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China’s 10-year progress in DC gas-insulated equipment:From basic research to industry perspective

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iEnergy 2022年第4期1卷 400-433页

作者： Chuanyang Li Changhong Zhang Jinzhuang Lv Fangwei Liang Zuodong Liang Xianhao Fan Uwe Riechert Zhen Li Peng Liu Jianyi Xue Cheng Pan Geng Chen Lei Zhang Zheming Wang Wu Lu Hucheng Liang Zijun Pan Weijian Zhuang Giovanni Mazzanti Davide Fabiani Bo Liu Shaohua Cao Jianying Zhong Yuan Deng Zhenle Nan Jingen Tang Jinliang He State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Maintenance&Test Center of EHV Power Transmission Company China Southern Power Grid Guangzhou 510663China EHV Power Transmission Company China Southern Power Grid Guangzhou 510663China Hitachi Energy Switzerland Ltd ZürichSwitzerland Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbin 150080China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong UniversityXi’an 710049China School of Electrical Engineering and Automa-tion Hefei University of TechnologyHefei 230009China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical Engineering Shanghai University of Electric PowerShanghai 200090China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Department of Electrical Electronic and Information EngineeringUniversity of BolognaBologna 40136Italy Shandong Taikai Electrical Insulation Co. Ltd.Tai’an 271000China PingGao Group CO. Ltd.Pingdingshan 467001China China XD Group Co. Ltd.Xi’an 710075China Jiangsu Jinxin Electric Appliance Co. Ltd.Yangzhou 225202China

The construction of the future energy structure of China under the 2050 carbon-neutral vision requires compact direct current(DC)gas-insulation equipment as important nodes and solutions to support electric power transmission and distribution of long-distance and *** paper reviews China's 10-year progress in DC gas-insulated *** progresses in basic research and industry perspective are presented,with related scientific issues and technical bottlenecks being *** progress in DC gas-insulated equipment worldwide(Europe,Japan,America)is also reported briefly.

关键词： DC gas-insulated switchgear(GIS) DC gas-insulated power transmission line(GIL) surface charge flashover metal particle offshore projects

Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules

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arXiv 2021年

作者： Fu, Haisheng Liang, Feng Lin, Jianping Li, Bing Akbari, Mohammad Liang, Jie Zhang, Guohe Liu, Dong Tu, Chengjie Han, Jingning The School of Microelectronics Xi’an Jiaotong University Xi’an China The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The School of Engineering Science Simon Fraser University Canada The Tencent Technologies China The Google Inc. United States

Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures. Various models have been proposed, such as autoregressive, softmax, logistic mixture, Gaussian mixture, and Laplacian. Existing schemes only use one of these models. However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image. In this paper, we propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations, which can adapt to different contents in different images and different regions of one image more accurately and efficiently, given the same complexity. Besides, in the encoding/decoding network design part, we propose a concatenated residual blocks (CRB), where multiple residual blocks are serially connected with additional shortcut connections. The CRB can improve the learning ability of the network, which can further improve the compression performance. Experimental results using the Kodak, Tecnick-100 and Tecnick-40 datasets show that the proposed scheme outperforms all the leading learning-based methods and existing compression standards including VVC intra coding (4:4:4 and 4:2:0) in terms of the PSNR and MS-SSIM. The source code is available at https://***/fengyurenpingsheng. Copyright © 2021, The Authors. All rights reserved.

关键词： Image compression

Modeling Soybean Cultivation Suitability in China and its Future Trends in Climate Change Scenarios

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SSRN

SSRN 2023年

作者： Zhu, Qinyan Wang, Fumin Yi, Qiuxiang Zhang, Xiaoyang Chen, Siting Zheng, Jueyi Li, Jiale Xu, Tianyue Peng, Dailiang Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou310058 China Key Laboratory of Agricultural Remote Sensing and Information System Zhejiang University Hangzhou310058 China School of Geomatics and Municipal Engineering Zhejiang University of Water Resources and Electric Power Hangzhou311228 China Geospatial Sciences Center of Excellence Department of Geography & Geospatial Sciences South Dakota State University BrookingsSD57007 United States Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health Zhejiang University Hangzhou310058 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Soybean is an important source of oil and vegetable protein and plays a key role in agricultural production and economy. A suitability evaluation of soybean cultivation is important for identifying potential soybean planting areas. Based on the raster data of soybean harvest ratio (FSHA) and climate-soil-topographic environmental factors, we used MaxEnt to simulate the soybean planting suitability and potential distribution in China and the future trends of soybean cultivation under climate change. The result shows that the suitability of soybean cultivation was primarily influenced by elevation, precipitation of warmest quarter, capacity of the clay fraction, slope, mean diurnal temperature range, accumulated temperature ≥10℃, solar radiation and topsoil gravel content. High-suitability and moderate-suitability area are respectively 44.61 Mha and 71.12 Mha in China. High-suitability areas for soybean are mainly concentrated in the Northeast Plain, the North China Plain and the northern parts of the middle and lower Yangtze River plain. There were many provinces with high soybean planting potential but low development degrees, including Hebei, Henan, Shandong, Tianjin, Jilin, Liaoning, Jiangsu, Hubei and Shaanxi. Under future climate scenarios (SSP126, SSP245 and SSP585) of multiple global circulation models in CMIP6, the area highly or moderately suitable for soybean cultivation is generally stable, while low suitability area shows growth evidently. The total increase of three suitability area is the most obvious under SSP245 during 2021-2040, accounting for more than 5% in the base of historical data. In the future, most parts of Northeast China (eastern Inner Mongolia, Heilongjiang and eastern and northern Jilin), parts of north-central China (northern Hebei, northern Shanxi and Shaanxi) and parts of southwest China (eastern Sichuan, Chongqing, northern Guizhou and Yunnan) may be more suitable for soybean cultivation. This study provides a practical reference for

关键词： Climate change

NewsGPT: ChatGPT Integration for Robot-Reporter

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arXiv 2023年

作者： Hireche, Abdelhadi Belkacem, Abdelkader Nasreddine Jamil, Sadia Chen, Chao College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates School of International Communications University of Nottingham Ningbo China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China

The integration of large language models (LLMs) with social robots has emerged as a promising avenue for enhancing human–robot interactions at a time when news reports generated by artificial intelligence (AI) are gaining in credibility. This integration is expected to intensify and become a more productive resource for journalism, media, communication, and education. In this paper a novel system is proposed that integrates AI’s generative pretrained transformer (GPT) model with the Pepper robot, with the aim of improving the robot’s natural language understanding and response generation capabilities for enhanced social interactions. By leveraging GPT’s powerful language processing capabilities, this system offers a comprehensive pipeline that incorporates voice input recording, speech-to-text transcription, context analysis, and text-to-speech synthesis action generation. The Pepper robot is enabled to comprehend user queries, generate informative responses with general knowledge, maintain contextually relevant conversations, and act as a more domain-oriented news reporter. It is also linked with a news resource and powered with a Google search capability. To evaluate the performance of the framework, experiments were conducted involving a set of diverse questions. The robot’s responses were assessed on the basis of eight criteria, including relevance, context, and fluency. Despite some identified limitations, this system contributes to the field of journalism and human–robot interaction by showcasing the potential of integrating LLMs with social robots. The proposed framework opens up opportunities for improving the conversational capabilities of robots, enabling interactions that are smoother, more engaging, and more context aware. © 2023, CC0.

关键词： Human robot interaction

In-Plane and Out-of-Plane Anisotropy in the Optical and Dielectric Properties of Yba2cu3o7-x Superconducting Film

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SSRN

SSRN 2024年

作者： Wang, Yueming Sun, Weixuan Zhao, Minglin Li, Yongfu Wei, Mingyang Jin, Kui Li, Qian Zhou, Xiang'an Ha, Yating Lian, Jie School of Information Science and Engineering Shandong Provincial Key Laboratory of Laser Technology and Application Shandong University Qingdao266237 China Center for Optics Research and Engineering Key Laboratory of Laser & Infrared System Ministry of Education Shandong University Qingdao266237 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Science Jiangsu University of Science and Technology Zhenjiang212003 China

YBa2Cu3O7-x (YBCO) is a crucial aspect of research in the field of superconducting applications, where its high anisotropy is generally undesirable in strong power transmission. However, from a dialectical perspective, a thorough investigation of material anisotropy can offer an additional degree of freedom to tune potential properties and design innovative devices. This work employed pulsed laser deposition to fabricate a high-quality YBCO thin film (Tc = 89K) and used the Mueller matrix spectroscopic ellipsometer (MMSE) to determine the complete dielectric tensor of the YBCO film across the ultraviolet to near-infrared spectrum (245-1000nm), enabling a comprehensive, quantitative study of the optical anisotropy of YBCO. It provided optical constants and dielectric function spectra for different axes. We found that YBCO exhibits birefringence and dichroism both in-plane and out-of-plane. By combining standard critical point (SCP) analysis and first-principles calculations, we identified specific interband transitions related to SCP in the YBCO dielectric spectra, revealing the physical essence of anisotropic optical transitions from a quantum mechanical standpoint. Temperature-dependent studies of the transitions and optical constants were conducted, highlighting the significant potential of YBCO for optical device applications. The work also holds relevance for understanding the origins of copper-based high-temperature superconductivity. © 2024, The Authors. All rights reserved.

关键词： Barium compounds

Large-Scale Diurnal Responses of Terrestrial Photosynthesis to Varying Heat and Water Stresses: Implications for Monitoring Sif Variations from Satellite Observations

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SSRN

SSRN 2024年

作者： Zhao, Dayang Zhang, Zhaoying Zhang, Yongguang International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu Nanjing210023 China Jiangsu International Joint Carbon Neutrality Laboratory Jiangsu210023 China

Understanding diurnal patterns of plant photosynthesis is crucial for assessing the impacts of water and heat stresses on the carbon cycle under climate change. However, few studies have explored how large-scale photosynthesis responds to varying water and heat conditions on a diurnal scale. It also remains unclear how such discrepancies in diurnal responses affect assessments of photosynthesis variations using polar-orbiting satellite observations with fixed observation times. Here, we used Orbiting Carbon Observatory-3 (OCO-3) solar-induced chlorophyll fluorescence (SIF) observations to model four years of hourly SIF data for each day during the summer season across mainland China. Then, we comprehensively investigated the diurnal responses of SIF to varying water and heat conditions. Furthermore, we demonstrated the causes of uncertainties in assessing daily mean SIF variations from polar-orbiting satellite instantaneous observations and how they varied with water and heat conditions. Our findings revealed that warming could promote SIF throughout the day in forests except under severe water stress, whereas such patterns were not significant for grasslands. Water stress impaired SIF at different times of the day across both forests and grasslands, except in the morning for forests. Morning SIF exhibited higher sensitivity to heat conditions but lower sensitivity to water conditions in forests and grasslands compared to midday and afternoon periods. Morning instantaneous observations through daily correction factors tended to overestimate variations of daily SIF whereas midday observations tended to underestimate them. Such biases arose from discrepancies in diurnal responses of photosynthesis to water and heat conditions and intensified with increasing water stress and heat stress. Our findings enhance the understanding of diurnal responses of large-scale plant photosynthesis to heat and water stresses and highlight the importance of observation time in monitorin

关键词： Photosynthesis

Joint power allocation and beamforming for non-orthogonal multiple access (NOMA) in 5G millimeter-wave communications

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arXiv 2017年

作者： Xiao, Zhenyu Zhu, Lipeng Choi, Jinho Xia, Pengfei Xia, Xiang-Gen School of Electronic and Information Engineering Beihang University Beijing100191 China Beijing Key Laboratory for Network-Based Cooperative Air Traffic Management Beijing Laboratory for General Aviation Technology Beijing100191 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Korea Republic of School of Electronics and Information Engineering Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China College of Information Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

In this paper we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) communications (mmWave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mmWave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mmWave-NOMA to more-user mmWave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieve close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access (TDMA). Copyright © 2017, The Authors. All rights reserved.

关键词： Millimeter waves

What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs

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arXiv 2019年

作者： Huang, Zhongling Pan, Zongxu Lei, Bin School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Institute of Electronics Chinese Academy of Sciences Beijing100190 China

Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated dataset in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in SAR image interpretation. Transfer learning provides an effective way to solve this problem by borrowing the knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pre-trained with a large-scale natural image dataset, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR application because of the prominent discrepancy between SAR and optical images. In this paper, we attempt to discuss three issues that are seldom studied before in detail: (1) what network and source tasks are better to transfer to SAR targets, (2) in which layer are transferred features more generic to SAR targets and (3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multi-source data with domain adaptation is proposed in this paper to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models

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arXiv 2023年

作者： Qi, Xiyu Wu, Yifan Mao, Yongqiang Zhang, Wenhui Zhang, Yidan Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM's dependency on manual guidance given its category-agnostic nature, we identified unexplored potential within few-shot semantic segmentation tasks for remote sensing imagery. This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided mask to produce coarse pixel-wise prompts for SAM. Extensive experiments on the DLRSD datasets underlines the superiority of our approach, outperforming other available few-shot methodologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation