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Design and Efficiency Analysis 48V-12V Converter using Gate Driver Integrated GaN Module
김종완,최중묵,유세프알라브,제이슨라이,Kim, Jongwan,Choe, Jung-Muk,Alabdrabalnabi, Yousef,Lai, Jih-Sheng Jason The Korean Institute of Power Electronics 2019 전력전자학회 논문지 Vol.24 No.3
This study presents the design and experimental result of a GaN-based DC-DC converter with an integrated gate driver. The GaN device is attractive to power electronic applications due to its superior device performance. However, the switching loss of a GaN-based power converter is susceptible to the common source inductance, and converter efficiency is severely degraded with a large loop inductance. The objective of this study is to achieve high-efficiency power conversion and the highest power density using a multiphase integrated half-bridge GaN solution with minimized loop inductance. Before designing the converter, several GaN and Si devices were compared and loss analysis was conducted. Moreover, the impact of common source inductance from layout parasitic inductance was carefully investigated. Experimental test was conducted in buck mode operation at 48 -12 V, and results showed a peak efficiency of 97.8%.
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김종완,안제성,김종상,이흥호,조성원 대한전자공학회 1994 전자공학회논문지-B Vol.b31 No.9
Conventional competitive learning algorithms compute the Euclidien distance to determine the winner neuron out of all predetermined output neurons. In such cases, there is a drawback that the performence of the learning algorithm depends on the initial reference(=weight) vectors. In this paper, we propose a new competitive learning algorithm that dynamically generates output neurons. The proposed method generates output neurons by dynamically changing the class thresholds for all output neurons. We compute the similarity between the input vector and the reference vector of each output neuron generated. If the two are similar, the reference vector is adjusted to make it still more like the input vector. Otherwise, the input vector is designated as the reference vector of a new outputneuron. Since the reference vectors of output neurons are dynamically assigned according to input pattern distribution, the proposed method gets around the phenomenon that learning is early determined due to redundant output neurons. Experiments using speech data have shown the proposed method to be superior to existint methods.
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김종완 대한전자공학회 1996 전자공학회논문지-B Vol.b33 No.3
In this paper, a new parallel neural network system that performs dynamic competitive learning is proposed. Conventional learning mehtods utilize the full dimension of the original input patterns. However, a particular attribute or dimension of the input patterns does not necessarily contribute to classification. The proposed system consists of parallel neural networks with the reduced input dimension in order to take advantage of the information in each dimension of the input patterns. Consensus schemes were developed to decide the netowrks performs a competitive learning that dynamically generates output neurons as learning proceeds. Each output neuron has it sown class threshold in the proposed dynamic competitive learning. Because the class threshold in the proposed dynamic learning phase, the proposed neural netowrk adapts properly to the input patterns distribution. Experimental results with remote sensing and speech data indicate the improved performance of the proposed method compared to the conventional learning methods.
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