• Title/Summary/Keyword: Convergence Window System

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The Method of Reducing the Delay Latency to Improve the Efficiency of Power Consumption in Wireless Sensor Networks

  • Ho, Jang;Son, Jeong-Bong
    • 한국정보컨버전스학회:학술대회논문집
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    • 2008.06a
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    • pp.199-204
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    • 2008
  • Sensor nodes have various energy and computational constraints because of their inexpensive nature and ad-hoc method of deployment. Considerable research has been focused at overcoming these deficiencies through faster media accessing, more energy efficient routing, localization algorithms and system design. Our research attempts to provide a method of improvement MAC performance in these issues. We show that traditional carrier-sense multiple access(CSMA) protocols like IEEE 802.11 do not handle the first constraint adequately, and do not take advantage of the second property, leading to degraded latency and throughput as the network scales in size, We present more efficient method of a medium access for real-time wireless sensor networks. Proposed MAC protocol is a randomized CSMA protocol, but unlike previous legacy protocols, does not use a time-varying contention window from which a node randomly picks a transmission slot. To reduce the latency for the delivery of event reports, it carefully decides a fixed-size contention window, non-uniform probability distribution of transmitting in each slot within the window. We show that it can offer up to several times latency reduction compared to legacy of IEEE 802.11 as the size of the sensor network scales up to 256 nodes using widely used simulator ns-2. We, finally show that proposed MAC scheme comes close to meeting bounds on the best latency achievable by a decentralized CSMA-based MAC protocol for real-time wireless sensor networks which is sensitive to latency.

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Comparison of System Call Sequence Embedding Approaches for Anomaly Detection (이상 탐지를 위한 시스템콜 시퀀스 임베딩 접근 방식 비교)

  • Lee, Keun-Seop;Park, Kyungseon;Kim, Kangseok
    • Journal of Convergence for Information Technology
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    • v.12 no.2
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    • pp.47-53
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    • 2022
  • Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

A Study on Micro Drill-Bit Measurement Using Images (영상을 이용한 미세 드릴비트 측정에 관한 연구)

  • Kwak, Dong-gyu;Choi, Han-go
    • Journal of the Institute of Convergence Signal Processing
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    • v.16 no.3
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    • pp.90-95
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    • 2015
  • This study presents a method to test quite small-sized and light-weighted micro-drill bits which are used to make holes in printed circuit boards(PCB). After getting images of micro-drill bits through the high resolution microscope, we developed image processing algorithms to detect fiducial points, and then measured diverse factors of the drill-bit based on these points. We also developed the window-based inspection system to automatically discriminate normal and abnormal status. For the relative comparison of its performance, the system was compared with an existing inspection system using test images. Experimental results showed that the proposed system slightly improved performance, and also classified correctly some misjudged errors which were occurred in the existing system.

Detection of High Impedance Fault Using Adaptive Neuro-Fuzzy Inference System (적응 뉴로 퍼지 추론 시스템을 이용한 고임피던스 고장검출)

  • 유창완
    • Journal of the Korean Institute of Intelligent Systems
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    • v.9 no.4
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    • pp.426-435
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    • 1999
  • A high impedance fault(HIF) is one of the serious problems facing the electric utility industry today. Because of the high impedance of a downed conductor under some conditions these faults are not easily detected by over-current based protection devices and can cause fires and personal hazard. In this paper a new method for detection of HIF which uses adaptive neuro-fuzzy inference system (ANFIS) is proposed. Since arcing fault current shows different changes during high and low voltage portion of conductor voltage waveform we firstly divided one cycle of fault current into equal spanned four data windows according to the mangnitude of conductor voltage. Fast fourier transform(FFT) is applied to each data window and the frequency spectrum of current waveform are chosen asinputs of ANFIS after input selection method is preprocessed. Using staged fault and normal data ANFIS is trained to discriminate between normal and HIF status by hybrid learning algorithm. This algorithm adapted gradient descent and least square method and shows rapid convergence speed and improved convergence error. The proposed method represent good performance when applied to staged fault data and HIFLL(high impedance like load)such as arc-welder.

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Design and Implementation of Forensic Tool on Window Live System (윈도우 활성 시스템상의 디지털 증거 수집 도구 설계 및 구현)

  • Baek, Eun-Ju;Sung, Jin-Won;Lim, Kyoung-Su;Lee, Sang-Jin
    • Convergence Security Journal
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    • v.7 no.2
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    • pp.91-100
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    • 2007
  • Nowadays, there exist many forensic tools in forensic investigation. For common investigator it may cause some difficulty in handling the existing forensic tools. In case of urgent condition, if it takes long time to get the useful evidence from data, then it makes the investigation process difficult. Thus, the common investigator can collect the evidence easily by simple clicking the mouse. The only thing he needs is a tool for examination before investigating in details. Therefore, in this paper we refer to useful information in the forensic investigation, discuss the design and the implementation of tool.

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System Design and Implementation for New Move Picture Solution EZ-MOV Using FLV (FLV를 이용한 새로운 동명상 솔루션 EZ-MOV 대한 시스템 설계 및 구현)

  • Kwon, O-Byoung;Shin, Hyun-Cheul
    • Convergence Security Journal
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    • v.9 no.2
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    • pp.79-84
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    • 2009
  • Recently, Move Picture Files have the same file format and a compression technique as Window Media Video form. but Moving Pictures using file format and a compression technique have question about Motion blur and compressibility. In this paper, we design and Implement for new Move Picture Solution EZ-MOV using FLV different from developed FLV(Flash Video) in the Macromedia company. EZ-MOV have advantages as follow. first, FLV player is able to compact disk access time and DRM (Digital Rights Management) with a built-in self and unable to an illegal video recording, second, whenever WMV formal file encoded FLV are able to lossless compression to fifty percent, third, FLV is able to Moving Picture streaming no buffering. fourth, FLV file is able streaming service no streaming server. fifth, FLV file is able to streaming service keep pace with download and streaming. sixth, FLV file is able to full duplex service.

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Computer Vision Based Measurement, Error Analysis and Calibration (컴퓨터 시각(視覺)에 의거한 측정기술(測定技術) 및 측정오차(測定誤差)의 분석(分析)과 보정(補正))

  • Hwang, H.;Lee, C.H.
    • Journal of Biosystems Engineering
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    • v.17 no.1
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    • pp.65-78
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    • 1992
  • When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

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A Study on the Stabilization of a System for Big Data Transmission of Intelligent Ventilation Window based on Sensor and MCU (센서 및 MCU기반 지능형 환기창 빅데이터전송용 시스템 안정화에 관한 연구)

  • Ryoo, Hee-Soo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.16 no.3
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    • pp.551-558
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    • 2021
  • In this paper, we made the integrated intelligent air ventilation of the actuator module that can be remotely controlled based on IoT and sensors. we implemented a ventilation window system by configuring an algorithm design and a driving circuit to control the operation of the actuator to open and close the ventilation port based on a predetermined number of data that detects indoor gas/CO2/humidity temperature and outdoor fine dust related indoor/outdoor environment. It is difficult to store, manage, and analyze data due to the large number of sensors and conditions for the transmission data of indoor air circulation module. The remote monitoring and remote wireless control screens were constructed to automate the separation and operation conditions by extracting and managing the state. We apply MQTT to enhance big data transmission and construct the system using Rocket MQ to ensure safe transmission of operational big data against system errors.

Window Production Method based on Low-Frequency Detection for Automatic Object Extraction of GrabCut (GrabCut의 자동 객체 추출을 위한 저주파 영역 탐지 기반의 윈도우 생성 기법)

  • Yoo, Tae-Hoon;Lee, Gang-Seong;Lee, Sang-Hun
    • Journal of Digital Convergence
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    • v.10 no.8
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    • pp.211-217
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    • 2012
  • Conventional GrabCut algorithm is semi-automatic algorithm that user must be set rectangle window surrounds the object. This paper studied automatic object detection to solve these problem by detecting salient region based on Human Visual System. Saliency map is computed using Lab color space which is based on color opposing theory of 'red-green' and 'blue-yellow'. Then Saliency Points are computed from the boundaries of Low-Frequency region that are extracted from Saliency Map. Finally, Rectangle windows are obtained from coordinate value of Saliency Points and these windows are used in GrabCut algorithm to extract objects. Through various experiments, the proposed algorithm computing rectangle windows of salient region and extracting objects has been proved.

Dispersion Managed Optical Transmission Links with Optimized Optical Phase Conjugator

  • Lee, Seong-Real
    • Journal of information and communication convergence engineering
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    • v.7 no.3
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    • pp.372-376
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    • 2009
  • In this paper, new and simple optical transmission link with fixed dispersion management (DM) scheme, i.e., pre(post) compensation and residual dispersion per span (RDPS) are fixed to net residual dispersion (NRD) = 0 ps/nm, and optical phase conjugator (OPC) having optimal position depending on launch power in WDM transmission system is proposed. Also, effective launch power range of WDM channels resulting 1 dB eye opening penalty (EOP) is induced as a function of OPC position. First, it is confirmed that, for applying DM into WDM transmission link fixed pre(post)compensation and RDPS, which are independence on exact system parameters except launch power, sufficiently are used in WDM links, but OPC with optimal position is needed for effective compensating impairments of WDM channels. And, it is confirmed that effective launch power is broader in case of RDPS = 100 ps/nm than in RDPS = 50 ps/nm. But, it is shown that the best OPC position offset is -0.6 km from a point of view of power window, which is defined as difference between maximum and minimum effective launch power.