Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the t...
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Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.
Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimizati...
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Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.
Major Depressive Disorder (MDD) has been a major mental disease in recent years, imposing huge negative impacts on both our society and individuals. The current clinical MDD detection methods, such as self-report scal...
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With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate pred...
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With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.
As convolutional neural networks (CNNs) have shown excellent performance in various inference tasks, it has become increasingly critical to enable Artificial Intelligence of Things (A IoT) systems to run CNN-based app...
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Aimedat the problem of dynamic causal discovery in the era of artificial intelligence, this article combines partial rank correlation coefficients and streaming features in the field of Bayesian network structure lear...
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In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distributi...
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Previous methods on knowledge base question generation (KBQG) primarily focus on refining the quality of a single generated question. However, considering the remarkable paraphrasing ability of humans, we believe that...
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Cognitive diagnosis is a critical task in intelligent education, aimed at inferring students’ mastery of knowledge concepts based on their response logs. Although existing cognitive diagnosis models achieve excellent...
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ISBN:
(数字)9798331543143
ISBN:
(纸本)9798331543150
Cognitive diagnosis is a critical task in intelligent education, aimed at inferring students’ mastery of knowledge concepts based on their response logs. Although existing cognitive diagnosis models achieve excellent performance, they underestimate the difficulty of easy exercises and overestimate the difficulty of hard exercises. We attribute this to the class imbalance in the response logs of easy and hard exercises. Moreover, the convergence speed varies from exercise to exercise during model training, which further challenges generalization. To address these problems, we propose an exercise’s correct rate-based logit adjustment approach for a wide range of cognitive diagnosis models. Specifically, we enforce logit adjustment in the loss during training to overcome the class imbalance in response logs. Then, we apply group distributionally robust optimization for generalization. Finally, extensive experiments demonstrate the effectiveness of our model, especially on easy and hard exercises.
Novel orange-red Sr_(2)GdSbO_(6):xEu^(3+)(x=0,0.05,0.1,0.2,0.3,0.4,0.5 and 0.6) phospho rs were successfully prepared by the traditional high-temperature solid-state *** results of Rietveld refinement,energy dispersiv...
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Novel orange-red Sr_(2)GdSbO_(6):xEu^(3+)(x=0,0.05,0.1,0.2,0.3,0.4,0.5 and 0.6) phospho rs were successfully prepared by the traditional high-temperature solid-state *** results of Rietveld refinement,energy dispersive spectroscopy(EDS) spectrum and elemental mapping demonstrate that Eu^(3+) successfully replaces the Gd^(3+) sites and distributes uniformly in the particles of *** luminescence properties of Sr_(2)GdSbO_(6):Eu_(3+)phosphors were investigated in *** emission spectra of the strongest emission peak is the ^(5)D_(0)→^(7)F_(1)(593 nm) transition,which can emit orange-red light under393 nm *** the doping concentration of Eu3+ions is x=0.2,the luminescence intensity of the phosphors reaches the *** detailed mechanism of concentration quenching is attributed to dipole-dipole *** thermal stability values of Sr_(2)GdSbO_(6):0.2Eu^(3+) phosphors are 87%,82% and114% under 393,467 and 527 nm excitations,*** causes of the abnormal thermal quenching under 527 nm excitation were *** on the abnormal thermal quenching under527 nm excitation,the optical thermometry properties of Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors were investigated by fluorescence intensity ratio(FIR) technique,and appreciable relative sensitivity was *** results suggest that Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors can be potentially applied to w-LEDs and optical thermometers.
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