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Simultaneous hydrogen production with photo reforming of lactic acid over MXene derived MoS_(2)/TiO_(2)/Ti_(3)C_(2)nanowires

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Chinese Journal of Catalysis 2025年第4期71卷 234-245页

作者： Seemal Mansoor Zixu Hu Yuxin Zhang Muhammad Tayyab Mazhar Khan Zeeshan Akmal Liang Zhou Juying Lei Muhammad Nasir Jinlong Zhang Key Laboratory for Advanced Materials Shanghai Engineering Research Center for Multi–media Environmental Catalysis and Resource UtilizationJoint International Research Laboratory of Precision Chemistry and Molecular EngineeringFeringa Nobel Prize Scientist Joint Research CenterSchool of Chemistry and Molecular EngineeringEast China University of Science&TechnologyShanghai 200237China National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical ProcessEast China University of Science and TechnologyShanghai 200237China State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin 150090HeilongjiangChina Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China Institute of Materials Research Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055GuangdongChina COMSATS Univ Islamabad Interdisciplinary Res Ctr Biomed Mat IRCBMLahore CampusLahore 54000Pakistan

Developing efficient photocatalysts to address collaborative energy and environmental crises still faces significant *** this report,we present a highly efficient MXene–based photocatalyst,which is combined with MoS_(2)nano patches and TiO_(2)/Ti_(3)C_(2)(TTC)nanowires through hydrothermal *** all the composites tested,the optimized photocatalyst gave a remarkable H_(2)and revolving polylactic acid(PLA)into pyruvic acid(PA).Achieving a remarkable H_(2)evolution rate of 637.1 and 243.2μmol g^(−1)h^(−1),in the presence of TEOA and PLA as a sacrificial reagent under UV-vis(λ≥365 nm)light *** improved photocatalytic activity is a result of the combination of dual cocatalyst on the surface of TTC photocatalyst,which create an ideal synergistic effect for the generation of PA and the production of H_(2)*** MoS_(2)TiO_(2)/Ti_(3)C_(2)(MTT)composite can generate more photoexcited charge carriers,leading to the generation of more active radicals,which may enhance the system's photocatalytic *** work aims at demonstrating its future significance and guide the scientific community towards a more efficient approach to commercializing H_(2)through photocatalysis.

关键词： Photo reforming Polylactic acid Hydrogen evolution Lactic acid Pyruvic acid

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Immunohistochemical Image Quality control Algorithm for Colorectal Cancer Based on Deep Learning

Immunohistochemical Image Quality Control Algorithm for Colo...

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第34届中国过程控制会议

作者： Xiaolei Guo Jinbo Zhang Pingjie Huang Weiting Ge Yuqi Cao Guangxin Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Cancer Institute (Key Laboratory of Cancer Prevention and Intervention China National Ministry of Education)the Second Affiliated HospitalZhejiang University School of Medicine

High-quality pathological images are essential for reliable computer-aided ***,tissue-detachment images and cancer tissue deficiency images,which cannot be effectively used,are inevitable in *** address this issue,we propose to use the deep learning method of EfficientNet-b4 to identify unqualified P53 immunohistochemical(IHC) images of colorectal *** view of the limited number of unqualified images,the data augmentation technique is used to expand the dataset and the Focal Loss is used to force the model to pay more attention to the unqualified *** obtain a recall rate of 96.59% for the cancer tissue deficiency images and 100% for tissue-detachment images in our results,which is superior to the existing *** work proves that the improved model can effectively recognize cancer tissue deficiency and tissuedetachment images.

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Re-defining the performance of just-in-time learning based soft sensor for high dimensional process data

Re-defining the performance of just-in-time learning based s...

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第32届中国过程控制会议(CPCC2021)

作者： Xiaoyu Jiang Zhiqiang Ge State Key Laboratory of Industrial Control Technology Institute of Industrial Process ControlCollege of Control Science and EngineeringZhejiang University

Just-in-time learning(JITL) is a widely used online soft sensing method for time-varying *** the increase of dimensionality of industrial dataset,both reliability and usability of JITL would be seriously damaged due to the sparseness caused in the high-dimensional data *** this paper,variational autoencoder is introduced as a generative model to provide virtual samples for augmenting high-dimensional sparse *** on this,a data augmentation just-in-time learning framework is formulated and implemented with two different ***,we carefully discuss the effect of virtual data on just-in-time learning,as well as the influence of virtual data volume,virtual data ratio and other factors on the proposed ***,a real industrial example is applied to verify the effectiveness of the proposed method,in which two indicators(RMSE and R2) have been improved by an average of 22%,15%,respectively,compared to the traditional JITL approach.

关键词： Data augmentation just-in-time learning variational autoencoder soft sensor

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Co-inoculation of Lactiplantibacillus pentosus 1 and Saccharomyces cerevisiae 31 for a salt-free fish sauce production from channel catfish (Ietalurus punetaus) bone

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Food Bioscience 2022年第6期50卷 1531-1539页

作者： Pei Gao Zhiqing Zhang Yi Ge Shining Cao Xiaojing Zhang Qixing Jiang Yanshun Xu Wenshui Xia Shaoquan Liu State Key Laboratory of Food Science and Technology School of Food Science and TechnologyCollaborative Innovation Center of Food Safety and Quality Control in Jiangsu ProvinceJiangnan UniversityLihu 1800WuxiJiangsu214122China Department of Food Science and Technology National University of SingaporeScience Drive 2Singapore117546Singapore National University of Singapore (Suzhou) Research Institute No.377 Linquan StreetSuzhou Industrial ParkSuzhouJiangsu215123China

The study aimed to evaluate the co-inoculation of non-fish sauce strains for a fish sauce production made from Ietalurus punetaus fish bone hydrolysate (FBH) without salt addition. Individual inoculation (Lactiplantibacillus pentosus 1, LP and Saccharomyces cerevisiae 31, SC), and co-inoculation of both strains (FS) were employed, including an uninoculated control. Both strains exhibited good growth in FBH without salt addition. Compared with individual inoculation and uninoculated control, co-inoculation (FS) produced acid and amino nitrogen more quickly, as well as displaying higher sugar consumption. No significant difference was observed in trichloroacetic acid (TCA)-soluble peptides among all groups (P > 0.05). Moreover, significantly lower levels of six bitter amino acids were observed after co-fermentation (P < 0.05). In addition, more alcohols, esters and acids with pleasant notes and fewer unsaturated aldehydes with fishy note were generated after co-inoculation, in the absence of traditional fish sauce off-odor notes. The study demonstrated that the co-inoculation of non-fish sauce strains were appropriate for producing a salt-free fish sauce with better flavor.

关键词： Ietalurus punetaus Fish bone hydrolysate Fish sauce Co-inoculation Flavor

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Advancing Offshore Operations with Digital Twin technology: A Case Study of FLNG Operations in Offshore Environments

Advancing Offshore Operations with Digital Twin Technology: ...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Siqi Zhang Xiaoling Liang Ruihang Ji Wanyue Jiang Shuzhi Sam Ge University of Toronto and National University of Singapore National University of Singapore Toronto Canada Electrical and Computer Engineering Department National University of Singapore Singapore Institute for Future and Shandong Key Laboratory of Industrial Control Technology Qingdao University Qingdao China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper delineates a meticulous case study centered on the deployment of digital twin technology in Unity3D to augment operations in offshore environments, with a particular emphasis on Floating Liquefied Natural Gas operations. It encompasses the integration of real-time meteorological data, the application of a Proportional-Integral-Derivative controller for refined vessel maneuvering, and the incorporation of an advanced voice navigation interface. The primary objective of this research is to substantially elevate the controllability, predictability, and operational efficacy within the realm of offshore asset management. The findings of this study significantly contribute to the enhancement of strategic decision-making frameworks and propel forward-looking innovations in the offshore sector, culminating in heightened operational efficiency and fortified risk management strategies.

关键词： Technological innovation PI control Navigation Real-time systems Digital twins Liquefied natural gas Asset management

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Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance

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Science China(Information Sciences) 2021年第7期64卷 181-192页

作者： Shanling DONG Guanrong CHEN Meiqin LIU Zheng-Guang WU College of Electrical Engineering Zhejiang University Department of Electrical Engineering City University of Hong Kong National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Institute for Advanced Study Chengdu University

This paper investigates the cooperative output regulation problem for heterogeneous nonlinear uncertain multiagent networked systems subject to actuator failure, bounded matched or mismatched disturbances or disturbances produced by a given linear exosystem. Accurate information about nonlinearity,actuator failure and disturbance may be completely unknown. First, a distributed finite-time observer is designed to estimate the dynamics of the exosystem on a finite-time interval over a communication ***, a neural-adaptive control protocol is proposed. It is shown that(i) closed-loop multiagent systems are asymptotically stable, with output synchronization errors that tend to zero in the absence of mismatched disturbance, and(ii) the states of the closed-loop multiagent systems and the output synchronization errors are bounded in the presence of mismatched disturbance. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control strategy.

关键词： cooperative output regulation actuator failure matched/mismatched disturbance distributed finite-time observer neural-adaptive control

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A Photoplethysmography Signal Compression Method of Extreme Learning Machine

A Photoplethysmography Signal Compression Method of Extreme ...

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Chinese control Conference (CCC)

作者： Yan Zhou Aimin An Jicheng Liu Yongxin Chou College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology Lanzhou China School of Electrical and Automatic Engineering Changshu Institute of Technology Suzhou China

As one of the important physiological signals for human health monitoring, there are massive photoplethysmography (PPG) signals and excessive demands on storage and network communication resources for long time monitoring. Therefore, it is desirable to compress the PPG signal where redundancy exists without losing important information. In this paper, a PPG signal compression method with extreme learning machine (ELM) is proposed. ELM does not require iterative adjustment of the hidden layer parameters and has the advantages of high generalization ability and fast learning speed. First, the PPG signal is segmented and processed as input and output data. Second, the parameters of the hidden layer based on the set training set is calculated. Then, the obtained parameters are used to compress and reconstruct the PPG signal, one PPG wave band at a time, until the end of the training. Comparing with the error Back Propagation Training (BP) neural network, for the 4 min sampled data, the average time to compress and reconstruct the PPG signal is 0.026±0.007, the MSE is 0.004±0.004 and the MAD is 0.031±0.026.

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Dynamic Graph Learning Soft Sensor in process Industry

Dynamic Graph Learning Soft Sensor in Process Industry

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Data Driven control and Learning Systems (DDCLS)

作者： Mingwei Jia Danya Xu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Department of Chemical Engineering National Tsing Hua University Taiwan

The development of an accurate soft sensor modeling method in the process industry remains a great challenge because the coupling relationship between variables is always intricate and difficult to model. In this work, a dynamic graph learning (DGL) soft sensor is proposed to alleviate this problem. The proposed model realizes the ability of the soft sensor to perceive the coupling relationship in real time by automatically learning the dynamic graph. Then, a causal convolutional mechanism and a multi-hop graph attention mechanism are used to systematically construct the dependencies of variables in the spatial-temporal dimension and model their variation patterns effectively. Finally, the proposed method is tested on the penicillin fermentation process and shown to be feasible and effective. The results showed that the change of the dynamic graph in the spatial-temporal dimension was in line with the process mechanism.

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Continuous Perception Garment Classification Based on Optical Flow Variation

Continuous Perception Garment Classification Based on Optica...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Huang, Li Yang, Tong Zhang, Yu Jiang, Rongxin Tian, Xiang Chen, Yaowu Institute of Advanced Digital Technologies and Instrumentation Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control and Technology Zhejiang University 310027 China Zhejiang Provincial Key Laboratory for Network Multimedia Technology Hangzhou310027 China Embedded System Engineering Research Center Ministry of Education of China Hangzhou310027 China

ISBN: (纸本)9781665481090

A novel continuous perception garment classification mechanism is proposed in this paper, with the aim to identify the correct category of the garment from a set of known categories. It has been observed that due to the severe folding and overlapped texture of garments, treating a video of the continuous deformation of cloth as a set of disordered static figures would be ineffective which leads to low classification precision performed by an image-based garment classifier. In contrast, a high-level decision making module that leverages the classification results of each single image would significantly increase the algorithm performance. In this paper, we incorporate the optical flow variation of deformable cloth between consecutive configurations as a representative of how it is traversing within the confidence interval of the image-based classifier. We claim that it is not the number of video frames but the sum of optical flow variation of the garment configuration between consecutive frames having the same category label that constitutes the belief of garment classification. In other words, if two consecutive visual appearances of the garment could be identified as the same category by the image-based classifier, then the more diverged that two configurations are, the more confident that the garment is correctly identified. Experimental comparisons between the state-of-the-art algorithm and the proposed algorithm in a public dataset have been provided which prove the validity of the proposed algorithm. © 2022 IEEE.

关键词： Decision making

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Predicting the Risk of Arterial Stiffness in Coal Miners Based on Different Machine Learning Models

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Biomedical and Environmental Sciences 2024年第1期37卷 108-111页

作者： CHEN Qian Wei HUANG Xue Zan DING Yu ZHU Feng Ren WANG Jia ZOU Yuan Jie DU Yuan Zhen ZHANG Ya Jun HUI Zi Wen ZHU Feng Lin MU Min The Project was supported by Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining Anhui University of Science and TechnologyHefei 232001AnhuiChina School of Public Health Anhui University of Science and TechnologyHefei 232001AnhuiChina Key Laboratory of Industrial Dust Prevention and Control Occupational Safety and HealthMinistry of EducationAnhui University of Science and TechnologyHefei 232001AnhuiChina Anhui Institute of Occupational Safety and Health Anhui University of Science and TechnologyHefei 232001AnhuiChina Joint Research Center of Occupational Medicine and Health Institute of Grand HealthHefei Comprehensive National Science CenterAnhui University of Science and TechnologyHefei 232001AnhuiChina Department of Occupational&Environmental Health School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030HubeiChina Key Laboratory of Environment and Health Ministry of Education&Ministry of Environmental Protectionand State Key Laboratory of Environmental Health(Incubating)School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030HubeiChina

Coal is one of the world’s main energy resources,accounting for approximately 68%of China’s current total power ***,several studies have demonstrated that dust,exhaust fumes,and other harmful factors in coal mines increase the risk of cardiovascular disease(CVD)among miners^([1]).Arterial stiffness(AS)is an independent risk factor of CVD,and epidemiological studies have shown that AS plays a vital role in assessing the risk of CVD^([2]).

关键词： Arterial cardiovascular assessing

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