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An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanismdata fusion

Chinese Journal of Aeronautics 2024年第8期37卷 190-207页

作者： chen WANG Ziyang yu Xian DU Ximing SUN School of Control Science and Engineering Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of EducationDalian University of TechnologyDalian 116024China

Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion *** component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic *** paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental ***,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is *** study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational *** model’s output closely aligns with typical experimental *** combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight ***,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location *** approach significantly curbs performance deviations in supersonic *** example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%*** findings underscore the clear superiority of the proposed method.

关键词： Aero-propulsion system Integrated inlet-enginenozzle Component-level model On-board adaptive model Mechanism-data fusion Extreme learning machine

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Estimation of surface geometry on combustion characteristics of AP/HTPB propellant under rapid depressurization

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Defence Technology（防务技术） 2024年第3期33卷 546-558页

作者： Kaixuan chen Zhenwei Ye Xiaochun Xue Yonggang yu School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing 210094JiangsuChina School of Science Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina

The 2D sandwich model serves as a potent tool in exploring the influence of surface geometry on the combustion attributes of Ammonium perchlorate/Hydroxyl-terminated polybutadiene(AP/HTPB)propellant under rapid pressure *** thickness of the sandwich propellant is derived from slicing the 3D random particle packing,an approach that enables a more effective examination of the micro-flame *** analysis of the predicted burning characteristics has been performed with experimental *** findings demonstrate a reasonable agreement,thereby validating the precision and soundness of the *** on the typical rapid depressurization environment of solid rocket motor(initial combustion pressure is 3 MPa and the maximum depressurization rate is 1000 MPa/s).A-type(a flatter surface),B-type(AP recesses from the combustion surface),and C-type(AP protrudes from the combustion surface)propellant combustion processes are numerically *** comparison of the evolution of gas-phase flame between 0.1 and 1 ms,it is discerned that the flame strength and form created by the three sandwich models differ significantly at the beginning stage of depressurization,with the flame structures gradually becoming harmonized over *** are drawn by comparison extinction times:the surface geometry plays a pivotal role in the combustion process,with AP protrusion favoring combustion the most.

关键词： AP/HTPB propellant BDP model Rapid pressure decay Burning surface geometry

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Highly selective electrosynthesis of 3,4-dihydroisoquinoline accompanied with hydrogen production over three-dimensional hollow CoNi-based microarray electrocatalysts

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Nano Research 2024年第4期17卷 2509-2519页

作者： yu Xin Liyu chen Yingwei Li Kui Shen Guangdong Provincial Key Lab for Green Chemical Product Technology School of Chemistry and Chemical EngineeringSouth China University of TechnologyGuangzhou 510640China State Key Laboratory of Pulp and Paper Engineering School of Chemistry and Chemical EngineeringSouth China University of TechnologyGuangzhou 510640China

Electrocatalytic oxidation reaction of biomass-based derivatives is an excellent candidate to replace water oxidation for obtaining both value-added products and hydrogen(H_(2)),but the exploration of competent electrocatalysts is still highly ***,two new types of three-dimensional self-supported hollow microarrays containing CoNi layered double hydroxide(CoNi-LDH)and N-doped carbon nanosheets decorated with CoNi alloyed nanoparticles(CoNi-NC)on carbon cloth(CC)are prepared,which are further used as efficient electrocatalysts for tetrahydroisoquinoline(THIQ)electrooxidation and hydrogen evolution reaction(HER),*** demonstrate that the Co-modulated electronic environment for Ni(II)/Ni(Ⅲ)redox-looping in CoNi-LDH is the main factor to boost the selectivity of 3,4-dihydroisoquinoline(DHIQ)for the indirect electrooxidation process of *** functional theory(DFT)calculations reveal that the Ni(Ⅲ)/Co(Ⅲ)dual sites of CoNi-LDH exhibit enhanced adsorption for THIQ but poorer adsorption for DHIQ compared to pure Co(Ⅲ)or Ni(Ⅲ).Therefore,the Ni(Ⅲ)/Co(Ⅲ)dual sites can effectively inhibit the peroxidation of DHIQ to isoquinoline(IQ)over CoNi-LDH,thus improving the selectivity of DHIQ to nearly 100%,much higher than that of its pure Ni ***,CC@CoNi-NC can deliver high HER activity with low overpotential(40 mV@10 mA·cm^(-2))and high exchange current density(3.08 mA·cm^(-2)).Impressively,the assembled flow-cell device with CC@CoNi-LDH anode and CC@CoNi-NC cathode only requires low cell voltage and electricity consumption of 1.6 V and 3.50 kWh per cubic meter of H_(2)(@25 mA·cm^(-2)).

关键词： highly selective electrosynthesis dihydroisoquinolines hydrogen evolution reaction metal-organic frameworks hollow microarrays

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pH-controlled reversible sol-gel inversion by cerous phosphate nanofibers for hemostasis

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Rare Metals 2024年第10期43卷 5141-5151页

作者： Tuo Su Jun-chen Xu Wei yu Dan Su di-Er Shi Yi-Chao Pang Yao Ying Wang-Chang Li Juan Li Jing-Wu Zheng Liang Qiao Sheng-Lei Che Jing yu Research Center of Magnetic and Electronic Materials College of Materials Science and EngineeringZhejiang University of Technology State Key Laboratory for diagnosis and Treatment of Infectious diseases Collaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Affiliated HospitalCollege of MedicineZhejiang University Key Laboratory of Drug Safety Evaluation and Research of Zhejiang Province Department of Clinical MedicineHangzhou Medical College Department of Chemistry Zhejiang University Clinical Medical College Yangzhou University

Developing biomimetic soft materials that display stimuli responsiveness using solely inorganic materials has been regarded as a challenge owing to that such materials?properties typically vary from those of living ***,biomimetic soft materials have been developed using organic materials or inorganic materials modified with organic small *** this study,we prepared cerium phosphate nanofibers （CePO4NFs） by using inorganic agents only without further *** CePO4NFs demonstrate sol-ge switching properties in response to pH value,allowing them to form a gel under high hydroxide ion （OH-） concentrations and turn back into sol under low *** formation of gel could be ascribed to the physical cross-linking of the nanofibers induced by the attractive electrostatic force of OH-and surface Ce3+.As a result,CePO4NFs are able to form a gel when in contac with blood of high pH value for *** specific clotting mechanism makes them better candidates for hemostasis of heavy bleeding.

关键词： Cerium phosphate nanofibers Hemostasis pH-responsive Reversible Sol-gel switching

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MarkINeRV: A Robust Watermarking Scheme for Neural Representation for Videos Based on Invertible Neural Networks

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Computers, Materials & Continua 2024年第9期80卷 4031-4046页

作者： Wenquan Sun Jia Liu Lifeng chen Weina Dong Fuqiang di Department of Cryptographic Engineering Engineering University of PAPXi’an710086China Department of Cryptographic Engineering Key Laboratory for Cryptology and Information SecurityXi’an710086China

Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos(NeRV).While explicit methods exist for accurately embedding ownership or copyright information in video data,the nascent NeRV framework has yet to address this issue *** response,this paper introduces MarkINeRV,a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV,which models the embedding and extraction of watermarks as a pair of inverse processes of a reversible network and employs the same network to achieve embedding and extraction of *** is just that the information flow is in the opposite ***,a video frame quality enhancement module is incorporated to mitigate watermarking information losses in the rendering process and the possibility ofmalicious attacks during transmission,ensuring the accurate extraction of watermarking information through the invertible network’s inverse *** paper evaluates the accuracy,robustness,and invisibility of MarkINeRV through multiple video *** results demonstrate its efficacy in extracting watermarking information for copyright protection of *** represents a pioneering investigation into copyright issues surrounding NeRV.

关键词： Invertible neural network neural representations for videos watermarking robustness

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A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1119-1130页

作者： Haoran Zhang Weihao Hu di Cao Qi Huang Zhe chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduChina Department of Energy Technology Aalborg UniversityAalborgDenmark IEEE

Electricity prices have complex features,such as high frequency,multiple seasonality,and *** factors will make the prediction of electricity prices ***,accurate electricity price prediction is important for energy producers and consumers to develop bidding *** improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency *** frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN *** results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

关键词： Autoregressive integrated moving average model electricity price forecasting empirical mode decomposition temporal convolutional network

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Modeling Method of Multi-Energy Systems Based on LSTM Algorithm

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1701-1709页

作者： di Qiu Fei chen Dong Liu Min Cao Siyang Liu Shanghai Jiao Tong University Shanghai 200240China yunnan Power Grid Co. Ltd

The development of the Energy Internet has improved the efficiency of energy utilization and promoted sustainable development of power and energy *** multi-energy system modeling considering the dynamic process of transmission line is one of the key research points of Energy Internet operation *** the energy circuit theory,the lumped parameter model of natural gas pipelines is built and the dynamic characteristic parameters under the control instruction are *** with dynamic characteristic parameters,the long short-term memory(LSTM)neural network is designed to fit the natural gas pipeline dynamic process into discrete linear time-varying(LTV)*** with the equations,an energy hub method is used to build a control model of industrial parks with multi-energy distribution *** the rolling optimal control strategy given in this paper,the model is solved by the Matlab-Yalmip solver and rolling control instructions of each energy conversion unit are ***,the case study demonstrates that the LSTM neural network-based modeling method presented in this paper can accurately fit the dynamic process of a natural gas pipeline *** rolling control model of the multi-energy system can improve the efficiency of energy utilization,exhibit the transmission line status constraints during the optimization control process and improve reliability of the multi-energy system operation.

关键词： Energy hub LSTM multi-energy supply system pipeline system modeling

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RWNeRF:Robust Watermarking Scheme for Neural Radiance Fields Based on Invertible Neural Networks

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Computers, Materials & Continua 2024年第9期80卷 4065-4083页

作者： Wenquan Sun Jia Liu Weina Dong Lifeng chen Fuqiang di Department of Cryptographic Engineering Engineering University of PAPXi’an710086China Department of Cryptographic Engineering Key Laboratory of PAP for Cryptology and Information SecurityXi’an710086China

As neural radiance fields continue to advance in 3D content representation,the copyright issues surrounding 3D models oriented towards implicit representation become increasingly *** response to this challenge,this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network *** 2D image watermarking technology for 3D scene protection,the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from images rendered by neural radiance fields through the reverse process,thereby ensuring copyright protection for both the neural radiance fields and associated 3D ***,challenges such as information loss during rendering processes and deliberate tampering necessitate the design of an image quality enhancement module to increase the scheme’s *** module restores distorted images through neural network processing before watermark ***,embedding watermarks in each training image enables watermark information extraction from multiple *** proposed watermarking method achieves a PSNR(Peak Signal-to-Noise Ratio)value exceeding 37 dB for images containing watermarks and 22 dB for recovered watermarked images,as evaluated on the Lego,Hotdog,and Chair datasets,*** results demonstrate the efficacy of our scheme in enhancing copyright protection.

关键词： Neural radiance fields 3D scene robust watermarking invertible neural networks

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Short-term Residential Load Forecasting Based on K-shape Clustering and Domain Adversarial Transfer Network

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1239-1249页

作者： Jizhong Zhu yuwang Miao Hanjiang Dong Shenglin Li Ziyu chen di Zhang School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

In recent years,the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution ***,due to the scarcity of historical data for these new consumers,it has become a com-plex challenge to accurately forecast their electricity demands through traditional forecasting *** paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering,deep learning,and transfer learning technologies to address this *** begin,this paper leverages the domain adversarial transfer *** employs limited data as target domain data and more abundant data as source domain data,thus enabling the utilization of source do-main insights for the forecasting task of the target ***,a K-shape clustering method is proposed,which effec-tively identifies source domain data that align optimally with the target domain,and enhances the forecasting ***-sequently,a composite architecture is devised,amalgamating at-tention mechanism,long short-term memory network,and seq2seq *** composite structure is integrated into the domain adversarial transfer network,bolstering the perfor-mance of feature extractor and refining the forecasting *** illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method *** the case study,the relative mean square error of the proposed method is within 30 MW,and the mean absolute percentage error is within 2%.A signifi-cant improvement in accuracy,compared with other compara-tive experimental results,underscores the reliability of the pro-posed *** findings unequivocally demonstrate that the proposed method advocated in this paper yields superior fore-casting results compared with prevailing mainstream forecast-ing methods.

关键词： Load forecasting domain adversarial K-shape clustering long short-term memory network seq2seq network attention mechanism

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Probing the electric double layer structure at nitrogen-doped graphite electrodes by constant-potential molecular dynamics simulations

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Journal of Energy Chemistry 2024年第6期93卷 299-305,I0008页

作者： Legeng yu Nan Yao yu-chen Gao Zhong-Heng Fu Bo Jiang Ruiping Li cheng Tang Xiang chen Tsinghua Center for Green Chemical Engineering Electrification(CCEE) Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China Ordos Carbon Neutral Research and Application Co. Ltd.Ordos 017010Inner Mongoli1China Institute for Carbon Neutrality Tsinghua UniversityBeijing 100084China Center for Next-Generation Energy Materials and School of Chemical Engineering Sungkyunkwan University2066 Seobu-roJangan-guSuwon-siGyeonggi-do 16419Republic of Korea

Electric double layer(EDL)is a critical topic in electrochemistry and largely determines the working performance of lithium ***,atomic insights into the EDL structures on heteroatom-modified graphite anodes and EDL evolution with electrode potential are very ***,a constant-potential molecular dynamics(CPMD)method is proposed to probe the EDL structure under working conditions,taking N-doped graphite electrodes and carbonate electrolytes as an *** interface model was developed,incorporating the electrode potential and atom *** a result,an insightful atomic scenario for the EDL structure under varied electrode potentials has been established,which unveils the important role of doping sites in regulating both the EDL structures and the following electrochemical reactions at the atomic ***,the negatively charged N atoms repel the anions and adsorb Li~+at high and low potentials,*** preferential adsorption suggests that Ndoped graphite can promote Li~+desolvation and regulate the location of Li~+*** CPMD method not only unveils the mysterious function of N-doping from the viewpoint of EDL at the atomic level but also applies to probe the interfacial structure on other complicated electrodes.

关键词： Lithium batteries Graphite N-doping Electric double layer Molecular dynamics Constant potential method Electrode potential

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