• Title/Summary/Keyword: NST algorithm

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Design of Asynchronous 16-Bit Divider Using NST Algorithm (NST알고리즘을 이용한 비동기식 16비트 제산기 설계)

  • 이우석;박석재;최호용
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.40 no.3
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    • pp.33-42
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    • 2003
  • This paper describes an efficient design of an asynchronous 16-bit divider using the NST (new Svoboda-Tung) algorithm. The divider is designed to reduce power consumption by using the asynchronous design scheme in which the division operation is performed only when it is requested. The divider consists of three blocks, i.e. pre-scale block, iteration step block, and on-the-fly converter block using asynchronous pipeline structure. The pre-scale block is designed using a new subtracter to have small area and high performance. The iteration step block consists of an asynchronous ring structure with 4 division steps for area reduction. In other to reduce hardware overhead, the part related to critical path is designed by a dual-rail circuit, and the other part is done by a single-rail circuit in the ring structure. The on-the-fly converter block is designed for high performance using the on-the-fly algorithm that enables parallel operation with iteration step block. The design results with 0.6${\mu}{\textrm}{m}$ CMOS process show that the divider consists of 12,956 transistors with 1,480 $\times$1,200${\mu}{\textrm}{m}$$^2$area and average-case delay is 41.7㎱.

A Study on Development of Automatic Westing Software by Vectorizing Technique (벡터라이징을 이용한 자동부재배치 소프트웨어 개발에 관한 연구)

  • Lho T.J.;Kang D.J.;Kim M.S.;Park Jun-Yeong;Park S.W.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2005.10a
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    • pp.748-753
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    • 2005
  • Among processes to manufacture parts from footwear materials like upper leathers, one of the most essential processes is the cutting one optimally arranging lots of parts on raw footwear materials and cutting. A new nesting strategy was proposed for the 2-dimensional part layout by using a two-stage approach, where which can be effectively used for water jet cutting. In the initial layout stage, a SOAL(Self-Organization Assisted Layout) based on the combination of FCM(Fuzzy C-Means) and SOM was adopted. In the layout improvement stage, SA(Simulated Annealing) based approach was adopted for a finer layout. The proposed approach saves much CPU time through a two-stage approach scheme, while other annealing-based algorithm so far reported fur a nesting problem are computationally expensive. The proposed nesting approach uses the stochastic process, and has a much higher possibility to obtain a global solution than the deterministic searching technique. We developed the automatic nesting software of NST(ver.1.1) software for footwear industry by implementing of these proposed algorithms. The NST software was applied by the optimized automatic arrangement algorithm to cut without the loss of leathers. if possible, after detecting damage areas. Also, NST software can consider about several features in not only natural loathers but artificial ones. Lastly, the NST software can reduce a required time to implement generation of NC code. cutting time, and waste of raw materials because the NST software automatically performs parts arrangement, cutting paths generation and finally NC code generation, which are needed much effect and time to generate them manually.

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Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO2/LiFePO4/LiNiMnCoO2 Cells

  • Lee, Hyun-jun;Park, Joung-hu;Kim, Jonghoon
    • Journal of Electrical Engineering and Technology
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    • v.11 no.6
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    • pp.1664-1673
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    • 2016
  • Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative $LiCoO_2/LiFePO_4/LiNiMnCoO_2$ cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the $LiFePO_4$ cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

A Fast Block Matching Motion Estimation Algorithm by using the Enhanced Cross-Hexagonal Search Pattern (개선된 크로스-육각 패턴을 이용한 고속 블록 정합 움직임 추정 알고리즘)

  • Nam Hyeon-Woo
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.4 s.42
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    • pp.77-85
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    • 2006
  • There is the spatial correlation of the video sequence between the motion vector of current blocks. In this paper, we propose the enhanced fast block matching algorithm using the spatial correlation of the video sequence and the center-biased properly of motion vectors. The proposed algorithm determines an exact motion vector using the predicted motion vector from the adjacent macro blocks of the current frame and the Cross-Hexagonal search pattern. From the of experimental results, we can see that our proposed algorithm outperforms both the prediction search algorithm (NNS) and the fast block matching algorithm (CHS) in terms of the search speed and the coded video's quality. Using our algorithm, we can improve the search speed by up to $0.1{\sim}38%$ and also diminish the PSNR (Peak Signal Noise Ratio) by at nst $0.05{\sim}2.5dB$, thereby improving the video qualify.

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