Research on thermal effect of power LED devices based on PCA and neural network

With the development of the controllable suspension systems, the mono-tube hydraulic adjustable damper has attracted great public attention with the advantages such as good heat dissipation, less power, fast response, durable, reliable, and simple structure. However, the unknown regulating mechanism modeling impedes the practical application of the mono-tube hydraulic adjustable damper. To model the regulating mechanism, this paper analytically studies the behavior of the mono-tube hydraulic adjustable damper via developing an analytical model and thermal effect equations for the use of engineering design. Then, the mono-tube hydraulic adjustable damper is tested in an integral shock absorber testing system to verify the accuracy of model and equations. On the basis of the verified analytical model and thermal effect equations, a compensation system with gray neural network algorithm is originally designed to model the regulating mechanism of the mono-tube hydraulic adjustable damper, thus achieving the desired damping force adaptively and accurately at various working conditions by obtaining the required rotary angle of the adjustment rod. The simulation results and experimental results show that the characteristic analyses of mono-tube hydraulic adjustable damper are reliable. Meanwhile, the simulation results of the gray neural network algorithm also indicate that the proposed compensation system can provide an exact regulating mechanism model for the mono-tube hydraulic adjustable damper and the proposed gray neural network algorithm is superior to the traditional neural network algorithm.

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Neural network based model for incorporating the thermal effect on the magnetic hysteresis of the 3F3 material

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-141778 ◽

2014 ◽

Vol 46 (1) ◽

pp. 281-286

Author(s):

Abdelmadjid Nouicer

Keyword(s):

Neural Network ◽

Thermal Effect ◽

Magnetic Hysteresis

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Positioning Accuracy of CNC Machine Tools. Thermal Effect Prediction Using Neural Network.

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C ◽

10.1299/kikaic.62.2686 ◽

1996 ◽

Vol 62 (599) ◽

pp. 2686-2691 ◽

Cited By ~ 2

Author(s):

Liang CHEN ◽

Yasuhiro HIROTA ◽

Masaomi TSUTSUMI ◽

Nobuhiko NISHIWAKI

Keyword(s):

Neural Network ◽

Thermal Effect ◽

Machine Tools ◽

Cnc Machine Tools ◽

Cnc Machine ◽

Positioning Accuracy

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UHV-STM studies of silicon nano-pyramid growth on silicon surface at high temperature

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100130353 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1144-1145

Author(s):

T. Sato ◽

S. Kitamura ◽

T. Sueyoshl ◽

M. Iwatukl ◽

C. Nielsen

Keyword(s):

High Temperature ◽

Relaxation Process ◽

Thermal Effect ◽

Bias Voltage ◽

Silicon Surface ◽

Growth Process ◽

Tunneling Current ◽

Fabrication Technique ◽

Nano Fabrication ◽

Electromigration Effect

Recently, the growth process and relaxation process of crystalline structures were studied by observing a SI nano-pyramid which was built on a Si surface with a UHV-STM. A UHV-STM (JEOL JSTM-4000×V) was used for studying a heated specimen, and the specimen was kept at high temperature during observation. In this study, the nano-fabrication technique utilizing the electromigration effect between the STM tip and the specimen was applied. We observed Si atoms migrated towords the tip on a high temperature Si surface.Clean surfaces of Si(lll)7×7 and Si(001)2×l were prepared In the UHV-STM at a temperature of approximately 600 °C. A Si nano-pyramid was built on the Si surface at a tunneling current of l0nA and a specimen bias voltage of approximately 0V in both polarities. During the formation of the pyramid, Images could not be observed because the tip was stopped on the sample. After the formation was completed, the pyramid Image was observed with the same tip. After Imaging was started again, the relaxation process of the pyramid started due to thermal effect.

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