• Journal of the Korean Society for Precision Engineering
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• v.17 no.11
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• pp.141-150
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• 2000

Continuously variable transmission(CVT) combined differential gear unit has many advantages, which are the decrease of CVT size, the increase of overall efficiency, the extension of speed ratio range, and the generation of geared neutral. It is known that such CVT can be classified into the input coupled type and the output coupled type according to the coupling location of continuously variable unit(CVU). In this paper, six different configurations of input coupled type CVT combined V-belt CVU and 2K-H I type differential gear unit are proposed. Some useful theoretical formula related to speed ratio, power flow and efficiency are derived and analyzed. The propriety of derived formula and theoretical analysis are proven by various experiments.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.339-342
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• 2007

The power control system of Smart UAV is similar to the propeller pitch governing concept of turboprop aircraft. The pilot inputs the engine power directly and the pitch governor controls the propeller pitch to maintain the propeller RPM. The manual back-up system of PW206C engine is used for the engine power control of Smart UAV. Engine performance estimation program is used to predict the control range of power lever arm(PLA) angle according to the variation of flight altitude and speed. These data provide a guide for the engine control in manual mode operation.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.699-704
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• 2001

ABSTRACT In this paper, a new hybrid modular recurrent TDNN (time-delay neural network)-HMM (hidden Markov model) architecture for speech recognition has been studied. In TDNN, the recognition rate could be increased if the signal window is extended. To obtain this effect in the neural network, a high-level memory generated through a feedback within the first hidden layer of the neural network unit has been used. To increase the ability to deal with the temporal structure of phonemic features, the input layer of the network has been divided into multiple states in time sequence and has feature detector for each states. To expand the network from small recognition task to the full speech recognition system, modular construction method has been also used. Furthermore, the neural network and HMM are integrated by feeding output vectors from the neural network to HMM, and a new parameter smoothing method which can be applied to this hybrid system has been suggested.