检索结果-内蒙古大学图书馆

Artificial intelligence for geoscience:Progress,challenges,and perspectives

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

来源：评论

学校读者我要写书评

暂无评论

Photonic Terahertz Phased Array

arXiv

引用

arXiv 2024年

作者： Niu, Li Feng, Xi Zhang, Xueqian Lu, Yongchang Wang, Qingwei Xu, Quan Chen, Xieyu Ma, Jiajun Qiu, Haidi Sha, Wei E.I. Zhang, Shuang Alù, Andrea Zhang, Weili Han, Jiaguang Center for Terahertz Waves College of Precision Instrument and Optoelectronics Engineering The Key Laboratory of Optoelectronics Information and Technology Tianjin University Tianjin300072 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical & Electronic Engineering University of Hong Kong 999077 Hong Kong Photonics Initiative Advanced Science Research Center City University of New York New York10031 United States Physics Program Graduate Center City University of New York New York10016 United States School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China

Phased arrays are crucial in various technologies, such as radar and wireless communications, due to their ability to precisely control and steer electromagnetic waves. This precise control improves signal processing and enhances imaging performance. However, extending phased arrays to the terahertz (THz) frequency range has proven challenging, especially for high-frequency operation, broadband performance, two-dimensional (2D) phase control with large antenna arrays, and strong phase modulation. Here, we introduce a photonic platform to realize a THz phased array that bypasses the above challenges. Our method employs 2D phase coding with 2-bit across a broad THz frequency range from 0.8 to 1.4 THz. The core of our design is a pixelated nonlinear Pancharatnam-Berry metasurface driven by a spatially modulated femtosecond laser, allowing precise phase control of THz signals. We showcase the effectiveness of our method through four proof-of-concept applications: single beamforming, dual beamforming, imaging and vortex beam generation. The realized photonic platform provides a promising pathway for developing broadband phased arrays in the THz regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

来源：评论

学校读者我要写书评

暂无评论

Comparison of the Effectiveness of Machine Learning Techniques for the Number of Road Traffic Injuries

Comparison of the Effectiveness of Machine Learning Techniqu...

引用

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reduce Fingerprint Construction for Positioning IoT Devices Based on Generative Adversarial Nets 1

Reduce Fingerprint Construction for Positioning IoT Devices ...

引用

1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Tarekegn, Getaneh Berie Juang, Rong-Terng Lin, Hsin-Piao Munaye, Yirga Yayeh Belay Adege, Abebe National Taipei University of Technology Department of International Graduate Program in Electrical Engineering and Computer Science Taipei Taiwan Feng Chia University Department of Electronic Engineering Taichung Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei Taiwan Debre Markos University Department of Information Technology Debre Markos Ethiopia

ISBN: (纸本)9781665404839

Fingerprint-based positioning is popular and applicable for Internet of Things (IoT) applications to offer seamless, intelligent and adaptive location-aware services for IoT devices. However, it takes time and cost to build the radio-map. This paper proposed deep convolutional Generative adversarial nets (DCGANs) to minimize the site survey time and cost, and to mitigate signal fluctuations. The radio-map was designed for receiving radio signals from detectable wireless local area network (WLAN) and cellular networks in scalable environments. The proposed fingerprinting-based positioning is a sequential combination of the hybrid support vector machine and long short-term memory algorithms. The experimental results indicate that the proposed method achieves a promising and reasonable positioning performance for IoT devices in scalable wireless environments. © 2020 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Mechanically reprogrammable Pancharatnam-Berry metasurface for microwaves

引用

Advanced Photonics 2022年第1期4卷 69-79页

作者： Quan Xu Xiaoqiang Su Xueqian Zhang Lijuan Dong Lifeng Liu Yunlong Shi Qiu Wang Ming Kang Andrea Alù Shuang Zhang Jiaguang Han Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics EngineeringKey Laboratory of Optoelectronic Information Technology(Ministry of Education of China)TianjinChina Shanxi Datong University Institute of Solid State Physics and College of Physics and Electronic ScienceShanxi Province Key Laboratory of Microstructure Electromagnetic Functional MaterialsDatongChina Wuhan University of Technology School of Information EngineeringWuhanChina Tianjin Normal University College of Physics and Materials ScienceTianjinChina City University of New York Advanced Science Research CenterPhotonics InitiativeNew YorkUnited States City University of New York Graduate CenterPhysics ProgramNew YorkUnited States University of Hong Kong Faculty of ScienceDepartment of PhysicsHong KongChina University of Hong Kong Department of Electrical and Electronic EngineeringHong KongChina Guilin University of Electronic Technology Guangxi Key Laboratory of Optoelectronic Information ProcessingSchool of Optoelectronic EngineeringGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Metasurfaces have enabled the realization of several optical functionalities over an ultrathin platform,fostering the exciting field of flat *** metasurfaces are achieved by arranging a layout of static meta-atoms to imprint a desired operation on the impinging wavefront,but their functionality cannot be *** and programmability of metasurfaces are the next important step to broaden their impact,adding customized on-demand functionality in which each meta-atom can be individually *** demonstrate a mechanical metasurface platform with controllable rotation at the meta-atom level,which can implement continuous Pancharatnam–Berry phase control of circularly polarized *** the proof-of-concept experiments,we demonstrate metalensing,focused vortex beam generation,and holographic imaging in the same metasurface template,exhibiting versatility and superior *** dynamic control of electromagnetic waves using a single,low-cost metasurface paves an avenue towards practical applications,driving the field of reprogrammable intelligent metasurfaces for a variety of applications.

关键词： reprogrammable metasurfaces Pancharatnam-Berry phase mechanical metasurfaces microwaves

来源：评论

学校读者我要写书评

暂无评论

Dataset Weighting Features Using Gain Ratio To Improve Method Accuracy Naïve Bayesian Classification

引用

IOP Conference Series: Earth and Environmental Science 2021年第1期748卷

作者： Novriadi Antonius Siagian Sutarman Wage Sawaluddin Graduate Program of Computer Science Faculty of Computer Science and Information Technology

The Naïve Bayes method is proven to have a high speed when applied to large datasets, but the Naïve Bayes method has weaknesses when selecting attributes because Naïve Bayes is a statistical classification method that is only based on the Bayes theorem so that it can only be used to predict the probability of the class membership of a class independently. Independent without being able to do the selection of attributes that have a high correlation and correlation between one attribute with other attributes so that it can affect the value of accuracy. Naïve Bayesian Weight has been able to provide better accuracy than conventional Naïve Bayesian. Where an increase in the highest accuracy value obtained from the Water Quality dataset is equal to 88.57% in the Weight Naïve Bayesian classification model, while the lowest accuracy value is obtained from the Haberman dataset which is 78.95% in the conventional Naïve Bayesian classification model. The increase in accuracy of the Weight Naïve Bayesian classification model in the Water Quality dataset is 2.9%. While the increase in accuracy value in the Haberman dataset is 1.8%. If done the average accuracy of each dataset using the Weight Naïve Bayesian classification model is 2.35%. Based on the testing that has been done on all test data, it can be said that the Weight Naïve Bayesian classification model can provide better accuracy values than those produced by the conventional Naïve Bayesian classification model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Supervised Contrastive Learning Enhances MHC-II Peptide Binding Affinity Prediction

SSRN

引用

SSRN 2024年

作者： Shen, Long-Chen Liu, Yan Liu, Zi Zhang, Yumeng Wang, Zhikang Guo, Yuming Rossjohn, Jamie Song, Jiangning Yu, Dong-Jun School of Computer Science and Engineering Nanjing University of Science and Technology 200 Xiaolingwei Nanjing210094 China Department of Computer Science Yangzhou University Yangzhou225100 China School of Information Enginnering Jingdezhen Ceramic University Jingdezhen333403 China Monash Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVIC3800 Australia Monash Data Futures Institute Monash University MelbourneVIC3800 Australia School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200240 China Department of Epidemiology and Preventive Medicine Xiangya Hospital China Infection and Immunity Program Netherlands

Accurate prediction of major histocompatibility complex (MHC)-peptide binding affinity can improve our understanding of cellular immune responses and guide personalized immunotherapies. Nevertheless, the existing deep learning-based approaches for predicting MHC-II peptide interactions fall short of satisfactory performance and offer restricted model interpretability. In this study, we propose a novel deep neural network, termed ConBoTNet, to address the above issues by introducing the designed supervised contrastive learning and bottleneck transformer extractors. Specifically, the supervised contrastive learning pre-training enhances the model’s representative and generalizable capabilities on MHC-II peptides by pulling positive pairs closer and pushing negative pairs further in the feature space, while the bottleneck transformer module focuses on MHC-II peptide interactions to precisely identify binding cores and anchor positions in an unsupervised manner. Extensive experiments on benchmark datasets under 5-fold cross-validation, leave-one-molecule-out validation, independent testing, and binding core prediction settings highlighted the superiority of our proposed ConBoTNet over current state-of-the-art methods. Data distribution analysis in the latent feature space demonstrated that supervised contrastive learning can aggregate MHC-II-peptide samples with similar affinity labels and learn common features of similar affinity. Additionally, we interpreted the trained neural network by associating the attention weights with peptides and innovatively find both well-established and potential peptide motifs. This work not only introduces an innovative tool for accurately predicting MHC-II peptide affinity, but also provides new insights into a new paradigm for modeling essential biological interactions, advancing data-driven discovery in biomedicine. © 2024, The Authors. All rights reserved.

关键词： Peptides

来源：评论

学校读者我要写书评

暂无评论

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

引用

Scientific Reports 2023年第1期13卷 1-10页

作者： Kuo, Tzu-Yun Wang, Jen-Hung Huang, Yung-Wen Sung, Ting-Yi Chen, Ching-Tai Department of Biochemical Science and Technology National Taiwan University Taipei 10617 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Statistical Science Academia Sinica Taipei 11529 Taiwan Institute of Information Science Academia Sinica Taipei 11529 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Bioinformatics and Biomedical Engineering Asia University Taichung 41354 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms;its executable files, user manual, and sample data sets are freely available at https://***/comics/Software_*** . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline. © 2023, Springer Nature Limited.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving the accuracy of k-nearest neighbor (k-NN) using Synthetic Minority Oversampling Technique (SMOTE) and Gain Ratio (GR) for imbalanced class data

引用

AIP Conference Proceedings 2023年第1期2714卷

作者： Adli A. Nababan Sutarman Muhammad Zarlis Erna B. Nababan Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia

k-Nearest Neighbor (k-NN) has fairly good accuracy results on data with almost the same class (balanced), whereas for data whose class distribution is not balanced, the accuracy value of k-NN tends to be low, moreover k-NN does not differentiate each feature data, meaning that each feature has the same weight in determining the new data class so that before classifying the feature selection process it is necessary to select the features that are most relevant to the data class. In this research, to overcome this problem, we will propose a framework using the Synthetic Minority Oversampling Technique (SMOTE) method to solve the class imbalance problem and the Gain Ratio (GR) to perform feature selection, so that get a new dataset with a balanced class distribution and features relevant to class data. The datasets used in this research such as E-Coli, Glass, and New Thyroid. For objective results, the 10-fold-cross validation method will be used as an evaluation method with k values 1 to 10. The results of the research prove that SMOTE and GR can increase the accuracy of the k-NN method, where the highest increase occurred in the Glass dataset by a difference increase of 18.43%. The lowest increase in accuracy occurred in the new thyroid dataset with an increase of 8.73%. The increase in accuracy occurred in all datasets used with an average increase of 12.87

关键词：

来源：评论

学校读者我要写书评

暂无评论

An intelligent applied Fuzzy Logic to prediction the Parts per Million (PPM) as hydroponic nutrition on the based Internet of Things (IoT) 4

An intelligent applied Fuzzy Logic to prediction the Parts p...

引用

4th International Conference on Informatics and Computing, ICIC 2019

作者： Musa, Purnawarman Sugeru, Herik Mufza, Haya Fadhila Faculty of Computer Science and Information Technology Universitas Gunadarma Indonesia Faculty of Industrial Technology Study Program of Agrotechnology Universitas Gunadarma Indonesia

ISBN: (纸本)9781728122076

Hydroponic plants require regular monitoring of plants that grow well and can even cause plants to die. Because too late in providing nutrition or getting too little or too little food or routine monitoring in the hydroponic system. The innovation proposed in the agricultural sector that we suggest is the process of monitoring hydroponic plants in real-time. This study aims to predict the excess or lack of nutrient content based on the results of routine monitoring and has the analytical ability to determine the prediction of the value of Parts per Million (PPM) to find the nutritional value of plant media. Measurement of nutritional value using Electrical Conductivity (EC) and the potential of Hydrogen (pH) in hydroponic plant media. In this research, using the NFT hydroponic system as the application of fuzzy logic with the Mamdani method to determine the predictions of PPM values in providing nutrients at plants to adjusting the nutritional at each plant (e.g., for water spinach plants are need 1050 until 1400 PPM). The implemented is method Internet of Things (IoT) to remote sensing on measure using a smartphone as the representation of the measurement results on the hydroponic system. © 2019 IEEE.

关键词： Hydroponics

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：