Hyperspectral Image Super-Resolution Network Based on Reinforcing Inter-Spectral Incremental Information

Hyperspectral images typically have high spectral resolution but low spatial resolution,which impacts the reliability and accuracy of subsequent applications,for example,remote sensingclassification and mineral identification.But in traditional methods via deep convolution neural net-works,indiscriminately extracting and fusing spectral and spatial features makes it challenging toutilize the differentiated information across adjacent spectral channels.Thus,we proposed a multi-branch interleaved iterative upsampling hyperspectral image super-resolution reconstruction net-work(MIIUSR)to address the above problems.We reinforce spatial feature extraction by integrat-ing detailed features from different receptive fields across adjacent channels.Furthermore,we pro-pose an interleaved iterative upsampling process during the reconstruction stage,which progres-sively fuses incremental information among adjacent frequency bands.Additionally,we add twoparallel three dimensional(3D)feature extraction branches to the backbone network to extractspectral and spatial features of varying granularity.We further enhance the backbone network’sconstruction results by leveraging the difference between two dimensional(2D)channel-groupingspatial features and 3D multi-granularity features.The results obtained by applying the proposednetwork model to the CAVE test set show that,at a scaling factor of×4,the peak signal to noiseratio,spectral angle mapping,and structural similarity are 37.310 dB,3.525 and 0.9438,respec-tively.Besides,extensive experiments conducted on the Harvard and Foster datasets demonstratethe superior potential of the proposed model in hyperspectral super-resolution reconstruction.

k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation

Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.

ADAPTIVE AND ACTIVE COMPUTING PARADIGM FOR PERSONALIZED INFORMATION SERVICE IN DISTRIBUTED HETERONGEOUS ENVIRONMENT

To solve the problem that traditional pull based information service can’t meet the demand of long term users getting domain information timely and properly, an adaptive and active computing paradigm (AACP) for personalized information service in heterogeneous environment is proposed to provide user centered, push based higsh quality information service timely in a proper way, the motivation of which is generalized as R 4 Service: the right information at the right time in the right way to the right person, upon which formalized algorithms framework of adaptive user profile management, incremental information retrieval, information filtering, and active delivery mechanism are discussed in details. The AACP paradigm serves users in a push based, event driven, interest related, adaptive and active information service mode, which is useful and promising for long term user to gain fresh information instead of polling from kinds of information sources.

炼油企业CIMS研究与实践

结合“九五”期间CIMS实施的经历 ,从炼油企业所面临的新问题出发提出了实施CIMS的必要性 ,以炼油企业增值链为基础描述了炼油企业CIMS的一般结构和实施原则 ,最后提出一个实例 ,简要介绍了CL -CIMS的功能结构。

Adaptive LII-RMPLS based data-driven process monitoring scheme for quality-relevant fault detection

The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-varying data processing and quality-relevant fault detecting.How-ever,it encounters heavy computational load in model updating,and the static control limits often lead to the low fault detection rate(FDR)or high false alarm rate(FAR).In this article,we first introduce the recursive MPLS(RMPLS)method for quality-relevant fault detection and computational complexity reducing,and then combine the local information increment(LII)method to obtain the time-varying control limits.First,the proposed LII-RMPLS method is capa-ble of quality-relevant faults detection.Second,the adaptive threshold leads to higher FDRs and lower FARs compared with traditional methods.Third,the adaptive parameter-matrices-based model updating approach ensures that the proposed method has better robustness and lower computational complexity when dealing with time-varying factors.

Critical audit matters and stock price crash risk

Using manually collected data on the number and category of critical audit matters(CAMs)in the period 2016–2017,we investigate the hitherto unexplored questions of whether CAMs affect firm-specific crash risk,how CAMs influence crash risk in the Chinese capital market,and recognize CAMs that contain incremental information.Our findings are as follows:(1)Crash risk decreases after implementing the new audit standard requiring the disclosure of CAMs;(2)CAMs release negative information and change the capital market information environment;(3)only corporateidiosyncratic CAMs contain incremental information;(4)crash risk is mitigated only by CAMs disclosed by companies with a high shareholding of institutional investors.The main conclusion of our study is a positive assessment of the new audit standard and of CAMs in terms of protecting the interests of investors and strengthening the stability of the capital market to provide a new perspective for supervising the implementation of the new audit standard.