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A 30-year overview of sodium-ion batteries

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Carbon Energy 2024年第6期6卷 56-98页

作者： yun Gao Hang Zhang Jian Peng lin li Yao Xiao li li Yang liu yun Qiao Shu-Lei Chou School of Environmental and Chemical engineering Shanghai UniversityShanghaiChina College of Chemistry and Materials engineering Wenzhou UniversityWenzhouZhejiangChina Institute for Superconducting and Electronic Materials University of WollongongWollongongNew South WalesAustralia

Sodium-ion batteries(NIBs)have emerged as a promising alternative to commercial lithium-ion batteries(liBs)due to the similar properties of the li and Na elements as well as the abundance and accessibility of Na *** of the current research has been focused on the half-cell system(using Na metal as the counter electrode)to evaluate the performance of the cathode/anode/*** relationship between the performance achieved in half cells and that obtained in full cells,however,has been neglected in much of this ***,the trade-off in the relationship between electrochemical performance and cost needs to be given more ***,systematic and comprehensive insights into the research status and key issues for the full-cell system need to be gained to advance its ***,this review evaluates the recent progress based on various cathodes and highlights the most significant challenges for full *** strategies have also been proposed to enhance the electrochemical performance of NIBs,including designing electrode materials,optimizing electrolytes,sodium compensation,and so ***,perspectives and outlooks are provided to guide future research on sodium-ion full cells.

关键词： commercial application electrode electrolyte sodium-ion full cell strategies

Probabilistic seismic inversion based on physics-guided deep mixture density network

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Petroleum Science 2024年第3期21卷 1611-1631页

作者： Qian-Hao Sun Zhao-yun Zong Xin li National Key Laboratory of Deep Oil and Gas China University of Petroleum(East China)Qingdao266580ShandongChina Laoshan Laboratory Qingdao266580ShandongChina CNOOC Research Institute Ltd. Beijing100028China

Deterministic inversion based on deep learning has been widely utilized in model parameters *** by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model ***,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the *** conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of *** order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency *** addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion *** illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion *** synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed *** addition,the robustness and generality of the proposed method are verified by the field seismic data.

关键词： Deep learning Probabilistic inversion Physics-guided Deep mixture density network

Theoretical study of kinetic isotope effects for vacancy diffusion of impurity in solids

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Acta Geochimica 2024年第5期43卷 959-970页

作者： Yuxi Jing Xuefang li yun liu Research Center for Planetary Science College of Earth and Planetary SciencesChengdu University of TechnologyChengdu 610059China State Key Laboratory of Ore Deposit Geochemistry Institute of GeochemistryChinese Academy of SciencesGuiyang 550081China CAS Center for Excellence in Comparative Planetology Hefei 230026China

Theoretical studies of the diffusionalisotope effect in solids are still stuck in the 1960s and *** the development of high spatial resolution mass spectrometers,isotopic data of mineral grains are rapidly *** dig up information from these data,molecularlevel theoretical models are urgently *** on the microscopic definition of the diffusion coe fficient(D),a new theoretical framework for calculating the diffusional isotope effect(DIE(v))(intermsofD*/D)forvacancy-mediated impurity diffusion in solids is provided based on statistical mechanics *** newly derived equation shows that theDIE(v)can be easily calculated as long as the vibration frequencies of isotope-substituted solids are *** calculatedDIE(v)values of^(199)Au/^(195)Au and^(60)Co/^(57)Co during diffusion in Cu and Au metals are all within 1%of errors compared to the experimental data,which shows that this theoretical model is reasonable and precise.

关键词： Vacancy diffusion Diffusional isotope effect Statistical mechanics

Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution

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Science china(Information Sciences) 2024年第7期67卷 107-132页

作者： Bin CAO Jianwei ZHAO Xin liU yun li State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology School of Artificial Intelligence Hebei University of Technology School of Economics and Management Hebei University of Technology Industrial Artificial Intelligence Centre Shenzhen Institute for Advanced StudyUniversity of Electronic Science and Technology of China

Mobile telemedicine systems based on the next-generation communication will significantly enhance deep fusion of network automation and federated learning(FL), but data privacy is a paramount issue in sectors like healthcare. This work hence considers FL augments 5G-and-beyond networks by training deep learning(DL) models without the need to exchange raw data. The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively. To address the opaque nature of DL models and to improve the interpretability of FL models, we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing. We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures. Our comprehensive FL framework integrates smart scheduling, interpretable fuzzy rough logic,and neuroevolution. This framework is shown to improve communication efficiency, increase interpretability of diagnosis with protected privacy, and generate low-complexity neural architectures.

关键词： 5G-and-beyond network interpretable federated learning mobile telemedicine system fuzzy rough set theory neuroevolution

Torque limit-based Inertial Control Method Based on Delayed Support for Primary Frequency Control of Wind Turbines

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 561-570页

作者： Wei Gu Zaiyu Chen Qun li Minghui Yin Qiang li yun Zou School of Automation Nanjing University of Science and TechnologyNanjing 210094China State Grid Jiangsu Electric Power Co. Ltd.Research InstituteNanjing 211103China

To avoid the secondary frequency dip caused by the steep drop of the electrical power of wind turbines(WTs)at the end of frequency support stage,the torque limit-based iner-tial control(TliC)method sets the power reference as a linear function of rotor speed,rather than the step form for the step-wise inertial ***,the compensation effect on the frequency nadir(FN)caused by the load surge is weakened as the TliC power is no longer in the step ***,the maximum point of the frequency response component(FRC)contributed by TliC occurs earlier than the minimum point of FRC corresponding to the load surge,so that the FN cannot be adequately ***,this paper first investigates the relation between the peak and nadir times of FRCs stimulated by the TliC and load *** this basis,with the compensation principle of matching the peak and nadir times of FRCs,the improved TliC method based on delayed support is ***,the effectiveness of the proposed method is validated via the experiments on the test bench of wind-integrated power system.

关键词： Wind turbine frequency response component torque limit inertial control delayed support

A Novel CCA-NMF Whitening Method for Practical Machine Learning Based Underwater Direction of Arrival Estimation

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Journal of Beijing Institute of Technology 2024年第2期33卷 163-174页

作者： yun Wu Xinting li Zhimin Cao School of Physics and Electronic engineering Northeast Petroleum UniversityDaqing 163318China

Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical *** to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation *** order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian ***,the obtained information about non-Gaussian components can be used to whiten the array *** on these considerations,a novel practical sonar array whitening method was ***,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array *** the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation *** results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.

关键词： direction of arrival(DOA) sonar array data underwater disturbance machine learn-ing canonical correlation analysis(CCA) non-negative matrix factorization(NMF)

The impact of broad glutamine metabolism inhibition on the tumor microenvironment

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Genes & Diseases 2024年第3期11卷 2-4页

作者： Tianhe li yun-Ta Chuang Tian-li Wang Departments of Pathology Oncologyand Gynecology and ObstetricsThe Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMD 21231USA

Glutamine is an importantα-amino acid that provides carbon and nitrogen sources for the biosynthesis of nucleotides,proteins,and *** also generates metabolites that fuel the tricarboxylic acid cycle and maintain intracellular redox *** cells have a heightened metabolic dependence on glutamine to support incessant tumor growth and mitigate tumor microenvironmental stresses including hypoxia,nutrient depletion.

关键词： metabolism tumor microenvironment

Representation learning: serial-autoencoder for personalized recommendation

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Frontiers of Computer Science 2024年第4期18卷 61-72页

作者： Yi ZHU Yishuai GENG yun li Jipeng QIANG Xindong WU School of Information engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge engineering with Big Data(Ministry of Education of the People’s Republic of china) Hefei University of TechnologyHefei 230009China School of Computer Science and Information engineering Hefei University of TechnologyHefei 230009China

Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

关键词： personalized recommendation autoencoder representation learning collaborative filtering

A Novel Divide and Conquer Solution for Long-term Video Salient Object Detection

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Machine Intelligence Research 2024年第4期21卷 684-703页

作者： yun-Xiao li Cheng-li-Zhao Chen Shuai li Ai-Min Hao Hong Qin State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Department of Computer Science Stony Brook UniversityNew York 11794USA

Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from the given *** such a learning scheme is generally effective,it has a critical limitation,i.e.,the model learned on sparse frames only possesses weak generalization *** situation could become worse on“long”videos since they tend to have intensive scene ***,in such videos,the keyframe information from a longer time span is less relevant to the previous,which could also cause learning conflict and deteriorate the model ***,the learning scheme is usually incapable of handling complex pattern *** solve this problem,we propose a divide-and-conquer framework,which can convert a complex problem domain into multiple simple ***,we devise a novel background consistency analysis(BCA)which effectively divides the mined frames into disjoint *** for each group,we assign an individual deep model on it to capture its key attribute during the fine-tuning *** the testing phase,we design a model-matching strategy,which could dynamically select the best-matched model from those fine-tuned ones to handle the given testing *** experiments show that our method can adapt severe background appearance variation coupling with object movement and obtain robust saliency detection compared with the previous scheme and the state-of-the-art methods.

关键词： Video salient object detection background consistency analysis weakly supervised learning long-term information background shift.