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In September 2020, China proposed the achievement of the emission reduction targets of “carbon peak” and “carbon neutral” by 2030 and 2060, respectively. As an important area of energy consumption in addition to industry and transportation, the construction industry has great energy-saving potential and is gradually becoming the key to achieving China’s energy-saving and emission-reduction goals. Energy data is an important basic support for measuring carbon emissions, analyzing energy-saving potential, and formulating energy-saving targets. In order to solve the he lack of data on China’s carbon emissions, this paper uses lamplight remote sensing image data in the study. Combined with China’s eastern, central, and western regions of building carbon emissions data and the establishment of a partition of China building carbon emissions calculation model, panel data found building carbon emissions and smooth lamp brightness values between the balanced relations. After that, using the building carbon emissions models of the three regions, the building carbon emissions of 30 provinces, 360 cities, and 2778 counties in China were measured, and the changing trends and temporal and spatial directions of building carbon emissions at three spatial scales were analyzed. The results showed that although the total carbon emissions of civil buildings in China has been increasing year by year, its average annual growth rate is gradually slowing down. In addition, the temporal and spatial development directions of carbon emissions from buildings of different spatial scales are basically the same, and they all show a trend of shifting to the east.

This paper proposes an integrated simulation method of the performance and reliability for aerospace electromechanical products, which can obtain the reliability parameters of the product through digital simulation, so as to guide the design of the product, taking into account the factors that affect the reliability in the design stage. This method is based on a multidisciplinary performance simulation model, comprehensively considers the failure mode and failure mechanism of the product. Through the method of DESIGN OF EXPERIMENT(DOE), the key design parameters and disturbance factors are combined and designed as input, the performance response surface model, the performance and reliability integrated simulation model are constructed in turn. Taking a typical aerospace electromechanical product solar wing drive mechanism as an example, the engineering application verification is carried out, and the response surface has a high matching degree with the sampling point, which can meet the engineering application.

This paper briefly described the structure of double beam bridge crane and the basic parameters of double beam bridge crane are defined. According to the structure and system division of double beam bridge crane, the reliability architecture of double beam bridge crane system is proposed, and the reliability mathematical model is constructed.

In spacecraft telemetry data prediction field, unknown residual distribution and great volatility of predicted value have hampered traditional prediction interval methods to follow forecast trend and give high-precision intervals. Hence, modified Block Bootstrap prediction interval Method is proposed in this paper. Contrast to traditional method, this method can enhance accuracy of non-stationary time series data prediction interval for its data sampling frequency can be adjusted by data character. In the end, an example is given to show the validity and practicality of this method.

In this paper, an approach is proposed for time series prediction based on Modified Probability Neural Network (MPNN). Bayesian-statistics and decision-making theories and non-parameters density function estimation using Parzen-window function are applied to MPNN. The efficiency of the approach was demonstrated by a case study, an application for prediction on moonlet power system data, through comparison with other methods the linear ARMA model and the nonlinear widely used BP neural network. It was found that MPNN has the highest precision with the least time cost. Consequently, it verified and illustrated that large number of battery data can be predicted quickly and accurately using MPNN, moreover, it is valuable in the field of moonlet power system data prediction.

Considering the drawbacks of traditional adjustment method without calculating possible nonlinear between rotor adjustments and fuselage vibration signals, a new rotor adjustment method based on general regression neural network (GRNN) and genetic algorithm is presented. GRNN network is employed to model the relationship of the rotor adjustments and fuselage vibrations, whose input parameters are rotor adjustment parameters and whose outputs are acceleration measurements along the three axes of rotor shaft and the fuselage. With helicopter vibration as objective function, genetic algorithm (GA) was used to make a global optimization to find the suitable rotor adjustments corresponding to the minimum vibrations. Flight test results indicate that proposed rotor adjustment method can minimize fuselage vibration at fundamental rotor frequency along the three axes, only in one or two adjustment flights. Moreover the neural networks are easily updated if new data becomes available thus allowing the system to evolve and mature in the course of its use.

With the development of the aircraft hydraulic power supply system towards high pressure and large load, Fluid-borne vibration in piping systems leads to serious problems. Because of pump’s periodic flow pulsation and fluid-structure interaction, mechanical fatigue can readily take place. Traditionally, passive pulsation dampers have been used to reduce fluid pulsation of high frequencies, but the application of such passive devices is limited in low frequency conditions because of their bulk volumes or poor performances. This paper presented a distributed active control method using piezoelectric direct drive servovalve to reduce the fluid pulsation in pipeline systems. The idea of distributed vibration active control based on the piezoelectric direct drive servo was described in detail. Models of the piezoelectric direct drive servovalve, aero-pump and fluid-structure coupling vibration were established. The control method can adjust control parameters adaptively against the disturbance. A test platform was designed and built to validate the vibration attenuation efficiency. The experimental results show that the vibration active control method is adaptive and robust against disturbance, and can keep the vibration minimum under variable speed and load.

In this paper, a new method is proposed for predicting the future evolution of a time series of spacecraft telemetry data. Because such a time series has usually a non-stationary trend, nonlinear functional relationship between inputs and outputs and other uncertainties, an appropriate prediction model for spacecraft data is needed to set up. To this end, we analyze the characteristics of the Probability Neural Network (PNN) and modify its architecture for fitting the requirements of prediction. We propose a new model: the Modified Probability Neural Network (MPNN) which incorporates the characteristics of statistics into the ANN prediction model. We also analyze the statistical characteritics of the error of MPNN prediction for spacecraft data to compute approximate prediction interval. Finally we construct the interval prediction with the MPNN model and apply it to predicting the spacecraft data.