PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online

Quantifying the average of the time-varying hazard ratio via a class of transformations

The hazard ratio derived from the Cox model is a commonly used summary statistic to quantify a treatment effect with a time-to-event outcome. The proportional hazards assumption of the Cox model, however, is frequently violated in practice and many alternative models have been proposed in the statistical literature. Unfortunately, the regression coefficients obtained from different models are often not directly comparable. To overcome this problem, we propose a family of weighted hazard ratio measures that are based on the marginal survival curves or marginal hazard functions, and can be estimated using readily available output from various modeling approaches. The proposed transformation family includes the transformations considered by [18] as special cases. In addition, we propose a novel estimate of the weighted hazard ratio based on the maximum departure from the null hypothesis within the transformation family, and develop a Kolmogorov–Smirnov type of test statistic based on this estimate. Simulation studies show that when the hazard functions of two groups either converge or diverge, this new estimate yields a more powerful test than tests based on the individual transformations recommended in [18], with a similar magnitude of power loss when the hazards cross. The proposed estimates and test statistics are applied to a colorectal cancer clinical trial

Bayesian gamma frailty models for survival data with semi-competing risks and treatment switching

Motivated from a colorectal cancer study, we propose a class of frailty semi-competing risks survival models to account for the dependence between disease progression time, survival time, and treatment switching. Properties of the proposed models are examined and an efficient Gibbs sampling algorithm using the collapsed Gibbs technique is developed. A Bayesian procedure for assessing the treatment effect is also proposed. The Deviance Information Criterion (DIC) with an appropriate deviance function and Logarithm of the Pseudomarginal Likelihood (LPML) are constructed for model comparison. A simulation study is conducted to examine the empirical performance of DIC and LPML and as well as the posterior estimates. The proposed method is further applied to analyze data from a colorectal cancer study

Implementation and optimization suggestions for ISI items of CPR1000 steam generator

The implementation logic of different ISI items of steam generator during the refueling outage of the nuclear power plant was studied. Based on the implementation relationship between the implementation window and the main line plan of the refueling outage, some optimization suggestions on the planning, preparation and implementation of relevant items were given, aimed at reducing the inspection time of SG ISI items and reducing the collective dose of the inspectors

Development and Application of MRF Based on Robot Arm

Magnetorheological Finishing (MRF) is widely regarded as an effective technique to polish and figure aspheric optics. MRF based on Robot Arm is developed by us. This new machine is more flexible, efficient, cost-effective and smaller space-usage in optical shop, compared with the traditional MRF machines. The components of MRF based on Robot Arm are introduced firstly. Position-attitude control and polishing tool path are also studied in this paper. The experiments and application of MRF based on Robot Arm demonstrate the effectiveness and validity of MRF based on Robot Arm in optical fabrication

Rigdelet Neural Networks-based Maximum Power Point Tracking for a PEMFC connected to the network with Interleaved Boost Converter optimized by Improved Satin Bowerbird Optimization

This paper proposes a new control policy for optimal control of the 3-phase system of PEMFC connected to the grid. This also includes a 3-phase high step-up Interleaved Boost Converter (IBC) to amplify the PEMFC outputted voltage. To control the PEMFC system, Maximum Power Point Tracking (MPPT) based on Rigdelet Neural Networks (RNN) has been utilized, and to improve this controller, an improved version of the Satin Bowerbird Optimization (ISBO) algorithm has been utilized. The main advantage of the proposed improved version is modifying the convergence weakness and fixing convergence in chaos theory. The method is then validated by performing it one time on a standalone PEMFC system and another time on a grid-connected PEMFC system. Simulation results indicate that based o the IBC converter, better results with lower current ripples can be achieved. Also, the method has the ability to feed to both active and reactive powers by keeping stable the sudden temperatures. Final results have been also put in comparison with two different latest techniques to indicate the technique proficiency

Development and Application of MRF Based on Robot Arm

Magnetorheological Finishing (MRF) is widely regarded as an effective technique to polish and figure aspheric optics. MRF based on Robot Arm is developed by us. This new machine is more flexible, efficient, cost-effective and smaller space-usage in optical shop, compared with the traditional MRF machines. The components of MRF based on Robot Arm are introduced firstly. Position-attitude control and polishing tool path are also studied in this paper. The experiments and application of MRF based on Robot Arm demonstrate the effectiveness and validity of MRF based on Robot Arm in optical fabrication

Rapid fabrication of a lightweight 2 m reaction-bonded SiC aspherical mirror

Magnetorheological finishing (MRF) fabrication can effectively facilitate a rapid fabrication of an aspherical 2 m reaction-bonded silicon carbide mirror. The dwell-time algorithm and tool path supported tool-mark mitigation and virtual-axis employment are analyzed. A rapid-fabrication strategy alternates MRF and a large polishing lap to converge surface error profile before MRF alone is used to high-precise finishing. In the alternate-use process, large polishing lap’s dwell time map for low-order space frequency is calculated before MRF’s for high-order one, but executed later in reality. The fabricated mirror with a silicon modification layer showed accuracy convergence from 0.098 λ rms to 0.019 λ rms at 84.6 h, demonstrating the strategy’s validity for large optical-surface processing. Keywords: Computer controlled polishing, Silicon carbide mirror, Surface error, Magnetorheological finishing, Large polishing la