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High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

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Innovation 2025年第6期6卷

作者： Hu, Jinkang Peng, Dailiang Chen, Jing M. Huete, Alfredo R. Yu, Le Lou, Zihang Cheng, Enhui Yang, Xuan Zhang, Bing Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China College of Resource and Environment University of Chinese Academy of Sciences Beijing100094 China School of Geographical Sciences Fujian Normal University Fuzhou350117 China Department of Geography and Planning University of Toronto TorontoONM5S 3G3 Canada School of Life Sciences University of Technology Sydney UltimoNSW2007 Australia Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute of Agricultural Remote Sensing and Information Technology Application College of Environmental and Resource Sciences Zhejiang University Hangzhou310058 China China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

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LSTM-based Pedestrian Trajectory Prediction Model under the General Direction Mechanism: DIR-LSTM 3

LSTM-based Pedestrian Trajectory Prediction Model under the ...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Luo, Junyuan Fu, Qiongfang Xiao, Jianhua Xiao, Hongbo Liao, Jiyong School of Computer Science and Engineering Huaihua University Hunan Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Hunan Huaihua China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9798400716775

Accurate prediction of future traffic flow trends is essential to solve urban transportation problems. However, traffic flow prediction faces great challenges due to the multimodal nature of pedestrian behavior and the complexity of the traffic environment. Although a large number of studies have been conducted to investigate these issues in depth, there are still some limitations. In order to address these challenges more effectively, we propose a pedestrian trajectory prediction model based on long-short-term memory networks (LSTMs): the DIR-LSTM. The model introduces an innovative generalized direction mechanism and a self-attention mechanism, which captures pedestrian movement patterns more comprehensively and accurately by predicting overall directional movements first and then gradually subdividing them into individual directions of movement. The DIR-LSTM is designed to address the challenges posed by the diversity of pedestrian behaviors and the complexity of urban environments. To validate the state-of-the-art of the model, we conducted experiments using the publicly available ETH [10] and UCY [9] datasets. The experiments demonstrate that DIR-LSTM performs better in terms of accuracy compared to other models, providing a more reliable prediction tool for future urban traffic management. © 2023 ACM.

关键词： Long short-term memory

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Design and Implementation of Carrier Detection System Based on FPGA 2

Design and Implementation of Carrier Detection System Based ...

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2020 2nd International Conference on Computer Modeling, Simulation and Algorithm, CMSA 2020

作者： Zhang, Xian Qu, Taiguo Liu, Yiwen Yin, Dong Deng, Shaowei School of Computer Science and Engineering Huaihua University Huaihua Hunan China Key Laboratory of Intelligent Control Technology Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Huaihua Hunan418000 China

In order to better monitor and manage the signal transmission in the communication system and base station, and carrier detection is more effective. This paper designs a carrier(Carrier frequency) detection system based on FPGA, including system design requirements, FPGA selection, system framework design, carrier detection method design and so on. The performance indexes of the system are tested, such as carrier frequency range, carrier search time, carrier detection number and CPU consumption rate. The system has the advantages of high flexibility and good portability, and has a good application prospect in communication system, base station and other equipment. © Published under licence by IOP Publishing Ltd.

关键词： Field programmable gate arrays (FPGA)

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Leaf area index estimation of winter pepper based on canopy spectral data and simulated bands of satellite 4th

Leaf area index estimation of winter pepper based on canopy ...

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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Li, Dan Jiang, Hao Chen, Shuisen Wang, Chongyang Huang, Siyu Liu, Wei Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing & GIS Application & Engineer Technology Research Center for Remote Sensing Big Data Application of Guangdong Province Guangzhou Institute of Geography Guangzhou510070 China

ISBN: (纸本)9789811039652

Leaf area index (LAI) is an important indicator of crop growth status. In this paper, the relationships between canopy reflectance at 400–2500 nm and leaf area index (LAI) in pepper crop were studied. 102 pair of canopy reflectance and LAI of pepper were collected in 2014–2015. Reflectance of canopy were measured in the field over a spectral range of 400–2500 nm. Simultaneously, the LAI were collected by the LAI-2000. Estimation models of LAI were developed based on the whole spectrum range by partial least squares regression (PLSR) and support vector regression (SVR), respectively. Then the field canopy spectra were resampled according to the band response functions of seven satellite sensors. They were the Vegetation and environment monitoring on a new micro-satellite (VENlS), Worldview-2 (WV-2), RapidEye-1 (RE-1), HJ1/CCD1, Sentinel-2, Landsat 8/OLI and GaoFen (GF) 1/WFV1. The values of common used spectral indices were calculated based on the simulated sensor bands, respectively. Prediction models were also developed based on the spectral indices and simulated bands. The results showed that the PLSR model by whole spectrum had the good accuracy of LAI estimation with the R2c = 0.726, RMSEc = 0.462, R2cv = 0.635, RMSEcv = 0.538. For the simulated satellite datasets, the better LAI estimation were obtained by Sentinel-2 and Venls bands with the R2cv greater than 0.600 and RMSEcv less than 0.557. The Estimation model by simulated WV-2 bands, and RE-1 bands had the lowest performance with the R2cv between 0.50 and 0.55, and RMSEcv between 0.600 and 0.623. The inversion results demonstrated the potential of the multispectral remote sensing data to calibrate the LAI estimation model of winter pepper for the precision agriculture application. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Remote sensing

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Malware Variant Detection Using Opcode Image Recognition with Small Training Sets

Malware Variant Detection Using Opcode Image Recognition wit...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Jixin Zhang Zheng Qin Hui Yin Lu Ou Sheng Xiao Yupeng Hu College of Computer Science and Electronic Engineering Hunan Key Laboratory of Big Data Research and Application Hunan China

ISBN: (纸本)9781509022809

Malware detection becomes mission critical as its threats spread from personal computers to industrial control systems. Modern malware generally equips with sophisticated anti-detection mechanisms such as code-morphism, which allows the malware to evolve into many variants and bypass traditional code feature based detection systems. In this paper, we propose to disassemble binary executables into opcodes sequences, and then convert the opcodes into images. By comparing the opcode images generated from binary targets with the opcode images generated from known malware sample codes, we can detect if the target binary executables contain variants of these known malwares. Theoretical analysis and real-life experiments results show that malware detection using visualized analysis is comparable in terms of accuracy, our approach can significantly improve 15\% of detection accuracy when the detection set contains a large quantity of binaries and the training set is small.

关键词： Malware Image recognition Feature extraction Training Principal component analysis Support vector machines

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Leveraging proficiency and preference for online Karaoke recommendation

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Frontiers of Computer Science 2020年第2期14卷 273-290页

作者： Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China School of Computer and Information Hefei University of TechnologyHefei 230026China Eller College of Management The University of ArizonaArizona 85721-0108USA iFlyTek Research Hefei 230026China

Recently,many online Karaoke(KTV)platforms have been released,where music lovers sing songs on these *** the meantime,the system automatically evaluates user proficiency according to their singing *** approximate songs to users can initialize singers5 participation and improve users,loyalty to these ***,this is not an easy task due to the unique characteristics of these ***,since users may be not achieving high scores evaluated by the system on their favorite songs,how to balance user preferences with user proficiency on singing for song recommendation is still ***,the sparsity of the user-song interaction behavior may greatly impact the recommendation *** solve the above two challenges,in this paper,we propose an informationfused song recommendation model by considering the unique characteristics of the singing ***,we first devise a pseudo-rating matrix by combing users’singing behavior and the system evaluations,thus users'preferences and proficiency are *** we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating ***,extensive experimental results on a real-world dataset show the effectiveness of our proposed model.

关键词： KTV matrix factorization recommendation system

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Reform of Blended-Teaching Mode for Discipline English Based on Mobile Terminal 17th

Reform of Blended-Teaching Mode for Discipline English Based...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Yu, Niefang Peng, Xiaoning Li, Xiaomei Lu, Youmin School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924455

In the context of the era of big data, the rise of using mobile terminal is also putting forward new requirements for the teaching of Discipline English. Based on some general problems in the teaching practice of this course, we discuss whether adopting the blended-teaching mode can expand the learning space and time, enrich the learning interaction, and improve the learning efficiency in the context of big data based on mobile terminals. And then improve the quality of teaching and enhance application ability for the Discipline English. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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Course Homework Reform in Universities Based on Extended Reality 17th

Course Homework Reform in Universities Based on Extended Rea...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Fu, Jinrong Liu, Yiwen Mi, Chunqiao Peng, Xiaoning Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

In view of the serious issues commonly existing with coursework in Chinese universities at present, such as its original function weakening or being suppressed, its form being too abstract and lack of elaborate design. In order to resolve these problems observed in usual homework, this paper proposes a reform scheme for cloud coursework based on Extended Reality and Bloom model. It mainly includes five key parts, such as interaction, scene, comprehensive evaluation, digital twin and data analysis. In addition, the reform’s effectiveness is illustrated through amassing and comparing the feedback of college students for teaching. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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A Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Zhu, Zhirong Liu, Yiwen Gao, Yanxia Wen, Wenkan Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

With the rise of data storage computing and IoT technology. The increase in data volume and user demand, the accurate delivery of data and low latency during transmission become important factors that affect the end-user experience. To address this issue, previous authors have proposed the concept of edge computings. In the general environment of edge computing, reasonable scheduling of edge caches can largely achieve low latency and high efficiency, thus improving user experience. In this paper, based on existing research, we propose a combination of a joint learning framework for cache prediction based on region popularity and an edge collaborative cache value optimization method to further improve cache hit rate and cache utilization efficiency. The method obtains excellent expected results through simulation experiments. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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research on Multi-dimensional Bilingual Teaching Model of Computer Courses Supported by Artificial Intelligence 17th

Research on Multi-dimensional Bilingual Teaching Model of Co...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Zhao, Changhua Mi, Chunqiao Tangbo School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

Aiming at the problems of the integration of computer course teaching with international teaching and engineering certification, this study designs a multi-dimensional teaching mode of computer courses through the support of Artificial Intelligence. It analyzes the teaching resources, learning services, teaching process and teaching means involved in computer course teaching. Taking the course of "Computer Introduction" as an example, this study explores the multi-dimensional bilingual teaching mode supported by Artificial Intelligence, trains computer talents who meet the ability requirements of the era of Artificial Intelligence, and provides ideas for the teaching innovation of other courses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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