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7th IEEE International Conference on High Voltage Engineering and Application, ICHVE 2020

作者： Li, Zhiyu Jiang, Xingliang State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing University Chongqing China

ISBN: (纸本)9781728155111

In high-humidity and low-temperature areas, wind turbines are exposed to the danger of icing in winter. The aerodynamic performance of icing wind turbine blades is drastically reduced, which seriously reduces the power output of wind turbines, even resulting in downtime. In order to study the blade icing and more effective de-icing and anti-icing work, using a blade section of 300kW wind turbine blade as research object, a rime ice accretion model of wind turbine blade was established. The influence of each factor on the rime ice accretion on wind turbine blade was studied and summarized. The results show that as the medium volume diameter, liquid water content, and wind velocity increase, the ice thickness increases significantly;but in terms of ice coverage, the ice coverage on the windward and leeward sides increases as the medium volume diameter increases within a certain range, the ice coverage on the windward side increases and the ice coverage on the leeward side decreases as wind velocity increases within a certain range;and the change of liquid water content has little effect on the ice coverage. © 2020 IEEE.

关键词： Ice

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Analysis of DC Microgrid Stability impedance method considering the state of Charge of Electric Vehicle Batteries

Analysis of DC Microgrid Stability impedance method consider...

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IEEE International Conference on Smart Grid and Smart Cities (ICSGSC)

作者： Xinglong Li Guangru Zhang Jiawu Zhang Zhenqi Ma Gang Lu Yongqiang Kang School of New Energy and Power Engineering Lanzhou Jiaotong University Lanzhou China State Grid Gansu Electric Power Research institute Lanzhou China Key Laboratory of Large Electric Drive System and Equipment Technology Tianshui China

With the development of electric vehicles, they are increasingly valued. The large number of electric vehicles connected to the DC microgrid for charging and discharging will impact the DC microgrid’s stability. This article first establishes an equivalent impedance model for the DC bus voltage control unit and a load unit equivalent impedance model considering the SOC (state of Charge) of electric vehicle power batteries; Subsequently, the equivalent impedance of the DC microgrid system is calculated based on its structure. Finally, the stability of the DC microgrid system is determined by using the Bode diagram and Nyquist diagram of the system impedance stability judgment conditions. Through numerical examples, it was found that the DC microgrid gradually loses stability with the increase of SOC in electric vehicle power batteries.

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ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation

arXiv

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arXiv 2024年

作者： Ma, Zhiyuan Wei, Yuxiang Zhang, Yabin Zhu, Xiangyu Lei, Zhen Zhang, Lei The Hong Kong Polytechnic University PolyU Hong Kong Center for Artificial Intelligence and Robotics HKISI CAS Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA China School of Artificial Intelligence University of Chinese Academy of Sciences UCAS China Harbin Institute of Technology HIT China

By leveraging the text-to-image diffusion priors, score distillation can synthesize 3D contents without paired text-3D training data. Instead of spending hours of online optimization per text prompt, recent studies have been focused on learning a text-to-3D generative network for amortizing multiple text-3D relations, which can synthesize 3D contents in seconds. However, existing score distillation methods are hard to scale up to a large amount of text prompts due to the difficulties in aligning pretrained diffusion prior with the distribution of rendered images from various text prompts. Current state-of-the-arts such as Variational Score Distillation finetune the pretrained diffusion model to minimize the noise prediction error so as to align the distributions, which are however unstable to train and will impair the model’s comprehension capability to numerous text prompts. Based on the observation that the diffusion models tend to have lower noise prediction errors at earlier timesteps, we propose Asynchronous Score Distillation (ASD), which minimizes the noise prediction error by shifting the diffusion timestep to earlier ones. ASD is stable to train and can scale up to 100k prompts. It reduces the noise prediction error without changing the weights of pre-trained diffusion model, thus keeping its strong comprehension capability to prompts. We conduct extensive experiments across different 2D diffusion models, including Stable Diffusion and MVDream, and text-to-3D generators, including Hyper-iNGP, 3DConv-Net and Triplane-Transformer. The results demonstrate ASD’s effectiveness in stable 3D generator training, high-quality 3D content synthesis, and its superior prompt-consistency, especially under large prompt corpus. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

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Advances in Atmospheric Sciences 2024年第7期41卷 1299-1312页

作者： Ya WANG Gang HUANG Baoxiang PAN Pengfei LIN Niklas BOERS Weichen TAO Yutong CHEN BO LIU Haijie LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Earth System Modelling School of Engineering and DesignTechnical University of MunichOttobrunn 85521Germany Potsdam Institute for Climate Impact Research Potsdam 14412Germany Department of Mathematics and Global Systems Institute University of ExeterExeter EX44QEUK CMA Earth System Modeling and Prediction Centre(CEMC) Chinese Academy of Meteorological SciencesBeijing 100081China

Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future *** these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning ***,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth system Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST ***,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to ***,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to *** enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales *** study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

关键词： generative adversarial networks model bias deep learning El Niño-Southern Oscillation marine heatwaves

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Global Characterizations of Drydown Events from a Long-Term Satellite Soil Moisture Dataset

Global Characterizations of Drydown Events from a Long-Term ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yawei Xu Qing He Panpan Yao Hui Lu Kun Yang Andrew Feldman Daniel J Short Gianotti Dara Entekhabi Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Department of Civil Engineering The University of Tokyo Tokyo Japan State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge USA

Soil moisture drydown plays an important role in many hydrometeorological processes such as regulating surface energy budget, evapotranspiration, and infiltration. In this study, we analyzed the spatial and temporal characteristics of global soil moisture drydown using the daily-scale long-term satellite soil moisture product NNSM. We find that the time-series of τ S and τ L remained stable over the years. The spatial distribution of global τ S and τ L shows an anti-spatial correlation pattern, implying that strong land-atmosphere interaction in the short and long term occurs in different regions. τ S . of NNSM is closer to the observation measurement than SMAP. The results show that NNSM can provide a long-term global reference for global soil moisture memory characterization, and for improving land surface models.

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Development of FBG discharge sensor based on surface-mounted sensitization package

Development of FBG discharge sensor based on surface-mounted...

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International Conference on High Voltage Engineering and Application, ICHVE

作者： Kejie Wu Weigen Chen Zhixian Zhang Yuxuan Song Fan Liu Haoyuan Tian State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China State Grid Hubei Electric Power Research Institute Wuhan Hubei China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407519

In order to improve the sensitivity of transformer partial discharge detection, this paper developed a fiber grating partial discharge sensor based on surface-mounted sensitization package. Based on theoretical analysis, the sensor sensitizing diaphragm is designed and fabricated, and the fiber optic sensing system is built. The performance of the sensor was tested through experiments. The resonant frequency of the sensor acoustic diaphragm is $20.3kHz$ , the highly sensitive detection angle range is $0^{\circ}-50^{\circ}$ , the partial discharge detection sensitivity is higher than that of $PZT$ , and the sensitive detection of transformer partial discharge can be realized.

关键词： Partial discharges Vibrations Sensitivity Surface discharges Resonant frequency Vibration measurement Transformers

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

arXiv

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arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

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Metabolomic Profiling of Turnip (Brassica Rapa L.) Roots from Different Production Areas in China Based on Widely Targeted Metabolomics

SSRN

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SSRN 2025年

作者： Bu, Tingting Yin, Dongyue Zhong, Yeqi Zhang, Ling Hamed, Yahya S. Jiang, Lili Wang, Lei Wang, Jian Sun, Peilong Yang, Kai College of Food Science and Technology Zhejiang University of Technology Hangzhou310014 China Food Technology Department Faculty of Agriculture Suez Canal University Ismailia41522 Egypt State Key Laboratory of Tibetan Plateau Earth System Science Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China Xizang Extreme Pure Sky Biological Technology Co. Ltd Nagqu852000 China

Turnip (Brassica rapa L.) root is traditionally used as an edible and medicinal plant in Tibet. This study aimed to profile the non-volatile and volatile metabolites of turnip roots from Tibet and four other regions in China (Xinjiang, Sichuan, Liaoning, and Zhejiang) using widely targeted metabolomics (UPLC-ESI-QTRAP-MS/MS and GC-MS/MS). A total of 920 non-volatile and 27 volatile metabolites were identified, in which organic acids and derivatives, organic oxygen compounds, and lipids and lipid-like molecules were the three major groups of metabolites. 2-Phenylethyl isothiocyanate was the predominant volatile metabolite in different turnip roots. Further comparative analysis selected 45 differential metabolites to distinguish the metabolomic profiles ofTibetan turnip root. 4-Pentenyl glucosinolate and 2-hydroxy-4-pentenyl glucosinolate could be used as indicators to discriminate Tibetan turnip. These findings suggested widely targeted metabolomics could be used to profile different turnip roots, and glucosinolate compounds could be used to discriminate Tibetan turnip. © 2025, The Authors. All rights reserved.

关键词： Metabolites

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A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features

arXiv

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arXiv 2024年

作者： Wang, Hao Li, Nao School of Computer and Artificial Intelligence Beijing Technology and Business University Haidian District Beijing China School of International Economics and Management Beijing Technology and Business University Beijing China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Most current click-through rate prediction(CTR) models create explicit or implicit high-order feature crosses through Hadamard product or inner product, with little attention to the importance of feature crossing;only few models are either limited to the second-order explicit feature crossing, implicitly to high-order feature crossing, or can learn the importance of high-order explicit feature crossing but fail to provide good interpretability for the model. This paper proposes a new model, FiiNet (Multiple Order Feature Interaction Importance Neural Networks). The model first uses the selective kernel network (SKNet) to explicitly construct multi-order feature crosses. It dynamically learns the importance of feature interaction combinations in a fine-grained manner, increasing the attention weight of important feature cross combinations and reducing the weight of featureless crosses. To verify that the FiiNet model can dynamically learn the importance of feature interaction combinations in a fine-grained manner and improve the model's recommendation performance and interpretability, this paper compares it with many click-through rate prediction models on two real datasets, proving that the FiiNet model incorporating the selective kernel network can effectively improve the recommendation effect and provide better interpretability. FiiNet model implementations are available in PyTorch. Copyright © 2024, The Authors. All rights reserved.

关键词： Neural network models

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A control method with terrain classification and recognition for lower limb soft exosuit 22

A control method with terrain classification and recognition...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Ni, Jiangpeng Chen, Chunjie Wang, Zhuo Liu, Youfu Wu, Xinyu Liu, Yida Shenzhen Institutes of Adanced Technology Chinese Academy of Sciences Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

Soft Exosuit is a kind of Lower-limb wearable robots to augment and assist the wearer's performance. The wearer need different assistance modes to reduce the metabolic rate when walking on different terrains. Therefore, assistance modes need to be selected according to different terrains for the wearer of soft Exosuit. Recently, our team has designed a stable terrain classification and recognition system (TCRS) for the soft Exosuit to discriminate terrains and estimating environmental features. Through this system, soft Exosuit can perceive the environment to auxiliary control of the locomotion modes. A depth sensor with an inertial measurement unit(IMU) can acquire to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from the origin 3D point cloud. Then, they are classified to estimate terrain environmental features, including the incline angle of the slope, the width, and the stairs' height. Finally, the TCRS was evaluated by classifying and recognizing five basic terrains in three different experimental scenarios while the subject was wearing the soft Exosuit with the TCRS module. The results show that the terrain classification accuracy rate reaches 97.74 %, and the environmental features estimation error is less than 15 %. The promising results indicate the robustness and the potential application of the presented TCRS to provide proper auxiliary force to reduce the metabolic rate of wearers on different terrains. © 2021 IEEE.

关键词： Metabolism

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