The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
With noisy environment caused by fluoresence and additive white noise as well as complicated spectrum fingerprints, the identification of complex mixture materials remains a major challenge in Raman spectroscopy application. In this paper, we propose a new scheme based on a constant wavelet transform (CWT) and a deep network for classifying complex mixture. The scheme first transforms the noisy Raman spectrum to a two-dimensional scale map using CWT. A multi-label deep neural network model (MDNN) is then applied for classifying material. The proposed model accelerates the feature extraction and expands the feature graph using the global averaging pooling layer. The Sigmoid function is implemented in the last layer of the model. The MDNN model was trained, validated and tested with data collected from the samples prepared from substances in palm oil. During training and validating process, data augmentation is applied to overcome the imbalance of data and enrich the diversity of Raman spectra. From the test results, it is found that the MDNN model outperforms previously proposed deep neural network models in terms of Hamming loss, one error, coverage, ranking loss, average precision, F1 macro averaging and F1 micro averaging, respectively. The average detection time obtained from our model is 5.31 s, which is much faster than the detection time of the previously proposed models.
Antimony trisulfide (Sb2Se3), a non-toxic and accessible substance, has possibilities as a material for use in solar cells. The current study numerically analyses Sb2Se3 solar cells through the program Solar Cell Capacitance Simulator (SCAPS). A detailed simulation and analysis of the influence of the Sb2Se3 layer’s thickness, defect density, band gap, energy level, and carrier concentration on the devices’ performance are carried out. The results indicate that a good device performance is guaranteed with the following values in the Sb2Se3 layer: an 800 optimal thickness for the Sb2Se3 absorber; less than 1015 cm−3 for the absorber defect density; a 1.2 eV optimum band gap; a 0.1 eV energy level (above the valence band); and a 1014 cm−3 carrier concentration. The highest efficiency of 30% can be attained following optimization of diverse parameters. The simulation outcomes offer beneficial insights and directions for designing and engineering Sb2Se3 solar cells.
Because it is relatively difficult in practice to classify the Raman spectrum under baseline noise and additive white Gaussian noise environments, this paper proposes a new framework based on a wavelet transform and deep neural network for identification of noisy Raman spectra. The framework consists of two main engines. Wavelet transform is proposed as the framework front end for transforming the 1-D noise Raman spectrum to two-dimensional data. The two-dimensional data are fed to the framework back end, which is a classifier. The optimum classifier is chosen by implementing several traditional machine learning (ML) and deep learning (DL) algorithms, and we investigate their classification accuracy and robustness performances. The four chosen MLs are naive Bayes (NB), a support vector machine (SVM), a random forest (RF) and a k-nearest neighbor (KNN), and a deep convolution neural network (DCNN) was chosen as a DL classifier. Noise-free, Gaussian noise, baseline noise, and mixed-noise Raman spectra were applied to train and validate the ML and DCNN models. The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectra (10-30 dB noise power). Based on the simulation, the accuracy of the DCNN classifier is 9% higher than that of the NB classifier, 3.5% higher than the RF classifier, 1% higher than the KNN classifier, and 0.5% higher than the SVM classifier. In terms of robustness to mixed noise scenarios, the framework with the DCNN back end showed superior performance compared with the other ML back ends. The DCNN back end achieved 90% accuracy at 3 dB SNR, while the NB, SVM, RF, and K-NN back ends required 27 dB, 22 dB, 27 dB, and 23 dB SNR, respectively. In addition, in the low-noise test dataset, the F-measure score of the DCNN back end exceeded 99.1%, and the F-measure scores of the other ML engines were below 98.7%.
The antimony sulphide (AnS) solar cell is a relatively new photovoltaic technology. Because of its attractive material, optical, and electrical qualities, Sb2Se3 is an excellent absorption layer in solar cells, with a conversion efficiency of less than 8%. The purpose of this research is to determine the best parameter for increasing solar cell efficiency. This research focused on the influence of absorber layer defect density and the n/i interface on the performance of antimony trisulfide solar cells. The researchers designed the absorber thickness values with the help of the SCAPS-1D (Solar Cell Capacitance Simulator-1D) simulation programme. For this purpose, they designed the ZnS/Sb2Se3/PEDOT: PSS planar p-i-n structure, and then simulated its performance. This result confirms a Power Conversion Efficiency (PCE) of ≥25% at an absorber layer thickness of >300 nm and a defect density of 1014 cm−3, which were within the acceptable range. In this experiment, the researchers hypothesised that the antimony triselenide conduction band possessed a typical energy of ≈0.1 eV and an energetic defect level of ≈0.6 eV. At the n/i interface, every condition generated a similar result. However, the researchers noted a few limitations regarding the relationship between the defect mechanism and the device performance.
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