• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.1-7
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• 2022

This paper aims to compare the mean object size of M/G/1/PS model with that of M/BP/1 model used in the web service. The mean object size is one of important measure to control and manage web service economically. M/G/1/PS model utilizes the processor sharing in which CPU rotates in round-robin order giving time quantum to multiple tasks. M/BP/1 model uses the Bounded Pareto distribution to describe the web service according to file size. We may infer that the mean waiting latencies of M/G/1/PS and M/BP/1 model are equal to the mean waiting latency of the deterministic model using the round robin scheduling with the time quantum. Based on the inference, we can find the mean object size of M/G/1/PS model and M/BP/1 model, respectively. Numerical experiments show that when the system load is smaller than the medium, the mean object sizes of the M/G/1/PS model and the M/BP/1 model become the same. In particular, when the shaping parameter is 1.5 and the lower and upper bound of the file size is small in the M/BP/1 model, the mean object sizes of M/G/1/PS model and M/BP/1 model are the same. These results confirm that it is beneficial to use a small file size in a web service.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.1-6
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• 2022

In this paper, we estimate the mean number of objects in the M/H₂/1 model for web service when the mean object size in the M/H₂/1 model is equal to that of the M/G/1/PS and M/BP/1 models. To this end, we use the mean object size obtained by assuming that the mean latency of deterministic model is equal to that of M/H₂/1, M/G/1/PS, and M/BP/1 models, respectively. Computational experiments show that if the shape parameter of the M/BP/1 model is 1.1 and the system load is greater than 0.35, the mean number of objects in the M/H₂/1 model when mean object size of M/H₂/1 model is the same as that of M/G/1/PS model is almost equal to the mean number of objects in the M/H₂/1 model when the mean object size of M/H₂/1 model is the same as that of M/BP/1 model. In addition, as the upper limit of the M/BP/1 model increases, the number of objects in the M/H₂/1 model converges to one, which increases latency. These results mean that it is efficient to use small-sized objects in the web service environment.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.1-6
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• 2023

One of the economical ways to satisfy the quality of service desired by the user in a web service environment is to adjust the size of the object. To this end, this study finds the maximum size of objects that satisfy this constraint when the mean response time is given below an arbitrary threshold for quality of service. It can be inferred that in the steady state of system, the mean response time in the deterministic model by using the round-robin will be the same as that of the queueing model following the general distribution. Based on this, analytical formulas and procedures for finding the maximum object size are obtained. As a service distribution of web traffic, the Pareto distribution is appropriate, so the maximum object size is computed by applying the M/G(Pareto)/1 model and the M/G/1/PS model using exponential distribution as computational experience. Performance evaluation through numerical calculation shows that as the shape parameter in the Pareto distribution increases, the M/G(Pareto)/1 model and M/G/1/PS model have the same maximum object size. The results of this study can be used to environments where objects can be sized for economical web service control.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.1-6
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• 2020

This paper aims to integrate the previous models about mean object transfer latency in one framework and analyze the result through the computational experience. The analytical object transfer latency model assumes the multiple packet losses and the Internet of Things(IoT) environment including multi-hop wireless network, where fast re-transmission is not possible due to small window. The model also considers the initial congestion window size and the multiple packet loss in one congestion window. Performance evaluation shows that the lower and upper bounds of the mean object transfer latency are almost the same when both transfer object size and packet loss rate are small. However, as packet loss rate increases, the size of the initial congestion window and the round-trip time affect the upper and lower bounds of the mean object transfer latency.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.138-148
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• 2015

Object detection methods based on background learning are widely used in video surveillance. However, when a car runs with headlights on, these methods are likely to detect the car region and the area illuminated by the headlights as one connected change region. This paper describes a method of separating the car region from the area illuminated by the headlights. First, we detect change regions with a background learning method, and extract blobs, connected components in the detected change region. If a blob is larger than the maximum object size, we extract candidate object regions from the blob by clustering the intensity histogram of the frame difference between the mean of background images and an input image. Finally, we compute the similarity between the mean of background images and the input image within each candidate region and select a candidate region with weak similarity as an object region.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2733-2737
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• 2014

Motion detection according to the movement and the change area detection method according to the background difference and the motion history image for use in a motion estimation technique using a real-time image, the motion detection method according to the optical flow, the back-projection of the histogram of the object to track for motion tracking At the heart of MeanShift center point of the object and the object to track, while used, the size, and the like due to the motion tracking algorithm CamShift, Kalman filter to track with direction. In this paper, we implemented the motion detection algorithm based on color and shape information of the object and verify.

• Journal of Internet of Things and Convergence
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• v.1 no.1
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• pp.1-4
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• 2015

This paper aims to present the mathematical model to find the mean web object transfer latency in the slow-start phase of TCP congestion control mechanism, which is one of the main control techniques of Internet. Mean latency is an important service quality measure of end-user in the network. The application area of the proposed latency model is the narrowband environment including multi-hop wireless network and Internet of Things(IoT), where packet loss occurs in the slow-start phase only due to small window. The model finds the latency considering initial window size and the packet loss rate. Our model shows that for a given packet loss rate, round trip time and initial window size mainly affect the mean web object transfer latency. The proposed model can be applied to estimate the mean response time that end user requires in the IoT service applications.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.123-128
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• 2018

In this paper, we propose a multi-vehicle object detection algorithm using feature point matching that tracks efficient vehicle objects. The proposed algorithm extracts the feature points of the vehicle using the FAST algorithm for efficient vehicle object tracking. And True if the feature points are included in the image segmented into the 5X5 region. If the feature point is not included, it is processed as False and the corresponding area is blacked to remove unnecessary object information excluding the vehicle object. Then, the post processed area is set as the maximum search window size of the vehicle. And A minimum search window using the outermost feature points of the vehicle is set. By using the set search window, we compensate the disadvantages of the search window size of mean-shift algorithm and track vehicle object. In order to evaluate the performance of the proposed method, SIFT and SURF algorithms are compared and tested. The result is about four times faster than the SIFT algorithm. And it has the advantage of detecting more efficiently than the process of SUFR algorithm.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.210-212
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• 2009

In object tracking based on the particle filter algorithm controlling the proper distribution of the samples is essential to accurately track the target. If the samples are spread too wide compared to the target size, the tracking accuracy may degrade as some samples can be caught by background clutters that is similar to the target. On the other hands if the samples are spread too narrow, the particle filter may fail to track the abrupt motion of the target. To solve this problem we propose an improved particle filter that adopts "re-weighting" technique at the observe step. We estimate the distribution of the weights of the current samples by its mean and variance. Then the samples are re-weighted so that the appropriate distribution of the samples in proportional to the target scale is obtained at the next select step. The proposed tracking method can avoid convergence to local mean and improve the accuracy of the estimated target state.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.9_10
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• pp.1172-1177
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• 2003

Microcapsules were prepared by complex coacervation between gelatin and gum arabic. The object of this work is evaluation of the effect of jelly strength, hardening agent on the particle size distribution, surface morphology and DSC. It was found that the 300bloom jelly strength caused microcapsules' size larger. When the amount of hardening agent increased, the particle mean diameter was larger. The amount of hardening agent was determined to be 10m1 for getting suitable size to finish the fabric.