• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.62-69
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• 2011

Development of sensing technology and progress of digital media have been creating new art genre named interactive media art. digital mirror working based on convergence between computer vision technology and video art, is expressing reconstituted spectator's visual image through various mediums. From this aesthetical point and high accessibility towards spectators, many types of digital mirrors have been introducing. However, the majority of digital mirrors express visual images unrelated to degree of spectator's participation and this caused obstruction to spectator's continuous participation and interaction. This paper proposes digital mirror operated by spectator's movements read through particle system synchronized with motion detection algorithm based on analyzing image difference. This work extracted the data of spectator's movement by image processing and designed particle system changed by this data. And it expressed reconstructed spectator's image.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.421-429
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• 2015

This paper is about an approach for pedestrian detection and tracking with infrared imagery. We used the CUDA(Computer Unified Device Architecture) that is a parallel processing language in order to improve the speed of video-based pedestrian detection and tracking. The detection phase is performed by Adaboost algorithm based on Haar-like features. Adaboost classifier is trained with datasets generated from infrared images. After detecting the pedestrian with the Adaboost classifier, we proposed a particle filter tracking strategies on HSV histogram feature that exploit adaptively at the same time. The proposed approach is implemented on an NVIDIA Jetson TK1 developer board that is full-featured device ideal for software development within the Linux environment. In this paper, we presented the results of parallel processing with the NVIDIA GPU on the CUDA development environment for detection and tracking of pedestrians. We compared the object detection and tracking processing time for night-time images on both GPU and CPU. The result showed that the detection and tracking speed of the pedestrian with GPU is approximately 6 times faster than that for CPU.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4658-4679
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• 2017

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.183-188
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• 2015

In this paper, it is proposed to detect and track pedestrian and analyse tracking performance with nighttime CCTV video. The detection is performed by a cascade classifier with Haar-like feature trained with Adaboost algorithm. Tracking pedestrian is performed by a particle filter. As results of experiments, it is introduced that efficient number of particles and the distributions are applied to track pedestrian at the night-time. Performance of detection and tracking is verified with nighttime CCTV video that is obtained at alleys etc.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.3
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• pp.323-331
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• 2012

A simple and robust methodology is presented to determine the location and amount of crack in beam like structures based on the incremental particle swarm optimization technique. A comparison is made for assessing the performance of standard particle swarm optimization and the incremental particle swarm optimization technique for detecting crack in structural members. The objective function is formulated using the measured natural frequency of the intact structure and the frequency obtained from the finite element simulation. The outcomes of the simulated results demonstrate that the developed method is capable of detecting and estimating the extent of damages with satisfactory precision.

• Structural Engineering and Mechanics
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• v.84 no.4
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.

• Mass Spectrometry Letters
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• v.2 no.2
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• pp.57-60
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• 2011

As background significantly affects measurement accuracy and a detection limit in determination of the trace amounts of uranium, it is necessary to determine the impurities in the Lexan detector film for single particle measurements by thermal ionization mass spectrometry coupled with fission track technique (FT-TIMS). We have prepared various micro sizes of the blank Lexan detector film using a micromanipulation technique for uranium measurements by TIMS. Few tens of fg of uranium background with no remarkable dependency on the film sizes were observed in the blank Lexan films with the sizes from $50{\times}50\;{\mu}m^2$ to $300{\times}300\;{\mu}m^2$. Based on the determination of the uranium background in the Lexan film, any background correction is necessary in the isotopic analysis of a uranium single particle with micron sizes when the particle bearing Lexan film is dissected with less than $300{\times}300\;{\mu}m^2$ size. The isotopic analysis of a uranium particle in U030 standard material using TIMS was carried out to verify the applicability of the Lexan film to the single particle analysis with high accuracy and precision.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.255-260
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• 2006

Safety-critical navigation systems have to provide 'reliable' position solutions, i.e., they must detect and exclude measurement or system faults and estimate the uncertainty of the solution. To obtain more accurate and reliable navigation systems, various filtering methods have been employed to reduce measurement noise level, or integrate sensors, such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Recently, particle filters have attracted attention, because they can deal with nonlinear/non-Gaussian systems. In most GNSS applications, the GNSS measurement noise is assumed to follow a Gaussian distribution, but this is not true. Therefore, we have proposed a fault detection and exclusion method using particle filters assuming non-Gaussian measurement noise. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian noise. Since the Kalman filters presume that measurement noise follows a Gaussian distribution, they used an overbounded standard deviation to represent the measurement noise distribution, and since the overbound standard deviations were too conservative compared to the actual distributions, this degraded the integrity-monitoring performance of the filters. A simulation was performed to show the improvement in performance of our proposed particle filter method by not using the sigma overbounding. The results show that our method could detect smaller measurement biases and reduced the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual noise model instead of the overbounding or improve the overbounding methods.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.325-328
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• 2001

Gelatin particle agglutination test (GPAT) was used to detect Cymbidum mosaic virus (CymMV) in Cattleya plants. Gelatin particles were coated with purified anti-CymMV immunoglobulin of 25-100 $\mu\textrm{g}$/ml and were subjected to several different concentrations of purified CyMfV as well as varying dilutions of orchid leaf extracts. The GPAT detected purified CymMV up to a minimum concentration of 10 $\mu\textrm{g}$/ml. CymMV was detected from crude sap extract of infected Cattleya leaves and roots up to 1:51,200 and 1:25,600 dilutions, respectively. However, the optimum range of leaf and root sap dilutions was between 50-100. Non-specific reactions were not encountered from any of the healthy orchid plants tested. The entire GPAT process was completed within 2-3 hours. This test was found to be very useful for the detection of CymMV in orchids because it is sensitive, economical, and easy to perform.