This paper overviews the estimation algorithms related with the problem of Attitude Determination (AD) of a Low Earth Orbit (LEO) satellite using carrier phase based measurements of the Global Navigation Satellite Systems (GNSS). In the past three decades, this problem has been addressed overwhelmingly and many solutions and schemes have not only been devised but also been implemented on real space missions. Many space based GPS receivers have been designed, tested and patented also. The problem involves two basic algorithms; Ambiguity Resolution (AR) and the Attitude Determination / Estimation Algorithms. Researchers have developed and employed different schemes and algorithms in this context but the quest for the computational efficient algorithms and the integration of both the optimal algorithms is still an active area of research for the GNSS researchers.
Convergence speed, steady-state performance and noise reduction performance of active noise control systems have been issues we need to address. In particular, bin-Normalized Frequency Domain Block Least Mean Square (NFBLMS) algorithm will have a biased steady-state solution when adaptive filter length is insufficient, while Modified Frequency Domain Block Least Mean Square (MFBLMS) algorithm and the mixed algorithm composed of NFBLMS and MFBLMS have slow convergence speed and poor noise reduction performance. In this paper, Frequency Domain Block Filtered-U Least Mean Square (FBFULMS) algorithm structure is proposed, which ensures both stability of system and fast convergence speed by means of secondary path modelling. Simulation results show that when input excitation is compressor noise data, the proposed FBFULMS algorithm achieves a noise attenuation of up to 26 dBA with 39 iteration steps. At last, XILINX Artix7-100T FPGA is used as the core control module for hardware design. Its measured results show that this method effectively attenuates the low and medium frequency noise in the target area of compressor, especially at the error microphone with a measured noise reduction of up to 20dBA and a convergence time of about 60µs.
Aiming at the reuse of existing CAD engineering drawings in manufacturing product design, a 2D engineering drawing retrieval method based on bag of visual words model is proposed. The scale invariant feature transformation (SIFT) algorithm is used to extract visual vocabulary vectors from different kinds of 2D engineering drawings, and the K-Means algorithm is used to cluster the extracted SIFT features to form visual vocabulary, construct visual dictionary, construct the bag of visual words model of 2D engineering drawings, and realize the retrieval of engineering drawings. Select 1000 engineering drawings, build bag of visual words model of engineering drawings, and build a 2D engineering drawing retrieval system. The method of retrieving engineering drawings using product point cloud data is proposed. The point cloud data of enterprise products is scanned by 3D scanner to generate multi views of STL model., extract the contour of product view, extract the characteristics of contour view, and retrieve similar 2D engineering drawings, so as to provide help for reverse engineering.
A novel fiber optical speckle sensor based on multimode interference for underground pipeline intrusion detecting is investigated both theoretically and experimentally. The sensor utilizes a single mode to multimode to single mode (SMF-MMF-SMF) structure as a sensing unit, which cascade to create a quasi-distributed optical sensor. To pave it near the underground pipeline we can detect any valid intrusion caused by motion, sound or vibration acts on the fiber. Experimental test shows that the detecting distance can be about 30Km and the locating accuracy of intrusion event is ±500m. Compared with Mach-Zehnder interferometer based configuration it has a much higher sensitivity.
Use DP-5 Dielictric-spectrometer,by scaling the dielectric frequency characteristic of the buzzer by pizoelectric ceramics,the research exhibited characteristic of the polarization,when the outfield are constantly changing.
Signal transmission loss of using wireless sensors for structural health monitoring is a usual case, which undermines the reliability of the sensors for monitoring the structural conditions. The measured vibration data with a high data loss ratio can hardly be used for the analysis, that is, modal identification, as it will lead to significant errors in the results. This paper proposes a novel approach based on convolutional neural networks for recovering the lost vibration data for structural health monitoring. The used network is a fully feed-forward convolutional neural network with bottleneck architecture and skip connection, which constructs the nonlinear relationships between the incomplete signal with data loss measured from the sensors with the transmission loss and the complete true signal. The trained network extracts the robust higher representation features of the measured incomplete signals using the compression layers and expands those features gradually throughout the reconstruction layers to recover and obtain the complete true signals. The long-term vibration data from Dowling Hall Footbridge are employed to validate the effectiveness and robustness of the proposed approach for the lost data recovery. Two case studies are conducted to validate the recovery accuracy for single-channel and multiple-channel cases, respectively. The effect of sampling rate on the recovery accuracy is also investigated. The proposed approach exhibits the outstanding capability of lost data recovery, even when the signals have severe data loss ratios up to 90%. To further demonstrate the reliability of the recovered signals for data analysis, modal identification results by using the recovered signals with different data loss ratios show a very good agreement with those obtained from the complete true data.
We propose two procedures to detect a change in the mean of high-dimensional online data. One is based on a max-type U-statistic and another is based on a sum-type U-statistic. Theoretical properties of the two procedures are explored in the high dimensional setting. More precisely, we derive their average run lengths (ARLs) when there is no change point, and expected detection delays (EDDs) when there is a change point. Accuracy of the theoretical results is confirmed by simulation studies. The practical use of the proposed procedures is demonstrated by detecting an abrupt change in PM2.5 concentrations. The current study attempts to extend the results of the CUSUM and Shiryayev-Roberts procedures previously established in the univariate setting.
