Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature

The multi-purpose forensics is an important tool for forge image detection. In this paper, we propose a universal feature set for the multi-purpose forensics which is capable of simultaneously identifying several typical image manipulations, including spatial low-pass Gaussian blurring, median filtering, re-sampling, and JPEG compression. To eliminate the influences caused by diverse image contents on the effectiveness and robustness of the feature, a residual group which contains several highpass filtered residuals is introduced. The partial correlation coefficient is exploited from the residual group to purely measure neighborhood correlations in a linear way. Besides that, we also combine autoregressive coefficient and transition probability to form the proposed composite feature which is used to measure how manipulations change the neighborhood relationships in both linear and non-linear way. After a series of dimension reductions, the proposed feature set can accelerate the training and testing for the multipurpose forensics. The proposed feature set is then fed into a multi-classifier to train a multi-purpose detector. Experimental results show that the proposed detector can identify several typical image manipulations, and is superior to the complicated deep CNN-based methods in terms of detection accuracy and time efficiency for JPEG compressed image with low resolution.     

Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

基于水位和水位—流量关系的险情状态分析评估

为了正确分析评估防汛抢险中抢护后的管涌、漏洞等险情状态，根据防汛抢险实践和相关溃口失事案例，分析评估了流量与水位正、负相关时和无法观测流量时水位对险情状态的影响程度。水位的涨落及水位与堤内出险处流量的相关关系，对险情分析与状态评估具有一定的参考价值和提示作用。出险处流量随水位的上涨（下降）而增加（减少）时应注意观测围井内的含沙量和反滤料的变化情况，并排查该处险情附近区域有无新的险情，或已发生的新险情处理后有无异常迹象。     

A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.     

Development and evaluation of sub-element testing of SiC/SiC ceramic matrix composites at elevated temperatures

SiC/SiC ceramic matrix composites (CMCs) are being developed for use in aero-engines to replace nickel superalloy components. Sub-element testing acts as the key stepping stone in bridging understanding derived from basic coupon testing and more complex component testing. This study presents the development of high temperature C-shape sub-element testing with the use of digital image correlation to study damage progression. The specimen is designed with a bias towards a mixed mode-stress state more similar to what a CMC component may see in service. Both monotonic and fatigue tests were completed on C specimens and compared with predicted behaviour from modelling. Test data from both test types suggested that specimens were failing once they reached a critical radial stress level. However evidence from fractography of specimens showed that in both monotonic and fatigue tests radial cracks (driven by hoop stresses) are initiating prior to circumferential cracks.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

Advances and perspectives on one-dimensional nanostructure electrode materials for potassium-ion batteries

Potassium-ion batteries (PIBs) have aroused considerable interest as a promising next-generation advanced large-scale energy storage system due to the abundant potassium resources and high safety. However, the K⁺ with large ionic radius brings restricted diffusion kinetics and severe volume expansion in electrode materials, resulting in inferior actual rate characteristics and rapid capacity fading. Designing electrode materials with one-dimensional (1D) nanostructure can effectively enhance various electrochemical properties due to the well-guided electron transfer pathways, short ionic diffusion channels and high specific surface areas. In this review, we summarize the recent research progress and achievements of 1D nanostructure electrode materials in PIBs, especially focusing on the development and application of cathode and anode materials. The nanostructure, synthetic methods, electrochemical performances and structure-performance correlation are discussed in detail. The advanced characterizations on the reaction mechanisms of 1D nanostructure electrode materials in PIBs are briefly summarized. Furthermore, the main future research directions of 1D nanostructure electrode materials are also predicted, hoping to accelerate their development into the practical PIBs market.