The rapid development of artificial intelligence (AI) requires one to speed up the development of the domain-specific hardware specifically designed for AI applications. The neuromorphic computing architecture consisting of synapses and neurons, which is inspired by the integrated storage and parallel processing of human brain, can effectively reduce the energy consumption of artificial intelligence in computing work. Memory components have shown great application value in the hardware implementation of neuromorphic computing. Compared with traditional devices, the memristors used to construct synapses and neurons can greatly reduce computing energy consumption. However, in neural networks based on memristors, updating and reading operations have system energy loss caused by voltage and current of memristors. As a derivative of memristor, memcapacitor is considered as a potential device to realize a low energy consumption neural network, which has attracted wide attention from academia and industry. Here, we review the latest advances in physical/simulated memcapacitors and their applications in neuromorphic computation, including the current principle and characteristics of physical/simulated memcapacitor, representative synapses, neurons and neuromorphic computing architecture based on memcapacitors. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic computation based on memcapacitors.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Spectral mismatch error should be carefully considered during the calibration of solar cells by means of solar simulator and calibrated reference cell. Even test and reference cells with the same type should be also considered spectral mismatch error to achieve good measurement results. Spectral mismatch error can be calculated with the relative spectral response of the test and reference cells, and the relative spectral irradiance of the simulator and reference solar. The reference solar spectral irradiance distribution was given according to IEC60904-3:2008. Experimental results, two cells, one test and one ref, with two different spectra solar simulators, were presented. The calculation method and experimental data presented could be positive reference to photovoltaic labs to obtain good calibration and test results of solar cells.
Affected by factors such as complex production operation data, high dimensions, and weak regularity, the existing ultra-short-term working condition prediction method struggles to guarantee the prediction accuracy and operation speed. Therefore, we propose an ultra-short-term working condition prediction method based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). Firstly, we use sliding window and normalized processing methods to carry out data processing, and use CNN to extract the characteristics of processed production operation data. Secondly, we then improve the LSTM gated structure and introduce L2 norm, learning the change law of the production operation data by means of the LSTM prediction layer, and then obtain the predicted value of the working condition. We use the Bayesian method to select the parameters of the CNN-LSTM model to improve the prediction accuracy. Finally, we apply our method to a real-world application to demonstrate that our ultra-short-term working condition prediction method achieves superior results for prediction accuracy and running speed when compared with other methods.
Detection of sea surface maneuvering small target has long been faced with the problems of strong sea clutter background and low signal-clutter-noise ratio (SCNR), the long-time coherent integration algorithm can effectively improve the SCNR. A long-time coherent integration algorithm is proposed in this paper to process the echo signals of sea surface strongly maneuvering targets with both range and Doppler frequency migration. By using the idea of stage optimization, this algorithm can compensate the range and Doppler frequency migration at the same time, efficiently accumulate the target energy along the trajectory of the target, and effectively improve the SCNR of the target. In addition, this algorithm can also achieve the precise tracking of the target speed and position during the accumulation process, and is suitable for any maneuvering mode. The actual data processing and analysis show that this algorithm can realize the effective accumulation of maneuvering targets with low SCNR, and the accumulation gain is improved by more than 6dB compared with existing methods.
In recent years, more advanced turbulence modeling techniques and various forms of equivalent stress have been introduced to better predict blood damage in blood pumps. Nonetheless, as previous study showed, the results showed significant divergence for different stress forms and turbulence simulation methods, discrediting hemolysis prediction as an important tool for the design, optimization and evaluation of blood circulatory devices. This study aims at quantitatively investigating the grid convergence for the prediction of hemolysis in blood circulatory devices, with a focus on its sensitivity to the stress forms and turbulence simulation methods. We revealed the integral of equivalent stress has very different characteristics of grid convergence. For the SST k-ω model of the Reynolds-averaged Navier-Stokes method, grid convergence was less demanding on grid size and insensitive to stress forms. For wall-modeled large eddy simulation (WMLES), grid convergence was demanding and sensitive to stress forms, with highest uncertainty for the “total scalar stress”, followed by “viscous stress”. The “energy-dissipation stress” showed the best grid convergence for both the SST k-ω model and WMLES. We also observed a significant divergence for metrics based on “total scalar stress” under different turbulence simulation methods, while the “energy-dissipation stress” showed a much higher consistency. We show the combination of energy-dissipation stress and WMLES can better capture the trend of hemolysis and has the best grid convergence. This study provides insights for a better prediction of hemolysis in turbulent flows in blood circulatory devices.
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