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Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

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CES Transactions on Electrical Machines and Systems 2024年第1期8卷 32-42页

作者： Xiao Liu Pingting Lin Fan Bu Shaoling zhuang Shoudao Huang CES&IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082China CES

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of ***,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search *** the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an ***,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model ***,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a ***,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression *** above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.

关键词： Generator optimization Gaussian Process Regression(GPR) Conditional Likelihood Lower Bound Search(CLLBS) Constraint improvement expectation(CEI) Finite element calculation

Human-like adrenal features in Chinese tree shrews revealed by multi-omics analysis of adrenal cell populations and steroid synthesis

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Zoological Research 2024年第3期45卷 617-632页

作者： jing-Hang Jiang yi-Fu Wang Jie Zheng yi-Ming Lei Zhong-Yuan Chen yi Guo Ya-Jie Guo Bing-Qian Guo Yu-Fang Lv Hong-Hong Wang Juan-Juan Xie yi-Xuan Liu Ting-Wei Jin Bi-Qi Li Xiao-Shu Zhu Yong-Hua Jiang Zeng-Nan Mo Center for Genomic and Personalized Medicine Guangxi Collaborative Innovation Center for Genomic and Personalized MedicineGuangxi Key Laboratory for Genomic and Personalized MedicineGuangxi Medical UniversityNanningGuangxi 530021China Department of Urology First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxi 530021China Reproductive Medicine Center Jingmen People’s HospitalJingChu University of Technology Affiliated Central HospitalJingmenHubei 448000China School of Computer Science and Engineering Yulin Normal UniversityYulinGuangxi 537000China Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry Guangxi Medical UniversityNanningGuangxi 530021China Department of Pathology Second Affiliated Hospital of Guangxi Medical UniversityNanningGuangxi 530007China Department of Obstetrics and Gynecology Second Affiliated Hospital of Guangxi Medical UniversityNanningGuangxi 530007China

The Chinese tree shrew(Tupaia belangeri chinensis)has emerged as a promising model for investigating adrenal steroid synthesis,but it is unclear whether the same cells produce steroid hormones and whether their production is regulated in the same way as in ***,we comprehensively mapped the cell types and pathways of steroid metabolism in the adrenal gland of Chinese tree shrews using single-cell RNA sequencing,spatial transcriptome analysis,mass spectrometry,and *** compared the transcriptomes of various adrenal cell types across tree shrews,humans,macaques,and *** showed that tree shrew adrenal glands expressed many of the same key enzymes for steroid synthesis as humans,including CYP11B2,CYP11B1,CYB5A,and *** analysis confirmed the production of aldosterone,cortisol,and dehydroepiandrosterone but not dehydroepiandrosterone sulfate in the tree shrew adrenal ***,genes in adrenal cell types in tree shrews were correlated with genetic risk factors for polycystic ovary syndrome,primary aldosteronism,hypertension,and related disorders in humans based on genome-wide association ***,this study suggests that the adrenal glands of Chinese tree shrews may consist of closely related cell populations with functional similarity to those of the human adrenal *** comprehensive results(publicly available at http://***:16245/scAGMap/)should facilitate the advancement of this animal model for the investigation of adrenal gland disorders.

关键词： Tree shrew Adrenal gland Dehydroepiandrosterone Genome-wide association studies Disease model

Modification of the CuO electronic structure for enhanced selective electrochemical CO_(2) reduction to ethylene

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Nano Research 2024年第8期17卷 7194-7202页

作者： Xin Wu zhuang Tong Yunliang Liu Yaxi Li Yuanyuan Cheng jingwen Yu Peng Cao Chunqiang zhuang Qiuzhong Shi Naiyun Liu Xiang Liu Hongyu Liang Haitao Li Institute for Energy Research School of Energy and Power EngineeringJiangsu UniversityZhenjiang 212013China Faculty of Materials and Manufacturing Beijing University of TechnologyBeijing 100124China Institute of Medicine&Chemical Engineering Zhenjiang CollegeZhenjiang 212013China Institute of Advanced Manufacturing and Modern Equipment Technology School of Mechanical EngineeringJiangsu UniversityZhenjiang 212013China

Electrochemical carbon dioxide reduction reaction(CO_(2)RR)can produce value-added hydrocarbons from renewable electricity,providing a sustainable and promising approach to meet dual-carbon targets and alleviate the energy ***,it is still challenging to improve the selectivity and stability of the products,especially the C^(2+) *** we propose to modulate the electronic structure of copper oxide(CuO)through lattice strain construction by zinc(Zn)doping to improve the selectivity of the catalyst to *** performance and in situ characterization analyses show that the compressive strain generated within the CuO lattice and the electronic structure modulation by Zn doping enhances the adsorption of the key intermediate*CO,thereby increasing the intrinsic activity of CO_(2)RR and inhibiting the hydrogen precipitation *** the best catalysts had significantly improved ethylene selectivity of 60.5%and partial current density of 500 mA·cm^(–2),and the highest C^(2+) Faraday efficiency of 71.47%.This paper provides a simple idea to study the modulation of CO_(2)RR properties by heteroatom doped and lattice strain.

关键词： CO_(2) reduction heteroatom-doped lattice strain ethylene selectivity

Multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling

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Underground Space 2024年第2期15卷 1-25页

作者： jingxiao Wang Peinan Li Xiaoying zhuang Xiaojun Li Xi Jiang Jun Wu Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai 200092China College of Environmental Science and Engineering Donghua UniversityShanghai 201620China Institute of Photonics Department of Mathematics and PhysicsLeibniz University HannoverHannover 30167Germany Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong 100872China College of Civil Engineering Shanghai Normal UniversityShanghai 200233China

A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel ***,regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics,especially at tunnel portals with complex geological *** paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis,with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel *** progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation *** improve the accuracy of the models,a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field ***,a regional-scale model and an engineering-scale model are built,providing a clear insight into geological phenomena and supporting numerical *** addition,the proposed framework is applied to a case study,the Long-tou mountain tunnel project in Guangzhou,China,where the dominant rock type is *** results show that the data integration and modeling methods effectively improve model structure *** improved model’s calculation deviation is reduced by about 10%to 20%in the mechanical *** study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling.

关键词： Mountain tunnel Geological modeling Multi-source data Progressive prediction Tunnel portals

Photoelectrochemical seawater oxidation with metal oxide materials:Challenges and opportunities

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Journal of Energy Chemistry 2024年第10期97卷 767-782,I0016页

作者： Miao Kan Hangyu Hu Weijie zhuang Meng Tao Shiqun Wu Jinlong Zhang Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering Feringa Nobel Prize Scientist Joint Research CenterSchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237China Shanghai Engineering Research Center for Multimedia Environmental Catalysis and Resource Utilization East China University of Science and TechnologyShanghai 200237 China

Photocatalytic water oxidation is a crucial counter-electrode reaction in the process of photoelectrochemical energy *** its importance,challenges remain in effectively and sustainably converting water to oxygen,particularly with readily available and inexpensive electrolyte solutions such as *** metal oxide materials have demonstrated their advantages in promoting efficiency by reducing overpotential and improving light utilization,stability remains limited by corrosion in multicomponent *** this paper,we reviewed the relationship between four basic concepts including photoelectrochemistry,metal oxide,water oxidation and seawater to better understand the challenges and opportunities in photoelectrochemical(PEC)seawater *** overcome these challenges,the advances in material design,interfacial modification,local environment control and reactor design have been further reviewed to benefit the industrial PEC seawater ***,we demonstrate engineered layered metal oxide electrodes and cell structures that enable powerful and stable seawater *** also outline and advise on the future direction in this area.

关键词： Photoelectrochemistry Anode Water oxidation Metal oxide Seawater Stability

Evaluation and Influencing Factors on Particle Agglomeration in RAP

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Journal of Wuhan University of Technology(Materials Science) 2024年第1期39卷 60-68页

作者：唐伟李宁 zhuang Yuan ZHAN He YU Xin WU Wenxiu DING Gongying College of Civil and Transportation Engineering Hohai UniversityNanjing 210098China Jiangsu Expressway Engineering Maintenance Technology Center Nanjing 211106China Jinhua Highway and Transportation Management Center Jinhua 321000China

Asphalt extraction test and scanning electron microscopy(SEM) were used for analysis of agglomerations of reclaimed asphalt pavement(RAP) particles. In order to quantify the agglomeration degree of RAP, the fineness modulus ratio(FMR) and the percentage loss index(PLI) were proposed. In addition, grey correlation analysis was conducted to discuss the relationship between particle agglomerations and RAP size,asphalt content(AC), and surface area. Two indexes indicate that the agglomeration degree increases in general as the RAP size reduces. This can be attributed to that particles are prone to agglomeration in the case of higher AC. Based on the SEM images and the material composition of RAP, the particle agglomeration in RAP can be classified into weak agglomeration and strong agglomeration. Grey correlation analysis shows that AC is the crucial factor affecting the agglomeration degree and RAP variability. In order to produce consistent and stable reclaimed mixtures, disposal measures of RAP are suggested to lower the AC of RAP.

关键词： RAP particle agglomeration grey correlation analysis asphalt content

A comprehensive survey of federated transfer learning: challenges, methods and applications

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Frontiers of Computer Science 2024年第6期18卷 186356-186356页

作者： GUO Wei zhuang Fuzhen ZHAN Xiao TONG yiqi DONG Jin Institute of Artificial Intelligence Beihang University SKLSDE School of Computer Science Beihang University School of Computer Science and Technology Shandong University Beijing Academy of Blockchain and Edge Computing

Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant. Therefore, many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution. Meanwhile, the differences in their local devices (system heterogeneity), the continuous influx of online data (incremental data), and labeled data scarcity may further influence the performance of these methods. To solve this problem, federated transfer learning (FTL), which integrates transfer learning (TL) into FL, has attracted the attention of numerous researchers. However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL. In this survey, we focus on categorizing and reviewing the current progress on federated transfer learning, and outlining corresponding solutions and applications. Furthermore, the common setting of FTL scenarios, available datasets, and significant related research are summarized in this survey.

关键词： federated transfer learning federated learning transfer learning survey

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

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China Geology 2024年第2期7卷 264-276页

作者： Jun Sun Yu zhuang Ai-guo Xing Guizhou Geology and Mineral Engineering Construction Co. LtdGuiyang 550000China State Key Laboratory of Ocean Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive *** methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical *** study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide ***,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)***,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded *** of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field *** numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

关键词： Landslide runout prediction Drone survey Multi-source data collaboration DAN3D numerical modeling Jianshanying landslide Guizhou Province Geological hazards survey engineering

De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

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Journal of Electronic Science and Technology 2024年第3期22卷 57-68页

作者： Yan Li Tai-Kang Tian Meng-Yu zhuang Yu-Ting Sun School of Economics and Management University of Electronic Science and Technology of ChinaChengdu611731China School of Economics and Management Beijing University of Posts and TelecommunicationBeijing100876China School of Electrical Engineering and Computer Science The University of QueenslandBrisbane4072Australia

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various ***,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still *** address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification *** the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing *** on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student *** approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student *** conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

关键词： De-biasing Deep learning Knowledge distillation Model compression