检索结果-内蒙古大学图书馆

2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Ran, Hao Cheng, Shi Sun, Yifei Shan, Yuyuan Jin, Honglin Lu, Hui Shaanxi Normal University School of Computer Science Xi'an710119 China Shaanxi Normal University School of Physics and Information Technology Xi'an710119 China Beihang University School of Electronic and Information Engineering Beijing100191 China

ISBN: (纸本)9798331504755

Traditional intelligent algorithms lack efficient utilization of knowledge of the agile earth observation satellite (AEOS) problem and the knowledge generated during the search process. To use the relevant knowledge and improve the algorithm-solving efficiency, this paper proposes an improved brain storm optimization in objective space (IBSOOS) algorithm based on knowledge strategy: 1) We use crossover and mutation strategies in the genetic algorithm (GA) to replace the original new solution generation strategy. This approach effectively handles non-continuous problems. 2) We introduce the worst solution updating strategy to enhance the algorithm's stability. During each iteration, this strategy adjusts the worst solution to learn from the target optimal solution. 3) We design a method for generating initial solutions, utilizing prior knowledge of the problem to pinpoint the range of candidate solutions swiftly. The experimental study compared the proposed algorithm with three other algorithms across four datasets. The results show that it significantly outperforms the others in solving AEOS mission planning problems. © 2024 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Ultra-wide FOV meta-camera with transformerneural-network color imaging methodology

引用

Advanced Photonics 2024年第5期6卷 31-40页

作者： Yan Liu Wen-Dong Li Kun-Yuan Xin Ze-Ming Chen Zun-Yi Chen Rui Chen Xiao-Dong Chen Fu-Li Zhao Wei-Shi Zheng Jian-Wen Dong Sun Yat-sen University School of PhysicsState Key Laboratory of Optoelectronic Materials and TechnologiesGuangzhouChina Sun Yat-sen University School of Computer Science and EngineeringGuangzhouChina

Planar cameras with high performance and wide field of view(FOV)are critical in various fields,requiring highly compact and integrated *** wide FOV metalenses show great potential for ultrathin optical components,but there is a set of tricky challenges,such as chromatic aberrations correction,central bright speckle removal,and image quality improvement of wide *** design a neural meta-camera by introducing a knowledge-fused data-driven paradigm equipped with transformer-based *** a paradigm enables the network to sequentially assimilate the physical prior and experimental data of the metalens,and thus can effectively mitigate the aforementioned *** ultra-wide FOV metacamera,integrating an off-axis monochromatic aberration-corrected metalens with a neural CMOS image sensor without any relay lenses,is employed to demonstrate the ***-quality reconstructed results of color images and real scene images at different distances validate that the proposed metacamera can achieve an ultra-wide FOV(>100 deg)and full-color images with the correction of chromatic aberration,distortion,and central bright speckle,and the contrast increase up to 13.5 ***,coupled with its compact size(<0.13 cm^(3)),portability,and full-color imaging capacity,the neural meta-camera emerges as a compelling alternative for applications,such as micro-navigation,microendoscopes,and various on-chip devices.

关键词： metalens meta-camera wide field of view neural network color imaging compact imaging device

来源：评论

学校读者我要写书评

暂无评论

Wavelength-multiplexed orbital angular momentum meta-holography

引用

PhotoniX 2024年第1期5卷 276-289页

作者： Jaehyuck Jang Seong-Won Moon Joohoon Kim Jungho Mun Stefan A.Maier Haoran Ren Junsuk Rho Department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea Department of Materials Science and Engineering Stanford UniversityStanfordCA 94305USA Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907USA School of Physics and Astronomy Monash UniversityMelbourneVIC 3800Australia Department of Physics Imperial College LondonLondon SW72AZUK Department of Electrical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics Pohang 37673Republic of Korea National Institute of Nanomaterials Technology(NINT) Pohang 37673Republic of Korea

The field of high-bandwidth holography has been extensively studied over the past *** angular momentum(OAM)holography,which utilizes vortex beams with theoretically unbounded OAM modes as information carriers,showcases the large capacitance of hologram ***,OAM holography has been limited to a single wavelength,restricting its potential for full-color holography and *** this study,we propose wavelength and OAM multiplexed holography that utilizes the multiple dimensions of light—wavelength and OAM—to provide a multi-color platform that expands the information capacity of holographic storage *** proposed wavelength-OAM multiplexed holography is physically realized by a metasurface,the state-of-the-art optical element consisting of an array of artificially engineered *** silicon meta-atoms,the constituents of the metasurface,are engineered to possess wavelength selectivity by tailoring the dispersion of polarization *** meta-atoms are used to encode the calculated OAM-preserved phase maps based on our *** sampling grid of the phase map is rotated by 45°,which effectively suppress higher-order diffraction,providing a great strategy for achieving large field-of-view(FOV)*** successfully demonstrate six holographic images that are selectively reconstructed under the illumination of light with specific wavelengths(λ=450,635 nm)and topological charges(l=-2,0,2),without high-order *** work suggests that ultrathin meta-holograms can potentially realize ultrahigh-bandwidth full-color holography and holographic video displays with large FOV.

关键词： Metasurface Meta-holography Wavelength-multiplexed holography OAM holography

来源：评论

学校读者我要写书评

暂无评论

General Model for Manufacturing Defect Detection Crossing Multiple Products 22

General Model for Manufacturing Defect Detection Crossing Mu...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhou, Longfei Qin, Zhanghao Xia, Ping Liu, Jiabao Cheng, Weihao Li, Yunwei Ying, Leyi Liu, Qian Liu, Mengxin Song, Gezhang Huang, Zebo Hao, Zhou Gannon University Biomedical Industrial and Systems Engineering Department EriePA United States School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China School of Atmospheric Science Nanjing University of Information Science and Technology NanJing China School of Computer Science and Artificial Intelligence Suzhou City College Suzhou China School of Physics and Optoelectronic Engineering Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Communication Engineering Jiangsu University ZhenJiang China School of Mechanical Engineering Jiangnan University Wuxi China College of Artificial Intelligence and Computer Science Jiangnan University Wuxi China School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9798350345346

As one of the most important aspects of artificial intelligence, computer vision technologies enable intelligent systems to comprehend the real world through imaging. Despite this, there are still opportunities to enhance the development of machine vision technologies in the manufacturing industry. Many industrial systems still apply manual methods, particularly in small to medium-sized enterprises. Existing machine learning models also struggle with diverse datasets because models were mostly trained for particular datasets. Hence, creating a rational defect detection system that can be used across different imaging datasets is a crucial task for efficiency, precision, and labor relief. We present a novel machine learning model for defect detection of industrial products, fusing classification and segmentation, and crossing multiple datasets. A novel framework is established by combining classification and segmentation modules for multiple datasets. The classification uses a DenseNet model for distinct data categories, followed by the segmentation part. The developed model can benefit the industry by enhancing inspection accuracy and efficiency for multiple products. © 2023 IEEE.

关键词： Artificial intelligence computer vision Defect detection Industrial products Machine learning

来源：评论

学校读者我要写书评

暂无评论

Analysis and Design of Vector Holographic Metasurfaces

Analysis and Design of Vector Holographic Metasurfaces

引用

2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Pitilakis, A. Tsilipakos, O. Tasolamprou, A.C. Tsioliaridou, A. Kantartzis, N.V. Ioannidis, S. Liaskos, C. Kafesaki, M. Institute of Electronic Structure and Laser Foundation for Research and Technology Hellas Greece Aristotle University of Thessaloniki School of Electrical and Computer Engineering Greece Theoretical and Physical Chemistry Institute National Hellenic Research Foundation Greece National and Kapodistrian University of Athens Department of Physics Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece Technical University of Crete School of Electrical & Computer Engineering Greece University of Ioannina Department of Computer Science and Engineering Greece University of Crete Department of Materials Science and Technology Greece

ISBN: (纸本)9798350375909

In this work we present a comprehensive framework for the analysis and design of vector holographic metasurfaces (VHMS). The term vector refers to the metasurface ability to manipulate the polarization of the scattered wave, while holographic refers to the configuration of the metasurface aperture, which can have any arbitrary complex profile in its equivalent surface impedance (phase and/or magnitude). We start from the presentation of the analysis of such VHMS, i.e., the calculation of its scattering under given equivalent surface impedance configuration and illumination;the analysis relies on physical optics and can, in theory, model the scattering and diffraction by a finite-aperture metasurface of arbitrary convex shape under arbitrary spatially coherent wavefront illumination. We then present the VHMS design and synthesis procedure, i.e., we compute its equivalent and physical configuration, when given the illuminating wavefront and the desired scattering response or functionality, e.g., beam steering or focusing. For the synthesis, we use a simple scalable unit cell topology incorporating square conductor patches interconnected with lumped complex loads;these can locally and continuously tune the VHMS phase and amplitude response, with independent control over both polarizations, thus allowing arbitrary incident-to-scattered wavefront transformations. These versatile devices, especially when fused with wavefront sensing capabilities, can be used in applications spanning wireless communications (e.g., beamforming, integrated/joint sensing and communication, or programmable wireless environments), radar scattering (e.g., stealth), and holography/imaging. © 2024 IEEE.

关键词： Wavefronts

来源：评论

学校读者我要写书评

暂无评论

A Quality Diversity Study in EvoDevo Processes for engineering Design 13

A Quality Diversity Study in EvoDevo Processes for Engineeri...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Buchanan, Edgar Hickinbotham, Simon Dubey, Rahul Friel, Imelda Colligan, Andrew Price, Mark Tyrrell, Andy M. School of Physics Engineering and Technology University of York Intelligent Systems & Robotics Research Group United Kingdom Missouri State University Computer Science United States School of Mechanical & Aerospace Engineering Queen's University Belfast United Kingdom

ISBN: (纸本)9798350308365

For a long time engineering design has relied on human engineers manually crafting and refining designs using their expertise and experience. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are employed to investigate a broader design space that may go beyond what human engineers have considered. Previous literature has demonstrated the use of quality and diversity (QD) algorithms in evolutionary approaches to drive the process to better quality solutions. This paper provides a study to understand the effects of using QD algorithms in EvoDevo processes for engineering design. This paper also analyses the impact of using different behavioural characterisations (BC) in the performance of the quality of the solutions found. The results demonstrate that quality and diversity algorithms can find better solutions than other EAs for engineering design problems. It was also found that the characterisation of the BC is important to get the best results. © 2024 IEEE.

关键词： evodevo generative design neural networks quality diversity structural engineering

来源：评论

学校读者我要写书评

暂无评论

DNMDA: Deep Non-negative Matrix Factorization with Multi-level Integration for MiRNA-Drug Interaction Prediction

DNMDA: Deep Non-negative Matrix Factorization with Multi-lev...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qi, Yujie Yang, Jiaxin Huang, Yu-An You, Zhu-Hong Zhang, Zimai Hu, Lun Zhou, Xi Hu, Pengwei Xinjiang University The School of Software Xinjiang Technical Institute of Physics Chemistry Chinese Academy of Sciences Urumqi China Columbia University School of Engineering and Applied Science NY United States Northwestern Polytechnical University School of Computer Science Xian China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China

ISBN: (纸本)9798350386226

Numerous studies have demonstrated that the interaction between miRNAs and drugs plays a pivotal role in regulating gene expression and cellular function. Therefore, predicting these interactions is crucial for the development of novel drugs and personalized therapies. Existing methods for predicting miRNA-drug interactions often fail to leverage the full spectrum of molecular and biological features and overlook complex high-dimensional patterns. Deep non-negative matrix factorization (DNMF) addresses these limitations by extracting higher-level representations, thereby enhancing prediction accuracy and robustness. Building on this, this paper proposes a model called DNMDA. In this model, we integrate multiple similarity networks for both miRNAs and drugs and then extract their features through three key modules. Moreover, autoencoders are used to combine various feature sets, allowing for the capture of complementary information and enhancing the model's capacity for making precise and reliable predictions. The resulting features are consolidated into a unified feature vector for each miRNA-drug pair. Ultimately, these feature vectors and their associated labels are provided to the classifier for training. To verify the predictions, a five-fold cross-validation was conducted. The five-fold cross-validation demonstrated a clear advantage in DNMDA's metrics, underscoring its reliability in predicting potential miRNA-drug interactions. This claim is further supported by the predictive results section in the paper, offering concrete evidence of DNMDA's efficacy in this field. © 2024 IEEE.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Sparse Recovery Space Time Adaptive Processing Based on Log-Determinant 15

Sparse Recovery Space Time Adaptive Processing Based on Log-...

引用

15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Chang, Yuqing Du, Xiaolin Jin, Biao Tian, Tuanwei Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China Ocean College Jiangsu University of Science and Technology Zhenjiang China School of Physics and Electronics Henan University Kaifeng China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510675056

Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse rocovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms. © 2024 SPIE.

关键词： Space time adaptive processing

来源：评论

学校读者我要写书评

暂无评论

The Immense Impact of Reverse Edges on Large Hierarchical Networks

引用

engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

来源：评论

学校读者我要写书评

暂无评论

Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

引用

Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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：