Park, Myeong-Chan;Lee, Sae-Sae;Choe, Yong-Rak
    • The KIPS Transactions:PartC
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    • v.9C no.3
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    • pp.359-366
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    • 2002
  • Nowadays, the hacking techniques are developed increasingly with wide use of internet. The recent type of scanning attack is appeared in against with multiple target systems on the large scaled domain rather than single network of an organization. The development of scan detection management system which can detect and analyze scan activities is necessary to prevent effectively those attacking at the central system. The scan detection management system is useful for effective utilization of various detection information that received from scan detection agents. Real time scan detection management system that can do the integrated analysis of high lever more that having suitable construction in environment of large scale network is developed.

A Method to Monitor Dutasteride in Rat Plasma Using Liquid-Liquid Extraction and Multiple Reaction Monitoring: Comparisons and Validation

  • Kang, Myung Joo;Cho, Ha Ra;Lee, Dong Hoon;Yeom, Dong Woo;Choi, Young Wook;Choi, Yong Seok
    • Mass Spectrometry Letters
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    • v.5 no.3
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    • pp.79-83
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    • 2014
  • Three different dutasteride extraction methods were compared and a method based on liquid-liquid extraction (LLE) using methyl tert-butyl ether and methylene chloride was proved to be more effective than others for the extraction of dutasteride and finasteride, the internal standard (IS), from rat plasma. Additionally, a method composed of the LLE extraction, liquid chromatography, and multiple reaction monitoring (MRM) to target dutasteride and IS was validated by assessing specificity, linearity (r2 = 0.9993, 5 - 400 ng/mL), sensitivity (the limit of detection: 4.03 ng/mL; the limit of quantitation: 12.10 ng/mL), accuracy (intra-day: 89.4 - 105.9%; inter-day: 84.9 - 100.9%), precision (intra-day: 0.8 - 6.9%; inter-day: 2.9 - 15.9%), and recovery (84.7 - 107.8%). Since the validated method was successfully applied to a pharmacokinetic study of dutasteride, it can be useful for the pharmacokinetic evaluation of newly developed dutasteride formulations.

Real-time Multiple Pedestrians Tracking for Embedded Smart Visual Systems

  • Nguyen, Van Ngoc Nghia;Nguyen, Thanh Binh;Chung, Sun-Tae
    • Journal of Korea Multimedia Society
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    • v.22 no.2
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    • pp.167-177
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    • 2019
  • Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.