This paper presents a forecasting model for powder factors in tunnel blasting using artificial neural networks (ANN). Case data of a railway tunnel recently under construction in Taiwan were used to establish the model. The main rock type in the tested case was metamorphic rock. In this study, the rock mechanical factors influencing the powder factors were empirically identified first. Rock mechanical parameters having a significant influcncc were then filtered to train and test the ANN. The ANN model for predicting powder factors had a testing root mean square (RMS) of 0.02983 on average. Rock quality designation (RQD) was the most important parameter of all the selected rock mechanical characteristics. Validation was also performed to show that the neural networks outperformed the multiple nonlinear regression method in analyzing relationships between powder factors and rock characteristics.
The Ritchey–Chrétien telescope has been the key optical module for remote sensing instruments (RSI), in which the root mean square (RMS) random surface wavefront error and the alignment error of the primary and the secondary mirror takes the highest weighting in the tolerance analysis for the fabrication and assembly of the telescope. Therefore, the higher tolerance of those items becomes preferable for higher efficiency of RSI manufacturing. In this paper, the correlation between those tolerance items and the f-number of the telescope has been investigated. Although the f-number is normally a system parameter well specified in the beginning of the design process, it is not very rigid in practice and has a certain amount of allowable range. The optimal f-number can then be chosen based on the consideration of those key tolerance items. The proposed concept can be generalized as a novel methodology of design for tolerance.
Built-in autonomous stereo vision devices play a critical role in the autonomous docking instruments of space vehicles. Traditional stereo cameras for space autonomous docking use charge-coupled device (CCD) image sensors, and it is difficult for the overall size to be reduced due to the size of the CCD. In addition, only the few outermost elements of the camera lens use radiation-resistant optical glass material. In this paper, a complementary metal–oxide semiconductor (CMOS) device is used as the image sensor, and radiation-resistant optical glass material is introduced to all lens elements in order to make a compact and highly reliable space grade instrument. Despite the limited available material, a fixed focus module with 7 lens elements and overall length of 42 mm has been achieved, while meeting all the required performance demands for the final vision-guided docking process.
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