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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2010年第12期11卷 931-936页

作者： Peng ZAN bang-hua yang Yong SHAO Guo-zheng YAN hua LIU Shanghai Key Laboratory of Power Station Automation Technology College of Meehatronics Engineering and Automation Shanghai University Shanghai 200072 China School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China

This paper reports on the electromagnetic effects on the biological tissue surrounding a transcutaneous transformer for an artificial anal *** coupling coils and human tissues,including the skin,fat,muscle,liver,and blood,were *** absorption rate (SAR) and current density were analyzed by a finite-length solenoid ***,SAR and current density as a function of frequency (10–107 Hz) for an emission current of 1.5 A were calculated under different tissue *** relations between SAR,current density,and five types of tissues under each frequency were *** a result,both the SAR and current density were below the basic restrictions of the International Commission on Non-Ionizing Radiation Protection (ICNIRP).The results show that the analysis of these data is very important for developing the artificial anal sphincter system.

关键词： Artificial anal sphincter Transcutaneous energy transmission Current density Specific absorption rate

Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface

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Frontiers of Information Technology & Electronic Engineering 2015年第6期16卷 486-496页

作者： bang-hua yang Liang-fei HE Lin LIN Qian WANG Shanghai Key Laboratory of Power Station Automation Technology Department of AutomationSchool of Mechatronic Engineering and Automation Shanghai University Shanghai 200072 China

Ocular artifacts cause the main interfering signals within electroencephalogram （EEG） signal measurements. An adaptive filter based on reference signals from an electrooculogram （EOG） can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces （BCIs）, a method named spatial constraint independent component analysis based recursive least squares （SCICA-RLS） is proposed. The method consists of two stages. In the first stage, independent component analysis （ICA） is used to decompose multiple EEG channels into an equal number of independent components （ICs）. Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition （EMD） is employed to remove any cerebral activity from the identified ocular ICs to obtain exact altifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs （SC-ICs）. Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction.

关键词： Ocular artifacts Electroencephalogram (EEG) Electrooculogram (EOG) Brain-computer interface (BCI) Spatialconstraint independent component analysis based recursive least squares (SCICA-RLS)

Clinical outcome and prognostic factors of T4N0M0 colon cancer after R0 resection:A retrospective study

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World Journal of Gastrointestinal Oncology 2024年第5期16卷 1869-1877页

作者： bang Liu Zhao-Xiong Zhang Xin-yang Nie Wei-Lin Sun Yong-Jia Yan Wei-hua Fu Department of General Surgery Tianjin Medical University General HospitalTianjin Medical UniversityTianjin 300052China

BACKGROUND Paradoxically,patients with T4N0M0(stage II,no lymph node metastasis)colon cancer have a worse prognosis than those with T2N1-2M0(stage III).However,no previous report has addressed this *** To screen prognostic risk factors for T4N0M0 colon cancer and construct a prognostic nomogram model for these *** Two hundred patients with T4N0M0 colon cancer were treated at Tianjin Medical University General Hospital between January 2017 and December 2021,of which 112 patients were assigned to the training cohort,and the remaining 88 patients were assigned to the validation *** between the training and validation groups were *** training cohort was subjected to multi-variate analysis to select prognostic risk factors for T4N0M0 colon cancer,followed by the construction of a nomogram *** The 3-year overall survival(OS)rates were 86.2%and 74.4%for the training and validation cohorts,***(P=0.000),T stage(P=0.001),right hemicolon(P=0.025),irregular review(P=0.040),and carbohydrate antigen 199(CA199)(P=0.011)were independent risk factors of OS in patients with T4N0M0 colon cancer.A nomogram model with good concordance and accuracy was *** Enterostomy,T stage,right hemicolon,irregular review,and CA199 were independent risk factors for OS in patients with T4N0M0 colon *** nomogram model exhibited good agreement and accuracy.

关键词： T4N0M0 colon cancer Prognosis Multivariate analysis Nomogram Colon cancer

A Mothed of Improving Identification Accuracy via Deep Learning Algorithm under Condition of Deficient Labeled Data

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A Mothed of Improving Identification Accuracy via Deep Learn...

第36届中国控制会议

作者： Jian-Guo Wang Zhi-Duo Cao bang-hua yang Shi-Wei Ma Min-Rui Fei Hao Wang Yuan Yao Tao Chen Xiao-Fei Wang School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering “Tsing-Hua University” Department of Chemical and Process Engineering University of Surrey Shanghai Zhenhua Heavy Industries Company Limited

ISBN: (纸本)9781538629185

In industrial process, some important variables such as quality index, efficiency index and concentration of product components are difficult or even impossible to be measured directly due to the limitation of technology. This phenomenon leads to few labeled data and plenty of unlabeled data. Traditional identification method for controlled auto regressive（CAR） model usually cannot deal with unlabeled training data. As a result, these traditional identification methods may receive poor identification precision or even cannot work entirely. To solve the problems above, this paper proposes a new identification method based on deep learning（DL）. Firstly, the CAR model is transformed into finite impulse response（FIR） model solve the problem of lack of autoregressive part;Secondly, autoencoder of deep learning make full use of unlabeled data to pretrain the model;Thirdly, small amount of label data is used for fine-tuning. As a semi-supervised learning method, deep learning can be able to extract more information from unlabeled data than traditional supervised learning method. The results show that the proposed method can acquire higher identification accuracy than BP neural network.

关键词： System Identification Deep Learning Controlled Auto Regressive Finite Impulse Response

Simultaneous Identification of Parameter and Time-delay Based on Subspace Method and Cross-correlation Function

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Simultaneous Identification of Parameter and Time-delay Base...

第36届中国控制会议

作者： Jian-Guo Wang Zhi-Fu Deng bang-hua yang Shi-Wei Ma Min-Rui Fei Li-Lan Liu Yuan Yao Tao Chen Yi-Ping Wu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering “Tsing-Hua University” Department of Chemical and Process Engineering University of Surrey Shanghai Baosight Software Co Ltd

ISBN: (纸本)9781538629185

Both the plant parameter4 and delay time of the system may vary in the time-varying system, which makes system identification relatively complex. In this paper, a new system identification algorithm based on subspace method and cross-correlation function is proposed to identify system parameter and time-delay simultaneously. The time delay is identified with the calculation of the cross-correlation function between pre-processed input data that processed through initial plant parameters and original output data. Then, the time delay in the output data can be deleted with the time delay identified before and the parameters can be achieved by subspace identification algorithm. Therefore, the process parameters and time delay identification can be performed online by through above two alternative operations. The validity of the proposed identification algorithm is verified by the simulation study.

关键词： Subspace Method Cross-Correlation Time-Varying Identification

Non-destructive Testing of CFRP Using Pulsed Thermographic Data Enhanced by Wavelet Transform-based Image Denoising

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Non-destructive Testing of CFRP Using Pulsed Thermographic D...

第36届中国控制会议

作者： Jian-Guo Wang Zhi-Duo Cao bang-hua yang Shi-Wei Ma Min-Rui Fei Shi-Qi huang Yuan Yao Tao Chen Xiao-Fei Wang School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering National Tsing-Hua University Department of Chemical and Process Engineering University of Surrey Shanghai Zhenhua Heavy Industries Company Limited

ISBN: (纸本)9781538629185

Pulsed thermography（PT） is a widely used non-destructive testing（NDT） technique for defect detection in carbon fiber reinforced polymers（CFRP） *** signal reconstruction（TSR） is often employed to process thermographic data due to its excellent performance in enhancing the contrast between defects and ***,TSR does not reduce the noise of thermographic data *** solve this problem,this thesis proposes an image denoising method based on wavelet *** experiment results show that dealt with wavelet transform the characteristics of the defective regions are more clearly compared with previous *** the signal-to-noise ratio（SNR） values are improved obviously at the same time.

关键词： Pulsed Thermography Non-Destructive Testing Thermographic Signal Reconstruction Wavelet Transform

A Nonnegative Garrote-based Method for Multivariate Fault Isolation

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A Nonnegative Garrote-based Method for Multivariate Fault Is...

第36届中国控制会议

作者： Yi-Min Guo Jian-Guo Wang bang-hua yang Shi-Wei Ma Min-Rui Fei Li-Lan Liu Hui Xu Yuan Yao Yi-Ping Wu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering National Tsing-Hua University Shanghai Baosight Software Co Ltd

ISBN: (纸本)9781538629185

Fault isolation is an essential step in multivariate statistical process monitoring,which discovers the cause of the detected *** common adopted techniques for fault isolation,such as contribution plots,reconstruction analysis,branch and bound,etc.,often suffer from the problems of the smearing effect,insufficient historical fault data,and heavy computational *** this paper,the nonnegative garrote（NNG） estimator is utilized for multivariate statistical fault isolation,which solves the problems of the existing *** proposed method is based on the finding that there is equivalence between fault isolation and variable selection in discriminant *** from the existing methods,the NNG-based fault isolation method does not suggest a single set of faulty ***,it provides a sequence of process variables according to their responsibilities for the detected *** results are informative for diagnosing the root cause of the fault.

关键词： Fault isolation Variable selection Nonnegative garrote Process monitoring

A Soft Sensing Method for Operation Optimization of Coke Dry Quenching Process

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A Soft Sensing Method for Operation Optimization of Coke Dry...

第36届中国控制会议

作者： Jian-Guo Wang Zhi-Duo Cao Li-Lan Liu bang-hua yang Shi-Wei Ma Min-Rui Fei Zhi-Fu Deng Yuan Yao Yi-Ping Wu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering “Tsing-Hua University” Shanghai Baosight Software Co Ltd

ISBN: (纸本)9781538629185

Based on the actual data analysis, it is found in this paper that the supplementary air flow rate in the CDQ operation didn’t follow the variation of the discharge rate of incandescent coke well, which results in the concentration increase of combustible gas in the exhaust gas and the decrease of economic efficiency. The correlation analysis results show that the introduced derived variables are more useful than some plain variables for the purpose of prediction. Next, to handle the contradiction between the steam productivity and the coke burning loss, a new economic efficiency index is introduced by synthesizing the two competing aspects. A kind of soft sensor of economic efficiency is put forward by combining nonnegative garrote（NNG） variable selection algorithm with the autoregressive integrated moving average（ARIMA） model, which gives a good solution to the economic efficiency real-time prediction problem of CDQ system. Then, the implementation of model-based optimization is studied based on the actual operation data. The results show that there exists large room for economic efficiency promotion.

关键词： Soft sensor Coke dry quenching Data-driven Variable selection Autoregressive integrated moving average

A Deep Learning-based Operation Optimization Strategy for BFG/coal Co-firing Boiler

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A Deep Learning-based Operation Optimization Strategy for BF...

第36届中国控制会议

作者： Jian-Guo Wang Jin-Qiu Min Li-Lan Liu bang-hua yang Shi-Wei Ma Min-Rui Fei Yi-Min Guo Yuan Yao Yi-Ping Wu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering National Tsing-Hua University Shanghai Baosight Software Co Ltd

ISBN: (纸本)9781538629185

How to operate a BFG/coal co-firing boiler in high efficiency is challenging for a gas/solid multi-fuel combustion *** operation data from a real boiler,this study proposes an operation optimization strategy of BFG/coal co-firing boiler based on deep ***,the thermal efficiency model is constructed based on deep learning with all the actual sampling data,which outperform the other modeling ***,the high efficiency data being selected as labeled data,a deep learning-based model of air flow is *** needs to be emphasized that,to take full advantage of all available information and improve the model accuracy,all the labeled and unlabeled data are utilized in auto-coder learning of the air flow ***,the constructed air flow model is used for the operation optimization and the thermal efficiency model is used to evaluate the level efficiency *** high accuracy of the proposed modeling approaches makes the implementation of the operation optimization strategy readily practicable.

关键词： BFG/coal co-firing thermal efficiency auto-coder deep learning data-driven

TRPC6 Knockout Alleviates Renal Fibrosis through PI3K/AKT/GSK3B Pathway

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Current Medical Science 2024年第3期44卷 589-602页

作者： An-bang SUN Fang-hua LI Lin ZHU Xi-xi ZENG Min ZHU Qing-hua LEI Yan-hong LIAO Department of Anatomy School of Basic MedicineTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China Department of Transfusion Medicine Wuhan Hospital of Traditional Chinese and Western MedicineTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China Department of Nephrology The First Affiliated Hospital of Zhengzhou UniversityZhengzhou450052China Department of Thoracic Surgery Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China National Basic Medical Teaching Demonstration Center Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China Key Laboratory of Neurological Diseases of Ministry of Education Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China

Objective Renal fibrosis is the ultimate pathway of various forms of acute and chronic kidney ***,the knockout of transient receptor potential channel 6(TRPC6)has shown promise in alleviating renal ***,the regulatory impact of TRPC6 on renal fibrosis remains *** In vivo,TRPC6 knockout(TRPC6−/−)mice and age-matched 129 SvEv(WT)mice underwent unilateral renal ischemia-reperfusion(uIR)injury surgery on the left renal pedicle or sham *** and serum were collected on days 7,14,21,and 28 after *** vitro,primary tubular epithelial cells(PTECs)were isolated from TRPC6−/−and WT mice,followed by treatment with transforming growth factorβ1(TGFβ1)for 72 *** anti-fibrotic effect of TRPC6−/−and the underlying mechanisms were assessed through hematoxylin-eosin staining,Masson staining,immunostaining,qRT-PCR,and Western *** Increased TRPC6 expression was observed in uIR mice and PTECs treated with TGFβ***6−/−alleviated renal fibrosis by reducing the expression of fibrotic markers(Col-1,α-SMA,and vimentin),as well as decreasing the apoptosis and inflammation of PTECs during fibrotic progression both in vivo and in ***,we found that the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)/glycogen synthase kinase 3 beta(GSK3β)signaling pathway,a pivotal player in renal fibrosis,was down-regulated following TRPC6 *** These results suggest that the ablation of TRPC6 may mitigate renal fibrosis by inhibiting the apoptosis and inflammation of PTECs through down-regulation of the PI3K/AKT/GSK3β*** TRPC6 could be a novel therapeutic strategy for preventing chronic kidney disease.

关键词： transient receptor potential channel 6 ischemia-reperfusion injury renal fibrosis renal tubular epithelial cells