A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

Characterization of 500 Chinese patients with cervical esophageal cancer by clinicopathological and treatment outcomes

Objective: There are no comprehensive studies on survival outcomes and optimal treatment protocols for cervical esophageal cancer(CEC), due to its rare clinical prevalence. Our objective was to determine the relationship between pathological characteristics, treatment protocols, and survival outcomes in Chinese CEC patients.Methods: A total of 500 Chinese CEC patients were selected from our 500,000 esophageal and gastric cardia carcinoma database(1973–2018). There were two main groups: patients treated with surgery, and patients receiving non-surgical treatments(radiotherapy, radiochemotherapy, and chemotherapy). The Chi-square test and Kaplan–Meier method were used to compare the continuous variables and survival.Results: Among the 500 CEC patients, 278(55.6%) were male, and the median age was 60.9 ± 9.4 years. A total of 496 patients(99.2%) were diagnosed with squamous cell carcinoma. In 171(34.2%) patients who received surgery, 22(12.9%) had undergone laryngectomy. In 322(64.4%) patients who received non-surgical treatments, 245(76.1%) received radiotherapy. Stratified survival analysis showed that only T stage was related with survival outcomes for CEC patients in the surgical group, and the outcomes between laryngectomy and non-laryngectomy patients were similar. It was noteworthy that the 5-year survival rate was similar in CEC patients among the different groups treated with surgery, radiotherapy, chemotherapy, or radiochemotherapy(P = 0.244). Conclusions: The CEC patients had similar survival outcomes after curative esophagectomy and radiotherapy, including those with or without total laryngectomy. These findings suggest that radiotherapy could be the initial choice for treatment of Chinese CEC patients.

MoS_(2) on topological insulator Bi_(2)Te_(3) thin films:Activation of the basal plane for hydrogen reduction

2H-MoS_(2) is a well-studied and promising non-noble metal electrocatalyst for heterogeneous reactions,such as the hydrogen evolution reaction(HER).The performance is largely limited by the chemically inert basal plane,which is unfavorable for surface adsorption and reactions.Herein,we report a facile method to boost the HER activities of 2H-MoS_(2) by coupling with epitaxial Bi2Te3 topological insulator films.The as-obtained MoS_(2)/Bi2Te3/SrTiO3 catalyst exhibits prominent HER catalytic activities compared to that of pure MoS_(2) structures,with a 189 mV decrease in the overpotential required to reach a current density of 10 mA cm^(−2) and a low Tafel slope of 58 mV dec−1.Theoretical investigations suggest that the enhanced catalytic activity originates from the charge redistribution at the interface between the Bi2Te3topological insulator films and the MoS_(2) layer.The delocalized sp-derived topological surface states could denote electrons to the MoS_(2) layer and activate the basal plane for hydrogen adsorption.This study demonstrates the potential of manipulating topological surface states to design high-performance electrocatalysts.

A Lightweight Three-Factor User Authentication Protocol for the Information Perception of IoT

With the development of computer hardware technology and network technology,the Internet of Things as the extension and expansion of traditional computing network has played an increasingly important role in all professions and trades and has had a tremendous impact on people lifestyle.The information perception of the Internet of Things plays a key role as a link between the computer world and the real world.However,there are potential security threats in the Perceptual Layer Network applied for information perception because Perceptual Layer Network consists of a large number of sensor nodes with weak computing power,limited power supply,and open communication links.We proposed a novel lightweight authentication protocol based on password,smart card and biometric identification that achieves mutual authentication among User,GWN and sensor node.Biometric identification can increase the nonrepudiation feature that increases security.After security analysis and logical proof,the proposed protocol is proven to have a higher reliability and practicality.

近十年国外K-12计算思维测评的分析与启示

培养中小学生的计算思维是21世纪一个重要的课题,世界各国正逐步将计算思维纳入国家教育课程体系,可靠且有效的测评是计算思维进入教育课程体系的必要条件。但是,目前我国基础教育阶段计算思维测评的研究正处于初级阶段,亟待发展与完善,而国外K-12计算思维测评的研究较为成熟。基于以上现状,文章采取文献研究法,对国外近十年来K-12计算思维测评文献进行梳理分析,探究计算思维测评的理论依据、测评内容、测评情境、测评方式与工具、数据收集与分析以及测评结果,为我国K-12计算思维的测评提出以下建议:明确多元化的计算思维测评内容,选择针对性的计算思维测评工具,采用多样化的计算思维测评方式,关注计算思维多学科背景与形成性评价,实施计算思维个性化测评。

Effects of Mg-based Alkaline Peroxide Bleaching on Physical Properties of Poplar Bleached Chemi-thermomechanical Pulp and Its Influence Mechanism

In this study,Mg O was partially used as an alkali source in the peroxide bleaching process of bleached chemi-thermomechanical pulp(BCTMP).The effects of substitution percentage of Mg O for Na OH on the bulk,optical,and physical properties of bleached pulp,and the main effluent characteristics were analyzed.In addition,the influencing mechanism of Mgbased alkali on the strength properties of the BCTMP was further investigated.Strength properties of the BCTMPs were investigated as a function of charge characteristics,fiber morphology,surface lignin content,relative bonding area,and hydrogen bonds of the BCTMP.The results showed that cationic demand(CD) and chemical oxygen demand(COD_(Cr)) of the bleaching effluent decreased as the substitution percentage of Mg O for Na OH increased; meanwhile,the bulk and optical properties of the BCTMP increased.Nevertheless,the strength properties(tensile,tear,and burst indices) of the bleached pulp decreased as the substitution percentage of Mg O for Na OH increased.The decrease in the fiber charge density and increase in the surface lignin content affected the fiber swelling,resulting in a decline in pulp interfibers bonding strength and further loss of the tensile and burst indices.

粉煤灰输送系统F-K螺旋泵轴头叶片的改进

输送粉煤灰的F-K螺旋泵,维护中常见螺旋轴密封位置磨损、内筒衬板磨损、螺旋轴轴承故障等现象,多与轴头叶片磨损有关。统计发现叶片引起的故障比例占F-K螺旋泵故障的87%。灰尘颗粒的进入、轴头叶片的耐磨性能、粉煤灰的化学成分以及细度影响着轴头叶片的使用寿命。实践认为,解决F-K螺旋泵轴头叶片维护维修最经济的办法是:调整螺旋轴头部叶片螺旋节距与堆焊+堆焊棱条相结合。

Image-to-Image Style Transfer Based on the Ghost Module

The technology for image-to-image style transfer(a prevalent image processing task)has developed rapidly.The purpose of style transfer is to extract a texture from the source image domain and transfer it to the target image domain using a deep neural network.However,the existing methods typically have a large computational cost.To achieve efficient style transfer,we introduce a novel Ghost module into the GANILLA architecture to produce more feature maps from cheap operations.Then we utilize an attention mechanism to transform images with various styles.We optimize the original generative adversarial network(GAN)by using more efficient calculation methods for image-to-illustration translation.The experimental results show that our proposed method is similar to human vision and still maintains the quality of the image.Moreover,our proposed method overcomes the high computational cost and high computational resource consumption for style transfer.By comparing the results of subjective and objective evaluation indicators,our proposed method has shown superior performance over existing methods.

Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution,and its performance is mainly due to the two being in the same distribution state.When there is out-of-class data in unlabeled data,its performance will be affected.In practical applications,it is difficult to ensure that unlabeled data does not contain out-of-category data,especially in the field of Synthetic Aperture Radar(SAR)image recognition.In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model,this paper proposes a semi-supervised learning method of threshold filtering.In the training process,through the two selections of data by the model,unlabeled data outside the category is filtered out to optimize the performance of the model.Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset,and compared with existing several state-of-the-art semi-supervised classification approaches,the superiority of our method was confirmed,especially when the unlabeled data contained a large amount of out-of-category data.

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Pipeline defect detection systems collect the videos from cameras of pipeline robots,however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats.The existing systems tend to reach the limit in terms of data access anywhere,access security and video processing on cloud.There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection.In this paper,we deploy the framework of a cloud based pipeline defect detection system,including the user management module,pipeline robot control module,system service module,and defect detection module.In the system,we use a role encryption scheme for video collection,data uploading,and access security,and propose a hybrid information method for defect detection.The experimental results show that our approach is a scalable and efficient defection detection cloud system.

Underground Disease Detection Based on Cloud Computing and Attention Region Neural Network

Detecting the underground disease is very crucial for the roadbed health monitoring and maintenance of transport facilities,since it is very closely related to the structural health and reliability with the rapid development of road traffic.Ground penetrating radar(GPR)is widely used to detect road and underground diseases.However,it is still a challenging task due to data access anywhere,transmission security and data processing on cloud.Cloud computing can provide scalable and powerful technologies for large-scale storage,processing and dissemination of GPR data.Combined with cloud computing and radar detection technology,it is possible to locate the underground disease quickly and accurately.This paper deploys the framework of a ground disease detection system based on cloud computing and proposes an attention region convolution neural network for object detection in the GPR images.Experimental results of the precision and recall metrics show that the proposed approach is more efficient than traditional objection detection method in ground disease detection of cloud based system.

Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

In recent years,deep learning algorithms have been popular in recognizing targets in synthetic aperture radar(SAR)images.However,due to the problem of overfitting,the performance of these models tends to worsen when just a small number of training data are available.In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition,in this paper,we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model(RBnet)for SAR image target recognition.In the RBnet,a Bayesian classifier is used to improve the effect of SAR image target recognition and improve the accuracy when the training data is limited.The experimental results on MSTAR dataset show that the RBnet can fully exploit effective information in limited samples and recognize the target of the SAR images more accurately.Compared with other state-of-the-art methods,our method offers significant recognition accuracy improvements under limited training data.Noted that theRBnet is moderately difficult to implement and has the value of popularization and application in engineering application scenarios in the field of small-sample remote sensing target recognition and recognition.

Image Super-Resolution Reconstruction Based on Dual Residual Network

Research shows that deep learning algorithms can ffectivelyimprove a single image's super-resolution quality.However,if the algorithmis solely focused on increasing network depth and the desired result is not achieved,difficulties in the training process are more likely to arise.Simultaneously,the function space that can be transferred from a iow-resolution image to a high-resolution image is enormous,making finding a satisfactory solution difficult.In this paper,we propose a deep learning method for single image super-resolution.The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in low-resolution images.Finally,these features will be sent to the image reconstruction module torestore high-quality images.The function space is constrained by the closedloop formed by dual learning,which provides additional supervision forthe super-resolution reconstruction of the image.The up-sampling processincludes residual blocks with short-hop connections,so that the networkfocuses on learning high-frequency information,and strives to reconstructimages with richer feature details.The experimental results of ×4 and ×8 super-resolution reconstruction of the image show that the quality of thereconstructed image with this method is better than some existing experimental results of image super-resolution reconstruction in subjective visual ffectsand objective evaluation indicators.

Structural feature and tectonic evolution of Awati-Bachu area in Tarim Basin

The research area is situated in the western part of Tarim basin,which includes Awati depression and Bachu uplifted block. It underwent three times processes of compression in a large scale and a near term extension since Cambrian. The first compression occurred during Middle Cambrian to Devonian, which formed fault band folds in NW axial direction. They were "under-water uplift"and distributed all over the research area. The second compression occurred in Late Permian and formed fault band folds and a few fault propagation folds in NS axial direction. They are developed near Tumuxiuke fault belt and the northern research area. The western anticline is bigger than the eastern one in extent and size. The third compression occurred during Palaeogene to Quaternary and formed tumuxiuke fault belt and fault propagation folds in NW direction. They are distributed over the south part of the research area. Tumuxiuke fault belt is a big scale dextral reversed strike-slip fault belt; it transformed or destroyed the fold structure of the research area. A short-term extension occurred during Early Permian. Tarim Basin is in the rift forming stage of craton, and there exist widespread basic volcanic rocks, basic intrusive bodies and dikes.

Lossless and High Robust Watermarking of Electronic Chart for Copyright Protection

With the progression of sea exploration and offshore engineering, electronic charts have come to see widespread use in many intelligent applications. Like other digital products, electronic charts are easy to duplicate and distribute. Some watermarking solutions have proven defective to prevent copying of electronic charts because it’s as easy to forge as it is to redistribute. If the problems of copyright infringement cannot be solved, the creation of these electronic charts will be limited. The most important characteristic of electronic charts is the topological relationships among vertices, but few algorithms can control this feature. A new watermarking algorithm is here proposed as a means of copyright protection, in which the watermarks will be hosted in the electronic chart by taking into account the preservation of the topology. Sometimes, additional vertices are inserted into the middle of two adjacent vertices, sometimes not, which are governed by the value of the watermark. Experiments show that the improved algorithm is better than similar algorithms; it was found to resist geometric attacks and format exchange attacks.

Recycling of waste aluminum scraps to fabricate sulfidated zero-valent iron-aluminum particles for enhanced chromate removal

Massive waste aluminum scraps produced from the spent aluminum products have high electron capacity and can be recycled as an attractive alternative to materials based on zerovalent iron(Fe^(0))for the removal of oxidative contaminants from wastewater.This study thus proposed an approach to fabricate micron-sized sulfidated zero-valent iron-aluminum particles(S-Al^(0)@Fe^(0))with high reactivity,electron selectivity and capacity using recycled waste aluminum scraps.S-Al^(0)@Fe^(0)with a three-layer structure contained zero-valent aluminum(Al^(0))core,Fe^(0) middle layer and iron sulfide(FeS)shell.The rates of chromate(Cr(Ⅵ))removal by S-Al^(0)@Fe^(0)at pH 5.0-9.0 were 1.6-5.9 times greater than that by sulfidated zero-valent iron(S-Fe^(0)).The Cr(Ⅵ)removal capacity of S-Al^(0)@Fe^(0)was 8.2-,11.3-and 46.9-fold greater than those of S-Fe0,zero-valent iron-aluminum(Al^(0)-Fe^(0))and Fe^(0),respectively.The chemical cost of S-Al^(0)@Fe^(0) for the equivalent Cr(Ⅵ)removal was 78.5%lower than that of S-Fe^(0).Negligible release of soluble aluminum during the Cr(Ⅵ)removal was observed.The significant enhancement in the reactivity and capacity of S-Al^(0)@Fe^(0)was partially ascribed to the higher reactivity and electron density of the Al0core than Fe^(0).More importantly,S-Al^(0)@Fe^(0) served as an electric cell to harness the persistent and selective electron transfer from the Al^(0)-Fe^(0) core to Cr(Ⅵ)at the surface via coupling Fe^(0)-Fe^(2+)-Fe^(3+)redox cycles,resulting in a higher electron utilization efficiency.Therefore,S-Al^(0)@Fe^(0) fabricated using recycled waste aluminum scraps can be a cost-effective and environmentally-friendly alternative to S-Fe^(0) for the enhanced removal of oxidative contaminants in industrial wastewater.

面向教育新基建的中小学校长信息化领导力评价量表设计

教育新基建是教育信息化创新发展阶段新的“体系化”擘画。中小学校长是教育信息化的重要推进力量,其信息化领导力随着教育信息化的发展不断演化。现有围绕学校信息化建设应用的中小学校长信息化领导力测评工具已然失效,急需迭代更新。面向教育新基建的新要求,从愿景规划、组织变革、校园建设、教学改革、学习创新、自我发展六个方面概括了校长信息化领导力的转变,对校长在信息化领导过程中扮演的角色进行了定义,设计了新的中小学校长信息化领导力评价框架,研究编制了面向教育新基建的中小学校长信息化领导力评价量表,经过探索性因子分析、验证性因子分析和信度检验等步骤,对820份有效数据进行了检测,最后依据检测结果修订了题项,评价量表结构良好,信效度高,可作为新时代中小学校长信息化领导力的有效测评工具。

A unique Janus PdZn-Co catalyst for enhanced photocatalytic syngas production from CO_(2) and H_(2)O

The development of excellent catalyst to achieve photocatalytic syngas production from CO_(2) and H_(2)O is a prospective and sustainable strategy to alleviate environment and energy crisis. In this study, a unique Janus PdZn-Co catalyst is prepared by annealed the Pd/IRMOF-3(Co, Zn) precursor. Due to the strong interaction, the electron transfers from PdZn terminal to Co terminal in the Janus structure. The electron-received Co terminal facilitates Co sites coordinate with the electrophilic C atom of CO_(2) and the electron-donated PdZn center is easier to coordinate with nucleophilic O atoms of H_(2)O or C=O bonds.The charge redistribution enhances the absorption of CO_(2) and H2O, which promotes H_(2) evolution and CO production. In addition, the carbon shell effectively suppresses the metal core agglomeration and facilitates the electron transmission from photosensitizer to metallic active sites. Meanwhile, the ratio of CO/H_(2) can be regulated(~3:1 to 2:1) by adjusting the proportion of Co and PdZn. The Janus structure and graphite carbon synergistically play a profound impact on improving the photocatalytic performance.The optimized PdZn-Co catalyst exhibits a superior photocatalytic CO production rate(20.03 μmol/h) and the H_(2) generation rate(9.90 μmol/h) with a ratio of CO/H_(2)= 2.02.

An adaptive LIC based geographic flow field visualization method by means of rotation distance

Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields,existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution,resulting in incomplete and inaccurate vortex information.To address this,we propose an adaptive Line Integral Convolution(LIC)based geographic flow field visualization method by means of rotation distance.Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively.We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution,with the integration step size adjusted.This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields.Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.

A mouse model for HBV immunotolerance and immunotherapy

Lack of an appropriate small animal model remains a major hurdle for studying the immunotolerance and immunopathogenesis induced by hepatitis B virus （HBV） infection. In this study, we report a mouse model with sustained HBV viremia after infection with a recombinant adeno-associated virus （AAV） carrying a replicable HBV genome （AAV/ HBV）. Similar to the clinical HBV carriers, the mice infected with AAV/H BV were sero-negative for antibodies against HBV surface antigen （HBsAg）. Immunization with the conventional HBV vaccine in the presence of aluminum adjuvant failed to elicit an immune response against HBV in these mice. To identify a vaccine that can potentially circumvent this tolerance, the TLR9 agonist CpG was added to HBsAg as an adjuvant. Vaccination of mice with HBsAg/CpG induced not only clearance of viremia, but also strong antibody production and T-cell responses. Furthermore, both the DNA replication and protein expression of HBV were significantly reduced in the livers of AAV/H BV-infected mice. Accordingly, AAV/HBV-infected mice may be used as a robust model for investigating the underlying mechanism（s） of HBV immunotolerance and for developing novel immunotherapies to eradicate HBV infections.