检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Ma, Yu Zhao, Xiaoli Shen, Yanjun Wang, Yi He, Bin Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management Guangdong Institute of Eco-environmental Science & Technology Guangzhou510650 China National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China Guangzhou510650 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China CAS Key Laboratory of Agricultural Water Resources Hebei Key Laboratory of Water Saving Agriculture Center for Agricultural Resources Research IGDB Chinese Academy of Sciences Shijiazhuang050021 China School of Advanced Agricultural Sciences University of The Chinese Academy of Sciences Beijing100049 China School of Water Resources and Hydropower Engineering North China Electric Power University 2 Beinong Road Changping Beijing102206 China

Photothermal materials play vital roles in solar-steam generation. However, a large number of photothermal nanoparticle (NPs) materials with high performances are not suitable for solar-steam generation owing to common defects such as light reflection and leakage. Therefore, it is necessary to rationally assemble photothermal NPs into a membrane with a hierarchical structure to localize heat at the liquid surface and trap light. Herein, we report a simple and effective method of dissolving a phase-inversion-formed nanohydrogel inside an originally compacted photothermal nanoparticle membrane to enable multiple reflections and light trapping, referred to as sacrificial nanoparticle template with phase inversion (SNTPI). The reflectance of the fabricated light-trapping membrane was 4.88 times lower than that of the compacted membrane, resulting in an average absorptance of 97.75%. Based on this high absorptance, the membrane showed good performances in solar desalination and indoor dehumidification. Because the nanohydrogel fabrication process only requires the addition of the diluted polymer solution to its nonsolvent, and the amorphous polymer nanohydrogel can be readily dissolved in its solvent, we believe that the proposed SNTPI method will facilitate the sustainable development of solar-steam devices. © 2022, The Authors. All rights reserved.

关键词： Nanoparticles

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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EVA-planner: Environmental adaptive quadrotor planning

arXiv

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arXiv 2020年

作者： Quan, Lun Zhang, Zhiwei Zhong, Xingguang Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China National Engineering Research Center for Industrial Automation Ningbo Institute Ningbo315000 China

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages. Copyright © 2020, The Authors. All rights reserved.

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Automatic Deep Extraction of Robust Dynamic Features for industrial Big Data Modeling and Soft Sensor application

Automatic Deep Extraction of Robust Dynamic Features for Ind...

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第30届中国过程控制会议（CPCC 2019）

作者： Xinyu Zhang Zhiqiang Ge Jiaxin Yu Zhihuan Song State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University

Dynamic is one of the main bottlenecks in the industrial soft sensor application, due to the difficulty in representing and extracting dynamic data ***, end-to-end deep network owns the ability to characterize sequence data information, but its fitting ability requires improvements in practical *** this paper, an ensemble tree based soft sensing model is proposed, with a new developed automatic dynamic feature extractor which is transportable and ***, the dynamic feature extractor with a reconstruction function is designed, through which the dynamic information of the process data can be effectively captured and ***, in order to remove heavy noises in the extracted dynamic vector, the regression network is trained with a consideration of extracting attention ***, an attenuation factor is defined based on the attention weights for the extracted dynamic ***, the robust dynamic vector and the original data features are concatenated and utilized for training the ensemble tree regression *** results on the debutanizer distillation process show that the incorporation of robust dynamic features can significantly improve the soft sensing performance, compared to traditional ***, the proposed model is further implemented through a cloud computing platform for industrial big data analytics.

关键词： Soft Sensor Dynamic Vector Features Extractor Attention Denoising Ensemble Trees

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Functional production of human antibody by the filamentous fungus Aspergillus oryzae

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Fungal Biology and Biotechnology 2020年第1期7卷 7页

作者： Huynh, Hung Hiep Morita, Naoki Sakamoto, Toshihiro Katayama, Takuya Miyakawa, Takuya Tanokura, Masaru Chiba, Yasunori Shinkura, Reiko Maruyama, Jun-Ichi Department of Biotechnology University of Tokyo Tokyo Japan Laboratory of Immunology and Infection Control Institute for Quantitative Biosciences University of Tokyo Tokyo Japan Collaborative Research Institute for Innovative Microbiology University of Tokyo Tokyo Japan Department of Applied Biological Chemistry University of Tokyo Tokyo Japan Biotechnology Research Institute for Drug Discovery National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki Japan Core Research for Evolutional Science and Technology Japan Agency for Medical Research and Development Tokyo Japan

Background: Monoclonal antibodies (mAbs) as biopharmaceuticals take a pivotal role in the current therapeutic applications. Generally mammalian cell lines, such as those derived from Chinese hamster ovaries (CHO), are used to produce the recombinant antibody. However, there are still concerns about the high cost and the risk of pathogenic contamination when using mammalian cells. Aspergillus oryzae, a filamentous fungus recognized as a GRAS (Generally Regarded As Safe) organism, has an ability to secrete a large amount of proteins into the culture supernatant, and thus the fungus has been used as one of the cost-effective microbial hosts for heterologous protein production. Pursuing this strategy the human anti-TNFα antibody adalimumab, one of the world's best-selling antibodies for the treatment of immune-mediated inflammatory diseases including rheumatoid arthritis, was chosen to produce the full length of mAbs by A. oryzae. Generally, N-glycosylation of the antibody affects immune effector functions such as antibody-dependent cell-mediated cytotoxicity (ADCC) via binding to the Fc receptor (FcγR) on immune cells. The CRISPR/Cas9 system was used to first delete the Aooch1 gene encoding a key enzyme for the hyper-mannosylation process in fungi to investigate the binding ability of antibody with FcγRIIIa. Results: Adalimumab was expressed in A. oryzae by the fusion protein system with α-amylase AmyB. The full-length adalimumab consisting of two heavy and two light chains was successfully produced in the culture supernatants. Among the producing strains, the highest amount of antibody was obtained from the ten-protease deletion strain (39.7 mg/L). Two-step purifications by Protein A and size-exclusion chromatography were applied to obtain the high purity sample for further analysis. The antigen-binding and TNFα neutralizing activities of the adalimumab produced by A. oryzae were comparable with those of a commercial product Humira^®. No apparent binding wit

关键词： Antibody Aspergillus oryzae Fc receptor N-linked glycan Neutralization

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Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network

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Sensors (Basel, Switzerland) 2025年第10期25卷 3224页

作者： Chen Zhou Fan Zhang Edric John Cruz Nacpil Zheng Wang Fei-Xiang Xu Information and Control Engineering China University of Mining and Technology Xuzhou 221000 China. Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery Huaqiao University Xiamen 361021 China. Advanced Institute of Nano Technology Sungkyunkwan University Suwon 16228 Republic of Korea. Institute of Industrial Science The University of Tokyo Tokyo 113-8654 Japan.

In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intention with a CNN-GRU hybrid machine learning model. The convolutional neural network (CNN) layer extracts features from the stochastic driver behavior, which is input to the gated-recurrent-unit (GRU) layer. And the driver's steering intention is forecasted based on the GRU model. Our study was conducted using a driving simulator to observe the lateral control behaviors of 18 participants in four different driving circumstances. Finally, the efficiency of the suggested prediction approach was evaluated employing long-short-term-memory, GRU, CNN, Transformer, and back propagation networks. Experimental results demonstrated that the proposed CNN-GRU model performs significantly better than baseline models. Compared with the GRU network, the CNN-GRU network reduced the RMSE, MAE, and MAPE of the driver's input torque by 33.22%, 32.33%, and 35.86%, respectively. The proposed prediction method also possesses adaptability to different driver behaviors.

关键词： CNN-GRU network driver intention prediction shared steering control

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Regulatory role of L-proline in fetal pig growth and intestinal epithelial cell proliferation

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Animal Nutrition 2020年第4期6卷 438-446页

作者： Jing Wang Bi'e Tan Jianjun Li Xiangfeng Kong Minjie Tan Guoyao Wu Hunan International Joint Laboratory of Animal Intestinal Ecology and Health Laboratory of Animal Nutrition and Human HealthCollege of Life SciencesHunan Normal UniversityChangsha 410081China College of Animal Science and Technology Hunan Agricultural UniversityChangsha 410128China Laboratory of Animal Nutritional Physiology and Metabolic Process Key Laboratory of Agro-ecological Processes in Subtropical RegionNational Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry ProductionInstitute of Subtropical AgricultureChinese Academy of SciencesChangsha 410125China Department of Animal Science Texas A&M UniversityCollege StationTX 77843USA

L-proline(Pro)is a precursor of ornithine,which is converted into polyamines via ornithine decarboxylase(ODC).Polyamines plays a key role in the proliferation of intestinal epithelial *** study investigated the effect of Pro on polyamine metabolism and cell proliferation on porcine enterocytes in vivo and in ***-four Huanjiang mini-pigs were randomly assigned into 1 of 3 groups and fed a basal diet that contained 0.77%alanine(Ala,iso-nitrogenous control),1%Pro or 1%Pro+0.0167%α-difluoromethylornithine(DFMO)from d 15 to 70 of *** fetal body weight and number of fetuses per litter were determined,and the small and large intestines were obtained on d 70±1.78 of *** in vitro study was performed in intestinal porcine epithelial(IPEC-J2)cells cultured in Dulbecco’s modified Eagle medium-high glucose(DMEM-H)containing 0μmol/L Pro,400μmol/L Pro,or400μmol/L Pro+10 mmol/L DFMO for 4 *** results showed that maternal dietary supplementation with 1%Pro increased fetal weight;the protein and DNA concentrations of the fetal small intestine;and mRNA levels for potassium voltage-gated channel,shaker-related subfamily,member 1(Kv1.1)in the fetal small and large intestines(P<0.05).Supplementing Pro to either gilts or IPEC-J2 cells increased ODC protein abundances and polyamine concentrations in the fetal intestines and IPEC-J2 cells(P<0.05).In comparison with the Pro group,the combined administration of Pro and DFMO reduced the expression of ODC protein and spermine concentration in the fetal intestine,as well as the concentrations of putrescine,spermidine and spermine in IPEC-J2 cells(P<0.05).Meanwhile,the percentage of cells in the S-phase and the mRNA levels of proto-oncogenes c-fos and c-myc were increased in response to Pro supplementation,whereas depletion of cellular polyamines with DFMO increased tumor protein p53(p53)mRNA levels(P<0.05).Taken together,dietary supplementation with Pro improved fetal pig growth and intestinal epithelial cell

关键词： L-proline Polyamine metabolism Ornithine decarboxylase a-difluoromethylornithine Intestine Pig

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Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

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Journal of Systems Engineering and Electronics 2018年第1期29卷 166-175页

作者： LIU Lu SHAN Liang DAI Yuewei LIU Chenglin QI Zhidong School of Automation Nanjing University of Science and Technology Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population *** tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

关键词： bacterial foraging algorithm fractional-order quantum rotation gate proportion integration differentiation(PID) servo system

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Pectinase hydrolysis of guava pulp: effect on the physicochemical characteristics of its juice

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Heliyon 2021年第10期7卷 e08141页

作者： Ninga, Kombele Aime Desobgo, Zangue Steve Carly De, Sirshendu Nso, Emmanuel Jong Department of Process Engineering National School of Agro-Industrial Sciences (ENSAI) University of Ngaoundere P.O. Box 455 Adamaoua Cameroon Department of Food Process & Quality Control Bioprocess Laboratory University Institute of Technology (IUT) University of Ngaoundere P.O. Box 455 Adamaoua Cameroon Department of Chemical Engineering Indian Institute of Technology Kharagpur 721 302 India

The objective of this research is to assess the effect of enzymatic treatment of guava puree on the physicochemical parameters of the juice. Pectinases from Aspergillus niger were applied to the puree at 43 ± 3 °C under constant stirring. Enzyme concentrations used were: 0.033 % (w/w), 0.055% (w/w), 0.078 % (w/w) and 0.1 % (w/w). For each enzyme concentration, the treatment times were varied from 3 – 90 min. Physicochemical parameters of raw puree and enzymatically treated juice were determined. These were: viscosity, pH, electric conductivity, protein and polyphenol content, galacturonic acid content, color, TSS, and antioxidant capacity. Particle distribution, homogeneity of raw puree and juice samples dried extracts were assessed using a Field Emission Scanning Electron Microscopy (FESEM). A 91% viscosity decrease was recorded for each enzyme concentration after 3 min of enzyme reaction. That drecrase was accompanied by an increase in galacturonic acid content with increasing depectinization factors. Enzyme treatment of guava puree led to a decrease in pH, protein and polyphenol contents and an increase in conductivity and color. Analysis of FESEM images of guava samples bestowed a decrease in particle size, a scattering of particles in the medium, an increase in continuous phase proportion and an improvement of sample homogeneity with increasing values of processing parameters, due to the breaking-down of bigger particles and the solubilization during depectinization © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

关键词： FESEM Guava pulp Hydrolysis Juice Pectinases

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Network Security Situation Awareness technology Based on Multi-source Heterogeneous Data 2020

Network Security Situation Awareness Technology Based on Mul...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of advanced Technologies

作者： Daojuan Zhang Kexiang Qian Wenhui Wang Fuyang fang Chonghua Wang Xi Luo State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute co. Ltd Beijing China Information Science Academy China Electronics Technology Group Corporation Beijing China Department of Security Technology China Industrial Control Systems Cyber Emergency Response Team Beijing China Cyber space Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9781450387828

With the development of information technology, the scale of the network continues to expand, and the security issues continue to increase. The complexity of the network security situation is increasing and the importance of the network security situational awareness continues to increase. The data sources are wide, the type and the number are large in a large-scale network environment currently. This paper comprehensively studies the network security situational awareness technology based on multi-source heterogeneous data. In addition, this paper introduces the origin, concept and the model of the network situational awareness, as well as the characteristics of network security situational awareness based on multi-source heterogeneous data fusion. Finally, the key technologies in the security situational awareness such as the extraction of situational elements, multi-source data fusion, situation assessment, situation prediction and visual display are introduced.

关键词： Data Fusion

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