• Journal of Communications and Networks
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• 제18권5호
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• pp.818-825
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• 2016

Network management and anomaly detection are challenges in high-speed networks due to the high volume of packets that has to be analysed. Flow-based analysis is a scalable method which reduces the high volume of network traffic by dividing it into flows. As sampling methods are extensively used in flow generators such as NetFlow, the impact of sampling on the performance of flow-based analysis needs to be investigated. Monitoring using sampled traffic is a well-studied research area, however, the impact of sampling on flow-based anomaly detection is a poorly researched area. This paper investigates flow sampling methods and shows that these methods have negative impact on flow-based anomaly detection. Therefore, we propose an efficient probabilistic flow sampling method that can preserve flow traffic distribution. The proposed sampling method takes into account two flow features: Destination IP address and octet. The destination IP addresses are sampled based on the number of received bytes. Our method provides efficient sampled traffic which has the required traffic features for both flow-based anomaly detection and monitoring. The proposed sampling method is evaluated using a number of generated flow-based datasets. The results show improvement in preserved malicious flows.

• Journal of the Optical Society of Korea
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• 제20권6호
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• pp.745-751
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• 2016

A detection algorithm, based on the combined local-global (CLG) optical-flow model and Gaussian pyramid for a moving target appearing against a dynamic background, can compensate for the inadaptability of the classic Horn-Schunck algorithm to illumination changes and reduce the number of needed calculations. Incorporating the hypothesis of gradient conservation into the traditional CLG optical-flow model and combining structure and texture decomposition enable this algorithm to minimize the impact of illumination changes on optical-flow estimates. Further, calculating optical-flow with the Gaussian pyramid by layers and computing optical-flow at other points using an optical-flow iterative with higher gray-level points together reduce the number of calculations required to improve detection efficiency. Finally, this proposed method achieves the detection of a moving target against a dynamic background, according to the background motion vector determined by the displacement and magnitude of the optical-flow. Simulation results indicate that this algorithm, in comparison to the traditional Horn-Schunck optical-flow algorithm, accurately detects a moving target undergoing illumination changes against a dynamic background and simultaneously demonstrates a significant reduction in the number of computations needed to improve detection efficiency.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.119-126
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• 2003

The problem of tracking moving objects in a video stream is discussed in this pa-per. We discussed the popular technique of optical flow for moving object detection. Optical flow finds the velocity vectors at each pixel in the entire video scene. However, optical flow based methods require complex computations and are sensitive to noise. In this paper, we proposed a new method based on the Hough transform and on voting accumulation for improving the accuracy and reducing the computation time. Further, we applied the Boo-lean based edge detector for edge detection. Edge detection and segmentation are used to extract the moving objects in the image sequences and reduce the computation time of the CHT. The Boolean based edge detector provides accurate and very thin edges. The difference of the two edge maps with thin edges gives better localization of moving objects. The simulation results show that the proposed method improves the accuracy of finding the optical flow vectors and more accurately extracts moving objects' information. The process of edge detection and segmentation accurately find the location and areas of the real moving objects, and hence extracting moving information is very easy and accurate. The Combinatorial Hough Transform and voting accumulation based optical flow measures optical flow vectors accurately. The direction of moving objects is also accurately measured.

• 로봇학회논문지
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• 제5권2호
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• pp.85-92
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• 2010

In this paper, we propose a digital image stabilization technique using edge detection and Lucas-Kanade optical flow in order to minimize the motion of the shaken image. The accuracy of motion estimation based on block matching technique depends on the size of search window, which results in long calculation time. Therefore it is not applicable to real-time system. In addition, since the size of vector depends on that of block, it is difficult to estimate the motion which is bigger than the block size. The proposed method extracts the trust region using edge detection, to estimate the motion of some critical points in trust region based on Lucas-Kanade optical flow algorithm. The experimental results show that the proposed method stabilizes the shaking of motion image effectively in real time.

• Bulletin of the Korean Chemical Society
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• 제18권3호
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• pp.316-318
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• 1997

A general and universal detection method, which can be used in high performance liquid chromatography (HPLC) and flow injection analysis (FIA) system for the determination of any metal ions complexing with ethylenediaminetetraacetic acid (EDTA), is demonstrated. Pulsed amperometric detection scheme is applied in a flow-through thin layer electrochemical cell at an Au working electrode. Fluctuation of peak current level at the same flow rate of carrier solution is minimized at this solid working electrode, whereas not at a dropping mercury electrode. Removal of dissolved oxygen can be omitted with this detection method, which is a required step for cathodic detection methods. Also, a group of metal ions can be determined selectively and indirectly with this detection scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• 대한임베디드공학회논문지
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• 제11권5호
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• pp.277-285
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• 2016

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

• 센서학회지
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• 제29권3호
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• pp.194-200
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• 2020

Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.

• 농업과학연구
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• 제46권3호
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• pp.549-557
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• 2019

Staphylococcal enterotoxin is a very common cause of food poisoning. Conventional detection methods for the toxin including enzyme-linked immunosorbent assays (ELISAs), chemiluminescence (ECL), and polymerase chain reaction (PCR) assays require a lot of time, efforts, and expert technicians. Lateral flow strip kits have shown great potential for the rapid detection of foodborne pathogens. The lateral flow strip kit is widely used in clinical settings because it is easy to use, fast, and cost effective. A typical lateral flow strip kit uses colloidal gold to generate a visual signal. However, the lateral flow strip kit based on colloidal gold has limited sensitivity to fulfill food safety regulation requirements. This study was performed to develop a rapid test kit for pathogenic staphylococcal enterotoxin B (SEB) in food samples. The rapid detection kit was fabricated based on a nitrocellulose lateral-flow strip. Cellulose nano beads and SEB antibodies were used as the tag and receptor, respectively, to improve the detection performance. Manually spotted SEB antibody and anti-rabbit antibody on the surface of the nitrocellulose membrane were used as test and control spots, respectively. The feasibility of the rapid test kit to detect SEB in samples was evaluated. The sensitivity of the kit was 10 ng/mL SEB spiked in PBS. Additionally, the rapid test kit could detect 1 ng/mL of SEB in chicken meat extract.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.3162-3167
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• 2007

The OPS (Optical Particle Sensor) using light scattering from the particles (real-time measurement without physical contact to the particles) can be used for cleanroom or atmospheric environment monitoring. For particles smaller than 300 nm, the detection efficiency becomes lower as scattered light decreases with particle size. To obtain higher detection efficiency with small particles, the flow field in particle chamber and the detection volume should be designed optimally to achieve maximum scattered light from the particles. In this study, a commercial computational fluid dynamics software FLUENT was used to simulate the gas flow field and particle trajectories with various optical chamber designs for 300 nm PSL particle. For estimation of laser viewing volume, we used a commercial computational optical design program ZEMAX. The results will be a great help in the development of OPS which can measure small particles with higher detection efficiency.