• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.517-524
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• 2020

Recently, many researches have been conducted to automatically recognize license plates of vehicles and use the analyzed information to manage stolen vehicles and track the vehicle. However, such a system must eventually be investigated by people through direct monitoring. Therefore, in this paper, the system of tracking a vehicle is implemented by sharing the information analyzed by the vehicle image among cameras registered in the IoT environment to minimize the human intervention. The distance between cameras is indicated by the node and the weight value of the weighted-graph, and the eigenvector centrality is used to select the camera to search. It demonstrates efficiency by comparing the time between analyzing data using weighted graph searching algorithm and analyzing all data stored in databse. Finally, the path of the vehicle is indicated on the map using parsed json data.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1E
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• pp.45-53
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• 2010

In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.

• Journal of Information Processing Systems
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• v.6 no.1
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• pp.79-90
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• 2010

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.2
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• pp.1-11
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• 2021

Salt and pepper is a type of impulse noise. It may appear due to an error in the image transmission process and image storage memory. This noise changes the pixel value at any position in the image to 0 (in case of pepper noise) or 255 (in case of salt noise). In this paper, we present an algorithm for SAP noise reduction. The proposed method consists of three steps. In the first step, the location of the SAP noise is detected, and in the second step, the pixel value of the detected location is restored using a weighted average of the surrounding pixel values. In the last step, a reliability matrix around the reconstructed pixels is constructed, and additional correction is performed with a weighted average using this. As a result of the experiment, the proposed method appears to have similar or higher objective and subjective image quality than previous methods for almost all SAP noise ratios.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.101-110
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• 1996

Color image quantization is a process of selecting a set of colors to display an image with some representative colors without noticeable perceived difference. It is very important in many applications to display a true color image in a low cost color monitor or printer. the basic problem is how to display 224 colors with 256 or less colors, called color palette. In this paper, we propose an algorithm to design the 256 or less size color palette by using spatial maskin geffect of HVS and subjective distortion measure weighted by color palette by using spatial masking effect of HVS and subjective distortion measure weighted by color activity in 4*4 local region in any color image. The proposed algorithm consists of octal prequantization and subdivision quantization processing step using the distortion measure and modified Otsu's between class variance maximization method. The experimental results show that the proposed algorithm has higher visual quality and needs less consuming time than conventional algorithms.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.4
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• pp.125-135
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• 1993

An Unrelated Parallel Processing with Weighted jobs and Setup times scheduling prolem is studied. We consider a parallel processing in which a group of processors(machines) perform a single operation on jobs of a number of different job types. The processing time of each job depends on both the job and the machine, and each job has a weight. In addition each machine requires significant setup time between processing jobs of different job types. The performance measure is to minimize total weighted flow time in order to meet the job importance and to minimize in-process inventory. We present a 0-1 Mixed Integer Programming model as an optimizing algorithm. We also present a simple heuristic algorithm. Computational results for the optimal and the heuristic algorithm are reported and the results show that the simple heuristic is quite effective and efficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1032-1041
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• 1999

This paper proposes on-line scheduling algorithms that reduce the largest weighted error incurred by preemptive imprecise tasks running on a single processor system. The first one is a two-level algorithm. The top-level scheduling, which is executed whenever a new task arrives, determines the processing times to be allotted to tasks in such a way to minimize maximum weighted error as well as to minimize total error. The lower-level algorithm actually allocates the processor to the tasks. The second algorithm extends the on-line algorithm studied by Shih and Liu[4] by formalizing the top-level algorithm mathematically. The numerical simulation shows that the proposed algorithm outperforms the previous works in the sense that it greatly reduces the largest weighted error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.45-52
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• 2008

In this paper, we describe the Perceptually Weighted Histogram(PWH) and the Gaussian Weighted Histogram Intersection(GWHI) algorithms. These algorithms are able to provide positive results in image retrieval. But these histogram methods alter the histogram of an image by using particular lighting conditions. Even two pictures with little differences in lighting are not easily matched. Therefore, we propose that the Histogram Matching Algorithm(HMA) is able to overcome the problem of an image being changed by the intensity or color in the image retrieval. The proposed algorithm is insensitive to changes in the lighting. From the experiment results, the proposed algorithm can achieve up to 32% and up to 30% more recall than the PWH and GWHI algorithms, respectively. Also, it can achieve up to 38% and up to 34% more precision than PWH and GWHI, respectively Therefore, with our experiments, we are able to show that the proposed algorithm shows limited variation to changes in lighting.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.8
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• pp.1755-1764
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• 1997

In this paper, we proposed the mechanism for the buffer allocation and a cell scheduling method with logical separation a single buffer in the ATm switch, and analyzed the cell loss probability and the delay of each trafic (CBR/VBR/ABR) based on the weighted value and the dynamic cell service scheduling algorithm. The proposed switch buffering system classifies composite trafics incoming to the switch, according to the characteristic of traffic, then stores them in the logically separated buffers, and adopts the round-robin service with weighted value in order to transmit cells in buffers though one output port. We analyzed 4 cell service scheduling algorithms with dynamic round-robinfor each logically separated service line of a single buffer, in which buffers have the respective weighted values and 3 classes on mixed traffic which characteristized by traffic descriptor. In simulation, using SIMCRIPT II.5., we model the VBR and the ABR traffics as ON/OFF processes, and the CBR traffic as a Poisson processes. As the results of analysis according to the proposed buffer management mechanism and cell service algorithm, we have found that the required QoS of each VC can be quaranteed depends on a scale of weighted values allocated to buffers that changed the weighted values, and cell scheduling algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.724-729
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• 2013

Recently, with the popularization of digital devices, the requirements of image quality is becoming higher and higher. However, the images are frequently corrupted in the image data processing, there are several reasons for this and the noise is considered as the main reason. Therefore, in order to alleviate the influence of AWGN(additive white Gaussian noise) in image, this paper puts forward the spatial weighted filtering algorithm. The algorithm set the weighted value according to the spatial distance, compared with the existing methods. The algorithm not only alleviated the influence of AWGN effectively but also reserved image details.