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Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

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Chinese Journal of Aeronautics 2021年第6期34卷 162-177页

作者： Zhanying CHEN Liping WANG Jiabo ZHANG Guoqiang GUO Shuailei FU Chao WANG Xuekun LI Department of Mechanical Engineering Tsinghua UniversityBeijing 100084China State Key lab of Tribology Tsinghua UniversityBeijing 100084China Beijing Spacecrafts China Academy of Space TechnologyBeijing 100094China Shanghai Spaceflight Precision Machinery Institute Shanghai 201600China

In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(*** shell)is of the same importance but overlooked in current *** the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large *** RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for *** leads to incompleteness in the remaining bottom thickness control and redundant measurement in *** this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall *** physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE *** to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,*** Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based ***,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction *** final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.

关键词： Data-driven Physics based model Thin-wall component Pocket milling Remaining bottom thickness error

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New algorithm of target classification in polarimetric SAR

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Journal of Systems Engineering and Electronics 2008年第2期19卷 273-279页

作者： Wang Yang Lu Jiaguo Wu Xianliang Key lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui Univ. Heifei 230039 P. R. China The 38th Research Inst. China Electronic Technology Corporation Hefei 230031 P. R. China

The different approaches used for target decomposition （TD） theory in radar polarimetry are reviewed and three main types of theorems are introduced： those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine （SVM）, as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is proposed. To conduct the experiment, the polarimetric synthetic aperture radar （SAR） data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.

关键词： polarimetric synthetic aperture radar target decomposition support vector machine target classification kernel function.

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Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines

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International Journal of Automation and computing 2014年第3期11卷 274-287页

作者： Nassim Laouti Sami Othman Mazen Alamir Nida Sheibat-Othman Université de Lyon Université Lyon 1 CNRS CPE Lyon UMR 5007 LAGEP F-69616 Villeurbanne France Gipsa-lab/CNRS University of Grenoble Rue de la Houille Blanche 38400 Saint Martin d Heres France

Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection

关键词： Fault detection and isolation wind turbine Kalman-like observer support vector machines data-based classification

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Comparison of the efficacy of IA and HAD induction regimens in the treatment of patients with newly diagnosed acute myeloid leukemia:a single-center study

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China Medical Abstracts (Internal Medicine) 2022年第3期39卷 183-184页

作者：张丛笑 State Key Experiment Hematol lab Natl Clin Blood Dis Res CenterHaihe Cell Ecosystem LabInstit Hematol & Blood Dis HospCAMS & PUMC

Objective To compare the efficacy of two induction regimens,namely,idarubicin combined with cytarabine(IA) versus the combination of homoharringtonine,daunorubicin,and cytarabine (HAD),in adult patients with newly diagnosed de novo acute myeloid leukemia(AML). Methods From May 2014 to November 2019,199 patients diagnosed with AML receiving either the IA or HAD regimens were assessed for overall survival(os),relapse-free survival (RFS),as well as the CR rate and the MRD negative rate after induction therapy.

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Remote ischemic perconditioning prevents liver transplantation-induced ischemia/reperfusion injury in rats: Role of Ros/RNS and e Nos

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World Journal of Gastroenterology 2017年第5期23卷 830-841页

作者： Ning He Jun-Jun Jia Jian-Hui Li Yan-Fei Zhou Bing-Yi Lin Yi-Fan Peng Jun-Jie Chen Tian-Chi Chen Rong-Liang Tong Li Jiang Hai-Yang Xie Lin Zhou Shu-Sen Zheng Key lab of Combined Multi-Organ Transplantation Ministry of Public Health Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Division of Hepatobiliary and Pancreatic Surgery Department of SurgeryFirst Affiliated HospitalZhejiang University School of Medicine

AIM To investigate the underlying mechanisms of the protective role of remote ischemic perconditioning (RIPerC) in rat liver transplantation. METHODS Sprague-Dawley rats were subjected to sham, orthotopic liver transplantation (OLT), ischemic postconditioning (IPostC) or RIPerC. After 3 h reperfusion, blood samples were taken for measurement of alanine aminotransferase, aspartate aminotransferase, creatinine (Cr) and creatinine kinase-myocardial band (CK-MB). The liver lobes were harvested for the following measurements: reactive oxygen species (Ros), H2O2, mitochondrial membrane potential (Delta psi m) and total nitric oxide (NO). These measurements were determined using an Ros/H2O2, JC1 and Total NOx Assay Kit, respectively. Endothelial NO synthase (eNos) was analyzed by reverse transcription-polymerase chain reaction (RTPCR) and western blotting, and peroxynitrite was semiquantified by western blotting of 3-nitrotyrosine. RESULTS Compared with the OLT group, the grafts subjected to RIPerC showed significantly improved liver and remote organ functions (P os (P osphoserine eNos, both in protein and mRNA levels, was observed in liver graft of the RIPerC group as compared with the OLT group (P osine content was significantly reduced in the RIPerC group as compared with the OLT group (P ostC groups for all the results except Cr. The Cr level was lower in the RIPerC group than in the IPostC group (P ostC, and involves inhibition of oxidative stres

关键词： Liver transplantation Ischemia/reperfusion injury Remote ischemic perconditioning Endothelial nitric oxide synthase Reactive oxygen species

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Ship recognition based on HRRP via multi-scale sparse preserving method

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Journal of Systems Engineering and Electronics 2024年第3期35卷 599-608页

作者： YANG Xueling ZHANG Gong SONG Hu College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key lab of Radar Imaging and Microwave Photonics Ministry of EducationNanjing 210016China Nanjing Marine Radar Institute Nanjing 210016China

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

关键词： ship target recognition high-resolution range profile(HRRP) multi-scale fusion kernel sparse preserving projection(MSFKSPP) feature extraction dimensionality reduction

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A haplotype block associated with thousand-kernel weight on chromosome 5DS in common wheat(Triticum aestivum L.)

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Journal of Integrative Plant Biology 2015年第8期57卷 662-672页

作者： Yuquan Wang Chenyang Hao Jun Zheng Hongmei Ge Yang Zhou Zhengqiang Ma Xueyong Zhang Crop Genomics and Bioinformatics Center and National Key lab of Crop Genetics and Germplasm Enhancement College of Agricultural SciencesNanjing Agricultural University Key laboratory of Crop Gene Resources and Germplasm Enhancement Ministry of Agriculture/Institute of Crop Science the Chinese Academy of Agricultural Sciences

Spike number per unit area, number of grains per spike, and thousand-kernel weight（TKW） are important yield components for wheat（Triticum aestivum L.）. TKW has the highest heritability among the three components. We validated 27 simple sequence repeat（SSR） loci associated with TKW in an F2：5breeding population grown in four environments. A cfd78265 bpmarker on chromosome 5DS showed the strongest association with TKW and had a significantly positive effect on TKW compared to allele cfd78259 bp, with mean increases of 5.17, 3.63, 4.11, and 5.16 g in the four environments. Markers cfd67 and cfd40 flanking cfd78 also showed significantly positive associations with TKW with increases of 5.11, 3.29, 4.31, and 4.50 g for cfd67205, and4.98, 3.49, 4.06, and 4.84 g for cfd40187 compared with cfd67203 and cfd40190in the four environments, respectively. A major quantitative trait locus for TKW spanning 2.94 c M on chromosome 5DS was detected by association *** linkage disequilibrium（LD）（r2〉 0.2） was detected Resear among the three linked markers, which formed three haplotype blocks in the F2：5breeding population. Mean TKW differences between Hap B-I and Hap B-II were 5.80, 4.41, 4.02,and 5.06 g in the four environments, respectively. Moreover,significant LD was detected only between cfd78 and cfd67 and between cfd67 and cfd40 in a germplasm collection. This study provides a base for cloning genes related to TKW on chromosome 5DS.

关键词： Association mapping haplotype thousand-kernel weight Triticum oestivum

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Seismic Low-Frequency Shadow beneath Gas Hydrate in the Shenhu Area Based on the Stereoscopic Observation System

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Journal of Earth Science 2018年第3期29卷 669-678页

作者： Xueqin Liu Huaishan Liu Lei Xing Yanxin Yin Jianhua Wang Key lab of Submarine Geosciences and Prospecting Techniques Ministry of Education Ocean University of China Evaluation and Detection Technology laboratory of Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology National Engineering laboratory of Offshore Oil Exploration CNOOC Research Institute

Spectral analysis shows a low-frequency shadow under the BSR interface. Traditional low-frequency shadow analysis is based on stacked data. In order to understand the BSR low-frequency shadow more clearly, a frequency division analysis on stereoscopic observation seismic data based on the adaptive optimal-kernel （AOK） frequency analysis method is presented. It includes ocean-bottom seismometer （OBS） data （common receiver point data including vertical and horizontal components）, vertical cable data （common receiver point data） and horizontal cable data （stacked section of different offsets）. The OBS data frequency analysis gets a conclusion that vertical component has a significant effect on the low-frequency shadow, but the horizontal component did not. The vertical cable data shows that the low frequency band of vertical cable is wider than OBS. And then the horizontal cable data frequency analysis points out that the bigger the angle of incidence is, the more obvious the low-frequency shadow will be. The low-frequency shadow feature is shown in the stereoscopic observation field and the visual effect on com- mon reception point data is better. The lateral reservoir distribution characteristics are predicted from low-frequency shadow feature analysis of the hydrate BSR based on stereoscopic observation.

关键词： hydrate BSR stereoscopic observation low-frequency shadow.

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Detecting Double JPEG Compressed Color Images via an Improved Approach

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Computers, Materials & Continua 2023年第4期75卷 1765-1781页

作者： Xiaojie Zhao Xiankui Meng Ruyong Ren Shaozhang Niu Zhenguang Gao School of Sciences Beijing University of Posts and TelecommunicationsBeijing100876China Beijing Key lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science Framingham State UniversityFraminghamMA01772USA

Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.

关键词： Color image forensics double JPEG compression detection the same quantization matrix CNN

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Learning-based parameter prediction for quality control in three-dimensional medical image compression

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Frontiers of Information Technology & Electronic Engineering 2021年第9期22卷 1169-1178页

作者： Yuxuan HOU Zhong REN Yubo TAO Wei CHEN State Key lab of CAD&CG Zhejiang UniversityHangzhou 310058China The First Affiliated Hospital Zhejiang UniversityHangzhou 310003China

Quality control is of vital importance in compressing three-dimensional(3D)medical imaging *** com-pression parameters need to be determined based on the specific quality *** high efficiency video coding(HEVC),regarded as the state-of-the-art compression tool,the quantization parameter(QP)plays a dominant role in controlling *** direct application of a video-based scheme in predicting the ideal parameters for 3D medical image compression cannot guarantee satisfactory *** this paper we propose a learning-based parameter prediction scheme to achieve efficient quality *** kernel is a support vector regression(SVR)based learning model that is capable of predicting the optimal QP from both vid-eo-based and structural image features extracted directly from raw data,avoiding time-consuming processes such as pre-encoding and iteration,which are often needed in existing *** results on several datasets verify that our approach outperforms current video-based quality control methods.

关键词： Medical image compression High efficiency video coding(HEVC) Quality control Learning-based

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