With the rapid increase in chip integration and power density,there is a growing need to develop advanced thermal interface materials for effective thermal management.Liquid metals with high thermal conductivity,excel...With the rapid increase in chip integration and power density,there is a growing need to develop advanced thermal interface materials for effective thermal management.Liquid metals with high thermal conductivity,excellent gap-filling capability and non-toxicity have received much attention.However,low-melting-point metals,such as galinstan and indium-bismuth-tin(EInBiSn) eutectic alloys,are prone to leaking,which limits their applications.In this study,In-Ga alloy composite thermal pads with a sandwich structure and a graphite film as an intermediate layer were prepared.The In-Ga alloy composition was adjusted so that these pads underwent partial phase change in the operating temperature range of the laptop CPU(50-100℃).This results in low thermal resistance and leakage prevention.The thermal resistance of the InGa5,InGa15 and InGa25 alloy thermal pads decreases to 7.3,4.1 and 2.66 K·mm^(2)·W^(-1),respectively,at a temperature and pressure of 100℃ and 50 psi.In a test measuring the actual cooling effect of the fabricated material on a CPU,the InGa 15 alloy thermal pad maintained the average CPU temperature at 90.1 ℃,significantly better than the EInBiSn thermal pad with an average CPU temperature of 94.1℃,and comparable to Galinstan,which had an average CPU temperature of 89.3℃.Due to their good heat dissipation and leak-proof properties,InGa alloy composite thermal pads are expected to become a new generation of thermal interface materials.展开更多
This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope （RLG） based on ge-netic algorithm （GA） and support vector regression （SVR） in the single-axis rotation inertial ...This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope （RLG） based on ge-netic algorithm （GA） and support vector regression （SVR） in the single-axis rotation inertial navigation system （SRINS）. GA is used for selecting the optimal parameters of SVR. The latitude error and the temperature variation during the identification stage are adopted as inputs of GA-SVR. The navigation results show that the proposed GA-SVR model can reach an identification accuracy of 0.000 2 （？）/h for the Z-axis drift of RLG. Compared with the ra-dial basis function-neural network （RBF-NN） model, the GA-SVR model is more effective in identification of the Z-axis drift of RLG.展开更多
This paper presents a new algorithm for de-noising global positioning system （GPS） and inertial navigation system （INS） data and estimates the INS error using wavelet multi-resolution analysis algorithm （WMRA）-b...This paper presents a new algorithm for de-noising global positioning system （GPS） and inertial navigation system （INS） data and estimates the INS error using wavelet multi-resolution analysis algorithm （WMRA）-based genetic algorithm （GA） with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule （TSR）, and the optimum level of decomposition （LOD）. This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.展开更多
基金financially supported by the National Natural Science Foundation of China (Nos.11204097 and U1530120)。
文摘With the rapid increase in chip integration and power density,there is a growing need to develop advanced thermal interface materials for effective thermal management.Liquid metals with high thermal conductivity,excellent gap-filling capability and non-toxicity have received much attention.However,low-melting-point metals,such as galinstan and indium-bismuth-tin(EInBiSn) eutectic alloys,are prone to leaking,which limits their applications.In this study,In-Ga alloy composite thermal pads with a sandwich structure and a graphite film as an intermediate layer were prepared.The In-Ga alloy composition was adjusted so that these pads underwent partial phase change in the operating temperature range of the laptop CPU(50-100℃).This results in low thermal resistance and leakage prevention.The thermal resistance of the InGa5,InGa15 and InGa25 alloy thermal pads decreases to 7.3,4.1 and 2.66 K·mm^(2)·W^(-1),respectively,at a temperature and pressure of 100℃ and 50 psi.In a test measuring the actual cooling effect of the fabricated material on a CPU,the InGa 15 alloy thermal pad maintained the average CPU temperature at 90.1 ℃,significantly better than the EInBiSn thermal pad with an average CPU temperature of 94.1℃,and comparable to Galinstan,which had an average CPU temperature of 89.3℃.Due to their good heat dissipation and leak-proof properties,InGa alloy composite thermal pads are expected to become a new generation of thermal interface materials.
文摘This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope （RLG） based on ge-netic algorithm （GA） and support vector regression （SVR） in the single-axis rotation inertial navigation system （SRINS）. GA is used for selecting the optimal parameters of SVR. The latitude error and the temperature variation during the identification stage are adopted as inputs of GA-SVR. The navigation results show that the proposed GA-SVR model can reach an identification accuracy of 0.000 2 （？）/h for the Z-axis drift of RLG. Compared with the ra-dial basis function-neural network （RBF-NN） model, the GA-SVR model is more effective in identification of the Z-axis drift of RLG.
基金supported in part by Graduate School of Studies through the Graduate Research Fellowship (GRF) sponsored by University Putra Malaysia
文摘This paper presents a new algorithm for de-noising global positioning system （GPS） and inertial navigation system （INS） data and estimates the INS error using wavelet multi-resolution analysis algorithm （WMRA）-based genetic algorithm （GA） with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule （TSR）, and the optimum level of decomposition （LOD）. This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.
