• Journal of Power Electronics
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• v.19 no.4
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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.

• Journal of Korean Library and Information Science Society
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• v.29
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1188-1194
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• 2019

Among the many ways to measure the flow of fluid the hot air wind speed sensor is a device for measuring the speed or temperature by heat transfer of a fluid. However, the hot wire wind speed sensor is sensitive to external environmental factors, and has a disadvantage of inaccuracy due to ambient temperature, humidity, and signal noise. In order to compensate for this disadvantage, advanced technology has been introduced by adding temperature compensation circuits, but it is expensive. In order to solve this problem, this paper studies the wind speed sensor that does not need temperature compensation. Heated wind speed sensors are very vulnerable to the ambient temperature, which is generated by electronic circuits, even among external environmental factors. in order to improve this, the auxiliary heating element is additionally installed in the heating element to control a constant temperature difference between the auxiliary heating element and the heating element.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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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.

• Proceedings of the IEEK Conference
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• 2001.06c
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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.

• JSTS:Journal of Semiconductor Technology and Science
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• v.8 no.1
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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.