• 제목/요약/키워드: Auxiliary information

검색결과 524건 처리시간 0.03초

ZVT Series Capacitor Interleaved Buck Converter with High Step-Down Conversion Ratio

  • Chen, Zhangyong;Chen, Yong;Jiang, Wei;Yan, Tiesheng
    • Journal of Power Electronics
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    • 제19권4호
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    • pp.846-857
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    • 2019
  • Voltage step-down converters are very popular in distributed power systems, voltage regular modules, electric vehicles, etc. However, a high step-down voltage ratio is required in many applications to prevent the traditional buck converter from operating at extreme duty cycles. In this paper, a series capacitor interleaved buck converter with a soft switching technique is proposed. The DC voltage ratio of the proposed converter is half that of the traditional buck converter and the voltage stress across the one main switch and the diodes is reduced. Moreover, by paralleling the series connected auxiliary switch and the auxiliary inductor with the main inductor, zero voltage transition (ZVT) of the main switches can be obtained without increasing the voltage or current stress of the main power switches. In addition, zero current turned-on and zero current switching (ZCS) of the auxiliary switches can be achieved. Furthermore, owing to the presence of the auxiliary inductor, the turned-off rate of the output diodes can be limited and the reverse-recovery switching losses of the diodes can be reduced. Thus, the efficiency of the proposed converter can be improved. The DC voltage gain ratio, soft switching conditions and a design guideline for the critical parameters are given in this paper. A loss analysis of the proposed converter is shown to demonstrate its advantages over traditional converter topologies. Finally, experimental results obtained from a 100V/10V prototype are presented to verify the analysis of the proposed converter.

열거식 계층분류체계에 분석합성식 기법의 도입에 관한 연구-KDC를 중심으로

  • 도태현
    • 한국도서관정보학회지
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    • 제29권
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    • pp.241-272
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    • 1998
  • The purpose of this paper is to examine the analytic-assembling(faceted analysis) methods applied in enumerative-hierarchical classification schemes. (mainly in KDC) The methods are summarized as follows : 1. For the enumerative-hierarchical classification schemes, in principle the subjects are divided into subdivisions by only one facet at the same level, and step by step. However some subjects, for example 'library and information science' 'education' and others in KDC, are divided into subdivisions by multiple facets at same level like Colon Classification. 2. Most of enumerative-hierarchical classification schemes have various kinds of auxiliary tables, such as standard subdivisions, areas, periods, and languages. Each of them is considered as foci by a facet applied to subdivide all kinds of subjects or some special subjects into lower level. 3. To classify the compound subjects with phase relation, KDC provides ready-made classification numbers or notes that says 'divide by 001-999'(whole subjects) of 'divide by xxx-xxx'(limited scope of subjects). The ready-made compound subjects, or subdividing by whole or limited scope of subjects are similar to representation of phase relation in Colon Classification. Yet these analytic-assembling methods in KDC are needed to be supplemented and amended. Subdividing methods for faceted analysis have to be unified through the whole schedule. The auxiliary tables should be enlarged and subdivided more specifically. And for representation of phase relation, the linking signs can be useful in KDC as well as UDC and other analytic-assembling classification schemes like Colon Classification.

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Single-channel Demodulation Algorithm for Non-cooperative PCMA Signals Based on Neural Network

  • Wei, Chi;Peng, Hua;Fan, Junhui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권7호
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    • pp.3433-3446
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    • 2019
  • Aiming at the high complexity of traditional single-channel demodulation algorithm for PCMA signals, a new demodulation algorithm based on neural network is proposed to reduce the complexity of demodulation in the system of non-cooperative PCMA communication. The demodulation network is trained in this paper, which combines the preprocessing module and decision module. Firstly, the preprocessing module is used to estimate the initial parameters, and the auxiliary signals are obtained by using the information of frequency offset estimation. Then, the time-frequency characteristic data of auxiliary signals are obtained, which is taken as the input data of the neural network to be trained. Finally, the decision module is used to output the demodulated bit sequence. Compared with traditional single-channel demodulation algorithms, the proposed algorithm does not need to go through all the possible values of transmit symbol pairs, which greatly reduces the complexity of demodulation. The simulation results show that the trained neural network can greatly extract the time-frequency characteristics of PCMA signals. The performance of the proposed algorithm is similar to that of PSP algorithm, but the complexity of demodulation can be greatly reduced through the proposed algorithm.

Minimum Row Weight and Polar Spectrum Based Puncture Polar Codes Construction Algorithm

  • Liu Daofu;Guo Rui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권8호
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    • pp.2157-2169
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    • 2023
  • In order to handle the problem that puncture patterns will change the position distribution of original information bits and frozen bits in polar codes, which affects performance of puncture polar codes further, a minimum row weight and polar spectrum based puncture polar codes construction algorithm (called PA-MRWP) is proposed in this paper. The algorithm calculates row weight of generator matrix and sorts the row weight in ascending order first. Next, the positions with the minimum row weight are selected as initial puncture positions. If the rows with the same row weight cannot all be punctured, polar spectrum based auxiliary puncture scheme is used. In sub-channels with the same row weight, rows corresponding to the polarized sub-channels with higher reliability are selected as puncture positions to construct puncture vector, and the reliability is calculated based on polar spectrum. It is actually a two-step selection strategy, the proposed minimum row weight puncture (MRWP) algorithm is used for primary selection and polar spectrum based auxiliary puncture is used for adjustment. Simulation results show that, compared with worst quality puncture (WQP) algorithm, the proposed PA-MRWP algorithm and Gaussian approximation-aided minimum row weight puncture (GA-MRWP) algorithm provide gains of about 0.46 dB and 0.29 dB at bit error rate (BER) of 10-4, respectively when code length N=400, code rate R=1/2. In addition, the proposed puncture algorithms improve the BER performance significantly with respect to quasi-uniform puncture (QUP) algorithm.

주변 온도보상이 필요 없는 열선식 풍속 센서 시스템 (Hot Wire Wind Speed Sensor System Without Ambient Temperature Compensation)

  • 성준규;이근우;정회경
    • 한국정보통신학회논문지
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    • 제23권10호
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    • pp.1188-1194
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    • 2019
  • 유체의 흐름을 측정하는 여러 방법 중 열선 풍속 센서는 유체의 열전달에 의해 속도나 온도를 측정하는 장치로 비정상 속도 및 난류 속도 성분을 측정하는데 유용하다. 하지만 열선 풍속 센서는 외부의 환경 요인에 민감하며, 주변 온도, 습도, 신호 잡음 등에 의해 정확도가 떨어지는 단점이 있다. 이런 단점을 보완하는 방법으로 온도 보상 회로를 추가하는 기술이 나오고 있지만 가격 경쟁력을 갖출 수 없는 상황이다. 이를 해결하기 위해 본 논문에서는 온도 보상이 필요 없는 풍속 감지 센서에 대해 연구를 진행하였다. 열선식 풍속 센서는 외부 환경 요인 중에서도 주변 온도에 매우 취약하다. 주변 온도로는 전자 회로에 의한 발열의 영향이 가장 크게 미치고 있으며, 이를 개선하는 방법으로 발열체에 보조 발열체를 추가로 장착하여 보조발열체와 발열체의 일정한 온도차를 제어하는 것이다. 이와 같이 기존 기술에 비해 복잡하지 않은 방법으로 동등한 성능을 확보할 수 있다는 것을 확인할 수 있었다.

부가 정보를 활용한 비전 트랜스포머 기반의 추천시스템 (A Vision Transformer Based Recommender System Using Side Information)

  • 권유진;최민석;조윤호
    • 지능정보연구
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    • 제28권3호
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    • pp.119-137
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    • 2022
  • 최근 추천 시스템 연구에서는 사용자와 아이템 간 상호 작용을 보다 잘 표현하고자 다양한 딥 러닝 모델을 적용하고 있다. ONCF(Outer product-based Neural Collaborative Filtering)는 사용자와 아이템의 행렬을 외적하고 합성곱 신경망을 거치는 구조로 2차원 상호작용 맵을 제작해 사용자와 아이템 간의 상호 작용을 더욱 잘 포착하고자 한 대표적인 딥러닝 기반 추천시스템이다. 하지만 합성곱 신경망을 이용하는 ONCF는 학습 데이터에 나타나지 않은 분포를 갖는 데이터의 경우 예측성능이 떨어지는 귀납적 편향을 가지는 한계가 있다. 본 연구에서는 먼저 NCF구조에 Transformer에 기반한 ViT(Vision Transformer)를 도입한 방법론을 제안한다. ViT는 NLP분야에서 주로 사용되던 트랜스포머를 이미지 분류에 적용하여 좋은 성과를 거둔 방법으로 귀납적 편향이 합성곱 신경망보다 약해 처음 보는 분포에도 robust한 특징이 있다. 다음으로, ONCF는 사용자와 아이템에 대한 단일 잠재 벡터를 사용하였지만 본 연구에서는 모델이 더욱 다채로운 표현을 학습하고 앙상블 효과도 얻기 위해 잠재 벡터를 여러 개 사용하여 채널을 구성한다. 마지막으로 ONCF와 달리 부가 정보(side information)를 추천에 반영할 수 있는 아키텍처를 제시한다. 단순한 입력 결합 방식을 활용하여 신경망에 부가 정보를 반영하는 기존 연구와 달리 본 연구에서는 독립적인 보조 분류기(auxiliary classifier)를 도입하여 추천 시스템에 부가정보를 보다 효율적으로 반영할 수 있도록 하였다. 결론적으로 본 논문에서는 ViT 의 적용, 임베딩 벡터의 채널화, 부가정보 분류기의 도입을 적용한 새로운 딥러닝 모델을 제안하였으며 실험 결과 ONCF보다 높은 성능을 보였다.

Dual Attention Based Image Pyramid Network for Object Detection

  • Dong, Xiang;Li, Feng;Bai, Huihui;Zhao, Yao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제15권12호
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    • pp.4439-4455
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    • 2021
  • Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

Modeling and an Efficient Com bined Control Strategy for Fuel Cell Electric Vehicles

  • Lee, Nam-Su;Shim, Seong-Yong;Ahn, Hyun-Sik;Choi, Joo-Yeop;Choy, Ick;Kim, Do-Hyun
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2004년도 ICCAS
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    • pp.1629-1633
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    • 2004
  • In this paper, we first implement the simulation environment to investigate the efficient control method of a Fuel Cell Electric Vehicle (FCEV) system with battery. The subsystems of a FCEV including the fuel cell system, the electric motor (including the power electronics) and the tansmission (reduction gear), and the auxiliary power source (battery) are mathematically fomulated and coded using the Matlab/Simulink software. Some examples are given to show the capabilities of the modeled system and d a basic control strategy is examined for the economic energy distribution between the fuel cell and the auxiliary power source. It is illustrated by simulations that the actual vehicle velocity follows the given desired velocity pattern while both SOC control and power distribution control are being performed.

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적응적 결함-허용 다단계 상호연결망 (Adaptive Fault-tolerant Multistage Interconnection Network)

  • 김금호;김영만;배은호;윤성대
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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    • pp.199-202
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    • 2001
  • In this paper, we proposed and analyzed a new class of irregular fault-tolerant multistage interconnection network named as Extended-QT(Quad Tree) network. E-QT network is extended QT network. A unique path MIN usually is low hardware complexity and control algorithm. So we proposes a class of multipath MIN which are obtained by adding self-loop auxiliary links at the a1l stages in QT(Quad Tree) networks so that they can provide more paths between each source-destination pair. The routing of proposed structure is adaptived and is based by a routing tag. Starting with the routing tag for the minimum path between a given source-destination pair, routing algorithm uses a set of rules to select switches and modify routing tag. Trying the self-loop auxiliary link when both of the output links are unavailable. If the trying is failure, the packet discard. In simulation, an index of performance called reliability and cost are introduced to compare different kinds of MINs. As a result, the prouosed MINs have better capacity than 07 networks.

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A Novel Boost PFC Converter Employing ZVS Based Compound Active Clamping Technique with EMI Filter

  • Mohan, P. Ram;Kumar, M. Vijaya;Reddy, O.V. Raghava
    • JSTS:Journal of Semiconductor Technology and Science
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    • 제8권1호
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    • pp.85-91
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    • 2008
  • A Boost Power Factor Correction (PFC) Converter employing Zero Voltage Switching (ZVS) based Compound Active Clamping (CAC) technique is presented in this paper. An Electro Magnetic Interference (EMI) Filer is connected at the line side of the proposed converter to suppress Electro Magnetic Interference. The proposed converter can effectively reduce the losses caused by diode reverse recovery. Both the main switch and the auxiliary switch can achieve soft switching i.e. ZVS under certain condition. The parasitic oscillation caused by the parasitic capacitance of the boost diode is eliminated. The voltage on the main switch, the auxiliary switch and the boost diode are clamped. The principle of operation, design and simulation results are presented here. A prototype of the proposed converter is built and tested for low input voltage i.e. 15V AC supply and the experimental results are obtained. The power factor at the line side of the converter and the converter efficiency are improved using the proposed technique.