• 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

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1032-1035
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• 1996

While Ethernet and TCP/IP are the most widely used protocol, for Real-time system, it is not applicable because it doesn't guarantee the deterministic transmission time. Furthermore, the TCP acknowledgement scheme and sliding window algorithm enforce to collide packets. Although various Collision-Free CSMA protocol was presented, it is very difficult to implement in well known OS(UNIX, WilidowsNT) because we have to modify network kernel. This paper presents another transmission protocol based on modified UDP. The colliding probability can be minimized by avoiding successive packet transmission and decreasing competition duration. The proposed algorithm can be used for the soft real-time industrial automation network.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.506-509
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• 2005

An adaptive ${\alpha}-{\beta}$ tracker is proposed for tracking maneuvering targets with a track-while-scan radar system. The tracker gain is updated on-line corresponding to the adjusted process noise variance which is obtained via time averaging of the process over a sliding window. The adjusted process noise variance is used to compute the maneuverability index for the tracker gain based on the steady-state Kalman filter equation for each epoch. It is shown via simulation that the proposed approach provides robust and accurate position estimates during the target maneuver while the performance of the conventional ${\alpha}-{\beta}$ tracker is shown much degraded.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.122-125
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• 2008

Sensors used in many USN (Ubiquitous Sensor Network) domain applications generate a large amount of sensor stream data. The volume of sensor stream data is too huge to store the whole data and data speed is too fast to control each of them. In order to provide rapid and reliable context analysis service over sensor stream data, we propose a WHEN-DO context analysis model that supports the functionality of sliding window. This model is designed to be used as follows: If the sensor stream data satisfies condition in 'WHEN' clause, then it will execute actions in 'DO' clause in WHEN-DO context analysis model. The proposed WHEN-DO context analysis model can be applied to many other USN environment applications such as monitoring the status of a building and then taking actions in corresponding context condition.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.8
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• pp.19-30
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• 1995

For the efficient interconnection between SMDS and LAN, an interconnection protocol architecture in the router is proposed in this paper. A control method of xongestion which is produced by this interconnection of SMDS and LAN is also proposed. Especially, the SIP level 3 of SMDS is devided into CS-SIP3 sublayer and CLNAP sublayer in order to circumvent the problems which are producted by the protocol difference of two networks and to consider the interconnection with B-ISDN in the future. In this way, the interconnection of SMDS and LAN is transparentlly achieved through CLNAP layer, and the interconnection protocol architecture becomes simple. To test the performance of the router, amodel of interconnection protocol which is proposed by this paper is simulated using sliding window flow control. The simulation results show that the throughput of router is increased. The packet delay and the rate of packet discard are also decreased.

• 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 Korean Society of Computer Information Conference
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• 2018.07a
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• pp.364-366
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• 2018

본 논문에서는 Digital Twin 기반 건설기계 지능화를 위한 실시간 건설기계 데이터 처리 및 이상 유무 예측 시스템을 제안한다. 이 시스템은 빅 데이터 분산처리 기반으로 실시간 스트리밍 처리가 가능하며, CEP(Complex Event Processing)의 Sliding Window Operator를 활용한 Rule 적용을 통해 건설기계 데이터 처리 및 분석한다. 분석된 결과로 건설기계의 실시간 이상 유무를 판단할 수 있으며, 결과를 기반으로 Deep Learning 기술을 적용하고 학습된 모델을 통해 건설기계의 이상 유무를 예측하여 원활한 부품관리를 할 수 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.996-1002
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• 2015

In this paper, we present an effective system for the 3D scene labeling of objects from RGB-D videos. Our system uses a Markov Random Field (MRF) over a voxel representation of the 3D scene. In order to estimate the correct label of each voxel, the probabilistic graphical model integrates both scores from sliding window-based object detectors and also from object location prior maps. Both the object detectors and the location prior maps are pre-trained from manually labeled RGB-D images. Additionally, the model integrates the scores from considering the geometric constraints between adjacent voxels in the label estimation. We show excellent experimental results for the RGB-D Scenes Dataset built by the University of Washington, in which each indoor scene contains tabletop objects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4C
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• pp.330-335
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• 2008

This paper proposes a packet detection algorithm for IEEE802.11n system. IEEE802.11n is a multiple input multiple output (MIMO) system and we have to consider several combining techniques which are used in multiple receive antenna system. In this paper, we propose a hybrid packet detection algorithm which combines double sliding window algorithm or delay and correlation algorithm, that is used in single input single output (SISO) system, and multiple receive antenna combining algorithms, and simulated their performances in Iin system environments and shows the results.

• 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.