检索结果-内蒙古大学图书馆

Nanophotonic catalytic combustion enlightens mid-infrared light source

Nano Research 2023年第9期16卷 11564-11570页

作者： Zhenhua Wu Zhimao Wu Haoran Lv Wenbin Zhang Zekun Liu Shuai Zhang Erzhen Mu Hengxin Lin Qing Zhang Daxiang Cui Thomas Thundat Zhiyu Hu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Micro/Nano Electronics Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute of NanoMicroEnergy School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China School of Physics University of Electronic Science and Technology of ChinaChengdu 611731China School of Materials Science and Engineering Henan Polytechnic UniversityJiaozuo 454000China Department of Instrument Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Chemical and Biological Engineering University at Buffalothe State University of New YorkNew York 14260USA

The tunable mid-infrared source in a broad-spectrum heralds great scientific implications and remains a *** catalytic combustion facilitates access to customizable infrared light ***,we report on fabricating platinumalumina bilayer nano-cylinder arrays for methanol catalytic combustion,which enables them to act as an array of infrared point light sources,with wavelength tunable by controlling the flow rate of methanol/air *** then propose a technique of integrating nanophotonic structures with catalytic combustion to engineer infrared light *** demonstrate a prototype of a topological photonic crystal catalyst array in which infrared emission can be enhanced significantly with highly vertical *** work establishes a framework of nanophotonic catalytic combustion for infrared light sources.

关键词： nanophotonics catalytic combustion nano-array catalyst infrared light source

来源：评论

学校读者我要写书评

暂无评论

Enhancing Agricultural Productivity: A Sensor Fusion, Spectroscopic Near Infrared, and AI-Based Framework for Monitoring Fruit Ripeness 21

Enhancing Agricultural Productivity: A Sensor Fusion, Spectr...

引用

21st International Bhurban Conference on Applied sciences and technology, IBCAST 2024

作者： Ambreen, Hira Imran, Muhammad Diyin, Tang Zhang, Chongsheng Sir Syed CASE Institute of Technology Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China Henan Univerity Department of EE Kaifeng475001 China School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics Department of Detection and Automation Engineering China School of Computer & Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9798331516680

Fruit ripeness evaluation is essential for harvest optimization and post-harvest loss prevention. Traditional visual inspection is inefficient. This study employs sensor fusion and AI algorithms to evaluate fruit maturity in real-time using gas sensors (i.e., MQ-3, MQ-6, MQ-8, MQ-135) and Near-Infrared spectral sensors (i.e., GY-AS7263). Primarily, data pro-cessing-sensor fusion, data preparation, and AI model application-is crucial to the proposed framework. Subsequently, sensor fusion approaches improve data accuracy by combining signals from various sensors. Secondly, data pretreatment techniques including noise reduction, normalization, and feature extraction prepare data for analysis. Thirdly, we use powerful machine learning algorithms such as Random Forest to judge maturity more accurately and reliably than conventional techniques. Lastly, integrating NIR spectroscopy with gas sensor data resolves cost and model transferability issues, enhancing accuracy and acces-sibility for agricultural use. Static image processing techniques provide challenges for real-time applications, including expensive computer costs and lower reliability. The proposed framework based on multi-sensor fusion system enhances agricultural applications' versatility, scalability, and reactivity, surpassing existing methodologies. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

A hybrid attention deep learning network for refined segmentation of cracks from shield tunnel lining images

引用

Journal of Rock Mechanics and Geotechnical Engineering 2023年第12期15卷 3105-3117页

作者： Shuai Zhao Guokai Zhang Dongming Zhang Daoyuan Tan Hongwei Huang Department of Civil and Environmental Engineering The Hong Kong Polytechnic University999077Hong KongChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji UniversityShanghai200092China

This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.

关键词： Crack segmentation Crack disjoint problem U-net Channel attention Position attention

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Intranet Abnormal Traffic Security Detection System

Design and Implementation of Intranet Abnormal Traffic Secur...

引用

Mechatronics, IoT and Industrial Informatics (ICMIII), International Conference on

作者： Aoran Zhao School of Computer Science and Technology Tianjin Polytechnic University Tianjin China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

Intranet is the fundamental information network for various systems and units to conduct business applications, with the advantages of open system architecture and flexible information sharing. This paper analyzes the characteristics of intranet traffic and abnormal network traffic, discusses the abnormal traffic detection method and algorithm based on traffic whitelist, and designs a security detection system for abnormal network traffic to locate malicious attacks and abnormal traffic generated by sudden access. The detection system helps network administrators to timely detect problems, take targeted measures, mitigate the harm of abnormal traffic to the network and its business, and provide support for network maintenance personnel in troubleshooting.

关键词： Accesslists Mechatronics Information sharing Telecommunication traffic Open systems Maintenance Security

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method 19th

A Novel Deep Federated Learning-Based and Profit-Driven Ser...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Ouyang, Zhaobin Xia, Yunni Peng, Qinglan Li, Yin Chen, Peng Wang, Xu College of Computer Science Chongqing University Chongqing China School of Artificial Intelligence Henan University Zhengzhou China Guangzhou Institute of Software Application Technology Guangzhou China School of Computer and Software Engineering Xihua University Chengdu China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

ISBN: (纸本)9783031545306

Service caching is an emerging solution to addressing massive service request in a distributed environment for supporting rapidly growing services and applications. With the explosive increases in global mobile data traffic, service caching over the edge computing architecture, Mobile edge computing (MEC), emerges for alleviating traffic congestion as well as for optimizing the efficiency of task processing. In this manuscript, we propose a novel profit-driven service caching method based on a federated learning model for service prediction and a deep reinforcement learning mode for yielding caching decisions (FPDRD) in an edge environment. The proposed method is temporal service popularity and user preference-aware. It aims to ensure quality of service (QoS) of delivery of cached service while maximizing the profits of network service providers. Experimental results clearly demonstrate that the FPDRD method outperforms traditional methods in multiple aspects. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Monte Carlo study on polarization PSF for underwater imaging enhancement 8

Monte Carlo study on polarization PSF for underwater imaging...

引用

8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Zhang, Yunyao Zhang, Xingguang Tian, Heng Dai, Yang School of Information Science and Technology Northwest University Xi'an China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Underwater imaging enhancement is challenging because the significant light attenuation along the propagation direction by the turbid water. Solving the problems caused by scattering is the core issue of improving underwater imaging. In recent years, generating a specific point spread function (PSF) has been one of the cornerstones of modern microscopy for turbid tissue, which showed that PSF still has potential for underwater imaging enhancement. As polarization imaging technology showed great potential to improve the underwater imaging, we propose to study the polarization point spread function (pPSF) of the turbid media. We used Monte Carlo method for studying optical transmission. We used polystyrene microspheres of two different diameters to be the scattering particles in the water. We studied PSF in forms of S0, S1, S2, S3, parallelly gated light and vertical gated light. The results showed that S3 has similar pattern with S0 and could be implied in 1D along polar direction. S1 was 90°rotationally symmetrical and should be studied in 2D. The pPSF of S1 changed from x direction to diagonal direction continuously along the azimuth with different speed varied with transport distance. Further study on these characteristics is of great potential for us to improve the underwater polarization imaging. © 2022 SPIE

关键词： Optical transfer function

来源：评论

学校读者我要写书评

暂无评论

5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

引用

computers, Materials & Continua 2025年第1期82卷 881-911页

作者： Sarvenaz Sadat Khatami Mehrdad Shoeibi Anita Ershadi Oskouei Diego Martín Maral Keramat Dashliboroun Department of Data Science Engineering University of HoustonHoustonTX 77204USA The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609USA School of Systems and Enterprises Stevens Institute of TechnologyHobokenNJ 07030USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Ernest G.Welch School of Art&Design Georgia State UniversityAtlantaGA 30303USA

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and

关键词： Internet of things intrusion detection generative adversarial networks five-dimensional binary gray wolf optimizer deep learning

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Cybersecurity: Enhancing Threat Detection and Defense in the Digital Age 7

AI-Powered Cybersecurity: Enhancing Threat Detection and Def...

引用

7th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2024

作者： Zhang, Xianni Wang, Pengfei Jia, Haonan Huang, Zhen Zhao, Runfeng College of Information Engineering Beijing Polytechnic College Department of New Generation Information Technology Beijing100042 China Beijing University of Technology School of Mechanical and Energy Engineering Beijing100000 China Huizhou University School of Computer Science and Engineering 516007 China

ISBN: (纸本)9798350384437

Information technology's swift progression has rendered network security a critical component of modern infrastructure. Artificial Intelligence (AI) has revolutionized network security, particularly in threat detection and defense mechanisms. Leveraging its advanced data processing and pattern recognition capabilities, AI can forecast and counter potential network threats with enhanced precision and efficiency. The burgeoning global AI market reflects its expansive potential in bolstering network security. By emulating the neural networks of the human brain, AI bolsters threat detection rates, dynamically refines security strategies, and enables automated response and recovery from cyber-attacks. Studies suggest that AI's integration can markedly diminish the risk of network breaches and fortify the reliability of security *** the cyber landscape grows more intricate and threats evolve, ongoing innovation in AI is imperative for future advancements. The anticipation is that AI will assume an increasingly pivotal role in network security, offering robust defense against the escalating complexity of cyber threats. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

引用

CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

来源：评论

学校读者我要写书评

暂无评论

OrthoXIC: A Fusion Approach for Multi-Implant Classification using X-ray Images

引用

Procedia computer science 2025年 258卷 2455-2468页

作者： Priyanka M Lakshmi J C Vyshnavi N B Suresha R Tian Jipeng Department of Computer Science School of Computing Mysuru Campus Amrita Vishwa Vidyapeetham India Computer Science college Zhongyuan University of Technology Zhengzhou Henan Province China

Orthopaedic surgical implants are vital for treating musculoskeletal disorders, necessitating precise implant identification in x-ray images to ensure accurate evaluation and management. Most of the studies focus on specific joint implants due to a lack of data availability and existing generic models suffer from the challenges of high similarity within and between different types of implants. To address these problems, a novel OrthoXIC method that automatically classifies orthopaedic multiple implants is proposed. To develop a generic multi-implant classification model, 546 x-rays with implants consisting of needles, screws, and plates of the elbow and wrist joints were collected. The canny edge detection and binary thresholding are applied to extract the specific implant region. The fusion mechanism extracts appropriate texture and deep features from modified HOG and VGG16, respectively. The Random Forest improves model efficiency by addressing the inter- and intra-class variation in the concatenated features and achieves a better result with 95% accuracy, 94% precision, 93% recall, and 93% f1-score for the collected dataset. The proposed model shows better generalization compared to state-of-the-art methods.

关键词： OrthoXIC HOG VGG16 Random Forest Multi-Implant Classification

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：