• 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 Multimedia Society
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• v.22 no.4
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• pp.424-431
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• 2019

In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

• Journal of Power Electronics
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• v.10 no.2
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• pp.203-209
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• 2010

This paper describes the development of a three-phase line-interactive DVR with a new sag detection algorithm. The developed detection algorithm has a hybrid structure composed of an instantaneous detector and RMS-variation detectors. The source voltage passes through the sliding-window DFT and RMS calculator, and the instantaneous sag detector. If an instantaneous sag is detected, the RMS variation detector-1 is selected to calculate the RMS variation. The RMS variation detector-2 is selected when the instantaneous sag occurs under the operation of the RMS variation detector-1. The feasibility of the proposed algorithm is verified through computer simulations and experimental work with a prototype of a line-interactive DVR with a 3kVA rating. The line-interactive DVR with the proposed algorithm can compensate for an input voltage sag or an interruption within a 2ms delay. The developed DVR can effectively compensate for a voltage sag or interruption in sensitive loads, such as computers, communications equipment, and automation equipment.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.165-171
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• 2005

In this paper, the centralized dynamic load-balancing was processed effectively by using the network based parallel genetic algorithm. Unlike the existing method using genetic algorithm, the performance of central scheduler was improved by distributing the process for the searching of the optimal task assignment to clients. A roulette wheel selection and an elite preservation strategy were used as selection operation to improve the convergence speed of optimal solution. A chromosome was encoded by using sliding window method. And a cyclic crossover was used as crossover operation. By the result of simulation for the performance estimation of central scheduler according to the change of flexibility of load-balancing method, it was verified that the performance is improved in the proposed method.

• ETRI Journal
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• v.22 no.4
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• pp.40-50
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• 2000

We apply a "sliding-window" Maximum Likelihood(ML) estimator to estimate traffic parameters On-Off source and develop a method for estimating stochastic predicted individual cell arrival rates. Based on these results, we propose a simple Connection Admission Control(CAC)scheme for delay sensitive services in broadband onboard packet switching satellite systems. The algorithms are motivated by the limited onboard satellite buffer, the large propagation delay, and low computational capabilities inherent in satellite communication systems. We develop an algorithm using the predicted individual cell loss ratio instead of using steady state cell loss ratios. We demonstrate the CAC benefits of this approach over using steady state cell loss ratios as well as predicted total cell loss ratios. We also derive the predictive saturation probability and the predictive cell loss ratio and use them to control the total number of connections. Predictive congestion control mechanisms allow a satellite network to operate in the optimum region of low delay and high throughput. This is different from the traditional reactive congestion control mechanism that allows the network to recover from the congested state. Numerical and simulation results obtained suggest that the proposed predictive scheme is a promising approach for real time CAC.

• Proceedings of the KSRS Conference
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• v.1
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• pp.188-190
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• 2006

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The local segmentor of the first stage performs regiongrowing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. This stage uses a sliding window strategy with boundary blocking to alleviate a computational problem in computer memory for an enormous data. The global segmentor of the second stage has not spatial constraints for merging to classify the segments resulting from the previous stage. The experimental results show that the new approach proposed in this study efficiently performs the segmentation for the images of very large size and an extensive number of bands

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.1
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• pp.104-115
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• 1997

This paper investigates PN code acquistion algorithm and demodulation performance of the RAKE receiver in the DS/CDMA(direct sequence code division multiple access) sysytems under a multipath fading channel with multiusers. To speed up the acquisition process, PN matched filter is applied and postdetection integration technique comable withthe dynamic threshold set method is proposed. the Maximum-Likelihood algorithmin serial fashion is able to find PN code delay estimates for the RAKE branches using sliding window in a multipath fading channel. The correct acquistion probability and mean acquistion time are used as a performance measure of the system using the Monte Carlo method. The performance of the RAKEreceiver, afte the code acquisition is achieved is the CDMA systems, is also investigated for three major combining techniques.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1157-1158
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• 2023

자율주행 시스템에서 차선 인지는 주행의 성능과 안전에 중요한 역할을 한다. 차선 인지 분야에서는 다양한 알고리즘이 사용된다. 본 논문에서는 슬라이딩 윈도우 기법을 사용한 알고리즘을 기반으로, 더 정확하고 효율적인 차선 인지를 위한 개선 방안을 소개한다.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.3
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.