检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

AVDNet: A small-sized vehicle detection network for aerial visual data

arXiv

引用

arXiv 2019年

作者： Mandal, Murari Shah, Manal Meena, Prashant Devi, Sanhita Vipparthi, Santosh Kumar Vision Intelligence Lab Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur302017 India

Detection of small-sized targets in aerial views is a challenging task due to the smallness of vehicle size, complex background, and monotonic object appearances. In this letter, we propose a one-stage vehicle detection network (AVDNet) to robustly detect small-sized vehicles in aerial scenes. In AVDNet, we introduced ConvRes residual blocks at multiple scales to alleviate the problem of vanishing features for smaller objects caused because of the inclusion of deeper convolutional layers. These residual blocks, along with enlarged output feature map, ensure the robust representation of the salient features for small-sized objects. Furthermore, we proposed a recurrent-feature aware visualization (RFAV) technique to analyze the network behavior. We also created a new airborne image data set (ABD) by annotating 1396 new objects in 79 aerial images for our experiments. The effectiveness of AVDNet is validated on VEDAI, DLR-3K, DOTA, and the combined (VEDAI, DLR-3K, DOTA, and ABD) data set. Experimental results demonstrate the significant performance improvement of the proposed method over state-of-the-art detection techniques in terms of mAP, computation, and space complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Rotation and Scale Invariant Bispectral Feature based Recognition of Contactless Palmprints

Rotation and Scale Invariant Bispectral Feature based Recogn...

引用

International Conference on Image and vision Computing New Zealand, IVCNZ

作者： Akmal-Jahan Mohamed-Abdul-Cader Jasmine Banks Kien Nguyen Thanh Vinod Chandran Vision and Signal Processing Lab School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

This paper proposes a new method for contactless palmprint recognition based on ridge orientation and bispectral invariant features. Although, this idea has been proposed in contactless finger images, use of ridge information has not been investigated in contactless palm and hand images to date. The advantage of using bispectral invariants from ridge profiles is that they are invariant to geometric transformations. The algorithm was tested with 1249 contactless palm images acquired from 40 users. Experimental results show that combining a number of ridge orientation profiles can improve the system performance to around 88% true positive rate at 5% false positive rate for 10 key-lines. The performance can be improved with the features from additional key-line configurations over the full hand. This method can be developed to suit high throughput lower security applications such as business premises and shopping malls.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CACAI: Comparative analysis of crypto apps in India

引用

AIP Conference Proceedings 2023年第1期2916卷

作者： Rahul Johari Anuj Talwar Suhas Harbola Jyotsna Yadav Himesh Nayak Deo Prakash Vidyarthi SWINGER: Security Wireless IoT Network Group of Engineering and Research University School of Information Communication and Technology (USICT) Guru Gobind Singh Indraprastha University Sector-16C Dwarka Delhi India Computer vision and Image processing lab University School of Information Communication and Technology (USICT) Guru Gobind Singh Indraprastha University Sector-16C Dwarka Delhi India School of Computer and Systems Sciences Parallel and Distributed Systems Lab JNU Delhi India

Crypto-currency is a decentralized digital currency which can be used as a medium of exchange. As it does not have any central authority, it is unregulated and a volatile asset. Globally the crypto market is worth a few trillion dollars. The Indian crypto market suffered a lot due to a ban imposed by the Reserve Bank of India (RBI), an Indian regulatory agency, which lasted for around two years. Even after the ban and cautions issued by the RBI, the crypto market in India touched a billion dollar mark in no time. Currently, the crypto market has more than 10 million Indian investors which is growing fast as the number of exchange apps in the Indian market increase with the ease of trading in crypto. In the current research work Comparative Analysis of Crypto App in India (CACAI) has been performed and results exceed the expectations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SoccerNet 2024 Challenges Results

arXiv

引用

arXiv 2024年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Joos, Victor Magera, Floriane Held, Jan Ghasemzadeh, Seyed Abolfazl Zhou, Xin Seweryn, Karolina Kowalczyk, Mateusz Mróz, Zuzanna Lukasik, Szymon Halon, Michal Mkhallati, Hassan Deliège, Adrien Hinojosa, Carlos Sanchez, Karen Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Gorski, Adam Clapés, Albert Boiarov, Andrei Afanasiev, Anton Xarles, Artur Scott, Atom Lim, ByoungKwon Yeung, Calvin Gonzalez, Cristian Rüfenacht, Dominic Pacilio, Enzo Deuser, Fabian Altawijri, Faisal Sami Cachón, Francisco Kim, HanKyul Wang, Haobo Choe, Hyeonmin Kim, Hyunwoo J. Kim, Il-Min Kang, Jae-Mo Tursunboev, Jamshid Yang, Jian Hong, Jihwan Lee, Jimin Zhang, Jing Lee, Junseok Zhang, Kexin Habel, Konrad Jiao, Licheng Li, Linyi Gutiérrez-Pérez, Marc Ortega, Marcelo Li, Menglong Lopatto, Milosz Kasatkin, Nikita Nemtsev, Nikolay Oswald, Norbert Udin, Oleg Kononov, Pavel Geng, Pei Alotaibi, Saad Ghazai Kim, Sehyung Ulasen, Sergei Escalera, Sergio Zhang, Shanshan Yang, Shuyuan Moon, Sunghwan Moeslund, Thomas B. Shandyba, Vasyl Golovkin, Vladimir Dai, Wei Chung, WonTaek Liu, Xinyu Zhu, Yongqiang Kim, Youngseo Li, Yuan Yang, Yuting Xiao, Yuxuan Cheng, Zehua Li, Zhihao Belgium King Abdullah University of Science and Technology Saudi Arabia Sportradar Switzerland UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States NASK National Research Institute Poland Belgium Sharif University of Technology Iran Footovision France Mobius Labs Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain TAHAKOM Saudi Arabia AIBrain Eidos.ai United Kingdom School of Computer Science and Engineering Nanjing University of Science and Technology China Constructor.tech Universitat de Barcelona Spain Computer Vision Center Spain Aalborg University Denmark Kyungpook National University Korea Republic of Intelligent Perception and Image Understanding Lab Xidian University China Germany Korea University Korea Republic of Warsaw University of Technology Poland University of Oxford United Kingdom China Science IntelliCloud Technology Co. Ltd China Robo Space Graduate School of Informatics Nagoya University Japan Tongji University China AGH University of Krakow Poland

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://***, with baselines and development kits available at https://***/SoccerNet. © 2024, CC BY.

关键词： HTTP

来源：评论

学校读者我要写书评

暂无评论

A NOVEL EXPERIMENTAL TECHNIQUE FOR RECOVERING CAMERA RESPONSE FUNCTION FOR HIGH DYNAMIC RANGE IMAGING 18

A NOVEL EXPERIMENTAL TECHNIQUE FOR RECOVERING CAMERA RESPONS...

引用

Proceedings of the 2nd International Conference on Advances in Image Processing

作者： Salvador Garcia Bernal Yuanhao Gong Bozhi Liu Guoping Qiu Novel Optics Computer Vision Lab ETH Zurich College of Information Engineering Shenzhen University

ISBN: (纸本)9781450364607

Recovering the camera's response function has wide applica-tion in image processing and computer *** computational approach using multiple differently exposed photographs is based on solving a constrained linear least square problem and can only recover the function up to a scaling *** this paper,we present a novel experiment based technique to recover the true response curves of spe-cific *** method first measures the average pixel intensities of a neutrally-grey cardboard for the full range of the shutter speed and gain control values of the ***-ter a simple data smoothing procedure,the camera's response function is recovered through simple table *** have implemented our technique on different kinds of sensors and show that it works *** new technique recover the actual range of the sensor and is more accurate than the computa-tional *** procedure is easy to set up and simple to implement which will be useful for developing applications such as High Dynamic Ranging(HDR) imaging.

关键词： Exposure Camera Response Curve HDR

来源：评论

学校读者我要写书评

暂无评论

A Review of Generalized Zero-Shot Learning Methods

arXiv

引用

arXiv 2020年

作者： Pourpanah, Farhad Abdar, Moloud Luo, Yuxuan Zhou, Xinlei Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Jonathan Wu, Q.M. The Centre for Computer Vision and Deep Learning Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada Deakin University Australia The Department of Computer Science City University of Hong Kong Hong Kong The College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. Firstly, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Effect of Undermining on Suture Forces During Simulated Skin Flap Surgeries with a Three-Dimensional Finite Element Method

SSRN

引用

SSRN 2024年

作者： Guo, Wenzhangzhi Lau, Allison Tsz Kwan Davies, Joel C. Forte, Vito Grinspun, Eitan Kahrs, Lueder Alexander Medical Computer Vision and Robotics Lab University of Toronto TorontoON Canada Department of Computer Science University of Toronto 40 St George St TorontoM5S 2E4 Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention The Hospital for Sick Children 170 Elizabeth St TorontoM5G 1E8 Canada Department of Otolaryngology - Head and Neck Surgery Sinai Health System University of Toronto 600 University Ave TorontoM5G 1X5 Canada Department of Otolaryngology - Head and Neck Surgery University of Toronto 600 University Ave TorontoM5G 1X5 Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga 3359 Mississauga Rd MississaugaL5L 1C6 Canada Institute of Biomedical Engineering University of Toronto 164 College St TorontoM5S 3G9 Canada

Skin flaps are common procedures used by surgeons to cover an excised area during the reconstruction of a defect. It is often a challenging task for a surgeon to come up with the most optimal design for a patient. In this paper, we set up a simulation system based on the finite element method for one of the most common flap types - the rhomboid flap. Instead of using the standard 2D planner patch, we constructed a 3D patch with multiple layers. This allowed us to investigate the impact of different undermining areas and depths. We compared the suture forces for each case and identified vertices with the largest suture force. The shape of the final suture line is also visualized for each case, which is an important clue when deciding on the most optimal skin flap orientation according to medical textbooks. We found that under the optimal undermining setup, the maximum suture force is around 0.7N for top of the undermined layer and 1.0N for bottom of the undermined layer. When measuring difference in final suture line shape, the maximum normalized Hausdorff distance is 0.099, which suggests that different undermining region can have significant impact on the shape of the suture line, especially in the tail region. After analyzing the suture force plots, we provided recommendations on the most optimal undermining region for rhomboid flaps. © 2024, The Authors. All rights reserved.

关键词： Finite element method

来源：评论

学校读者我要写书评

暂无评论

A new unified method for detecting text from marathon runners and sports players in video

arXiv

引用

arXiv 2020年

作者： Nag, Sauradip Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Sydney Australia

Detecting text located on the torsos of marathon runners and sports players in video is a challenging issue due to poor quality and adverse effects caused by flexible/colorful clothing, and different structures of human bodies or actions. This paper presents a new unified method for tackling the above challenges. The proposed method fuses gradient magnitude and direction coherence of text pixels in a new way for detecting candidate regions. Candidate regions are used for determining the number of temporal frame clusters obtained by K-means clustering on frame differences. This process in turn detects key frames. The proposed method explores Bayesian probability for skin portions using color values at both pixel and component levels of temporal frames, which provides fused images with skin components. Based on skin information, the proposed method then detects faces and torsos by finding structural and spatial coherences between them. We further propose adaptive pixels linking a deep learning model for text detection from torso regions. The proposed method is tested on our own dataset collected from marathon/sports video and three standard datasets, namely, RBNR, MMM and R-ID of marathon images, to evaluate the performance. In addition, the proposed method is also tested on the standard natural scene datasets, namely, CTW1500 and MS-COCO text datasets, to show the objectiveness of the proposed method. A comparative study with the state-of-the-art methods on bib number/text detection of different datasets shows that the proposed method outperforms the existing methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

引用

Medical Image Analysis 2022年 78卷 102396页

作者： Subramaniam, Pooja Kossen, Tabea Ritter, Kerstin Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar Madai, Vince I. CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Max-von-Laue-Str. 2 Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom QUEST-Center for Transforming Biomedical Research Berlin Institute of Health Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：