检索结果-内蒙古大学图书馆

Enhanced fluorescence sensing of tetracycline with Ti_(2)C quantum dots

Nano Research 2024年第4期17卷 3180-3188页

作者： Dejia Hu ShangZuo Jiang Tianhao Xia Danyang Xiao Yan Li Yanbing Hou Jin Zhong Zhang Ying-Chih Pu School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China Key Lab Luminescence&Optical Information Ministry EducationBeijing JiaoTong UniversityBeijing 100044China Department of Chemistry and Biochemistry University of CaliforniaSanta CruzCalifornia 95064USA Department of Materials Science "National University of Tainan"Tainan 700301TaiwanChina

Ti_(2)C quantum dots(QDs)with rich surface functional groups have been synthesized using a hydrothermal method,and used to detect tetracycline(Tc)based on enhanced *** interaction between the surface functional groups of Ti_(2)C QDs and Tc enhanced the fluorescence of Tc at 514 nm,which is used to detect Tc quickly and *** optimal conditions,the fluorescence intensity was linear to the concentration of Tc in the range of 50.0–30.0μM,with a detection limit of 21.6 ***,the Tc-Ti_(2)C QDs detection system was evaluated for detection of Tc in milk and artificial *** study demonstrates a new and simple strategy for Tc detection,which is important for food safety and human health.

关键词： Ti_(2)C MXene quantum dots tetracycline fluorescence enhancement detection

来源：评论

学校读者我要写书评

暂无评论

High-Resolution Wideband Dual Electro-Optic Comb Spectroscopy Enabled by Tunable Optical Source

High-Resolution Wideband Dual Electro-Optic Comb Spectroscop...

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： He, Huan Wei, Qingyu Zhang, Mingming Zhao, Zhiyong Fu, Songnian Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Photonics Information Technology School of Information Engineering Guangdong University of Technology Guangzhou510006 China

A high-resolution dual electro-optic comb spectroscopy working in quasi-integer-ratio mode is proposed. By stepwise tuning the light source wavelength, a spectral measurement with 120-kHz resolution and 24-GHz bandwid... 详细信息

ISBN: (纸本)9781957171258

关键词： Light sources

来源：评论

学校读者我要写书评

暂无评论

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

引用

Computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Developing Engagement in the Learning Management system Supported by Learning Analytics

引用

Computer systems science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

来源：评论

学校读者我要写书评

暂无评论

VMAP: Matching-based Efficient Offloading in IoT-Fog Environments with Variable Resources 17

VMAP: Matching-based Efficient Offloading in IoT-Fog Environ...

引用

17th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： Morey, Jui Satpathy, Anurag Addya, Sourav Kanti Cloud and Smart System Services Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Missouri University of Science and Technology United States

ISBN: (纸本)9798350307672

Fog computing is a promising technology for critical, resource-intensive, and time-sensitive applications. In this regard, a significant challenge is generating an offloading solution that minimizes the latency, energy, and number of outages for a dense IoT-Fog environment. However, the existing solutions either focus on a single objective or mainly dedicate fixed-sized resources as virtual resource units (VRUs). Moreover, these solutions are restrictive and not comprehensive, resulting in poor performance. To overcome these challenges, this paper proposes a VMAP model addressing the lacunas above. Offloading problem is abstracted as a one-to-many matching game between two sets of entities - tasks and fog nodes (FNs) by considering both preferences. Moreover, the preferences and weights of the parameters are generated using the Analytic Hierarchy Process (AHP). Exhaustive simulations indicate that the proposed strategy outperforms the baseline algorithms, considering average task latency and energy consumption by 35% and 22.2%, respectively. Additionally, resource utilization also experiences a boost by 28.57%, and 97.98% of tasks complete their execution within the deadlines. © 2023 IEEE.

关键词： Fog

来源：评论

学校读者我要写书评

暂无评论

Adversarial Recovery Network for Low-Light Person Re-Identification 23

Adversarial Recovery Network for Low-Light Person Re-Identif...

引用

6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Liu Wang, Xiao Yuan, Xin Mao, Zhu Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology ChinaHubei Prov. Key Lab. of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)9781450398572

Person re-identification (re-ID), which is to connect pedestrians with the same identity in different scenarios, has been more widely used in case detection of security. The re-ID datasets and technology have contributed greatly to advancing community research. There are currently no available low-light datasets due to the difficulty of collecting and labeling images of people in low-light scenes. As a result, the vast majority of current re-ID methods do not have the recognition capability in low-light scenes. However, the recognition of persons in the low-light surveillance video has become an important issue to be solved for urban security, which plays an important role in improving the crime-solving rate of public safety departments and protecting people's lives and property. In this paper, we propose an Adversarial Recovery Network (ARN) that, unlike other approaches, enables a re-ID model with low-light recognition capability without the need for low-light data. Specifically, we first degrade the original images to generate multi-level low-light images;then obtain recovered images through the adversarial recovery module, and finally input the recovered images into the CNN network to extract pedestrian recognition knowledge. In addition, to validate the effectiveness of the proposed method, we also propose a low-light re-ID dataset (LOLR). Extensive experiments on LOLR have shown that our method outperforms other competitors by a wide margin. The dataset and code are available online at https://***/xl02111/ARN. © 2023 ACM.

关键词： Recovery

来源：评论

学校读者我要写书评

暂无评论

Traffic sign recognition based on deformable convolutions 8

Traffic sign recognition based on deformable convolutions

引用

8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Li, Wenju Yu, Jie Liye, Sha Liu, Cui Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Precision Dosing & Weighing System Co Shanghai China

ISBN: (纸本)9798350369564

A traffic sign recognition model based on deformable convolution is proposed to address the low accuracy of existing traffic sign recognition algorithms for small and occluded targets. Firstly, replace some convolutions in the backbone network with deformable convolutions to adapt to different shape feature maps during feature extraction and improve the network's sensitivity to small targets;Secondly, in order to reduce the loss of information in the process of upsampling, the original Nearest-neighbor interpolation upsampling module is replaced by the sub-pixel convolution module in the feature fusion network to reduce the loss of information due to upsampling. Finally, design experiments on the TT100K dataset and test the advantages of this model in real-world scenarios. The experimental results show that the average accuracy of this model (mAP@0.5) It has reached 92.54%, with a Recall rate of 87.42%, which is 2.18% and 1.58% higher than the benchmark network YOLOv5n, respectively. The frame processing speed has reached 96.06. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Secure Parameter Self-Tuning Control Scheme for Vehicle Platoons under DoS Attacks 13

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

引用

13th IEEE International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Han, Qiaoni Cheng, Guowen Tang, Zhenning Liu, Chang School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China

ISBN: (纸本)9798350315196

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security. © 2023 IEEE.

关键词： Fuzzy control

来源：评论

学校读者我要写书评

暂无评论

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

引用

High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

来源：评论

学校读者我要写书评

暂无评论

Real-Time FPGA Implementation of CNN-Based Distributed Fiber Optic Vibration Event Recognition Method

Real-Time FPGA Implementation of CNN-Based Distributed Fiber...

引用

2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Luo, Zhongyao Ge, Zhao Wu, Hao Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350312614

utilizing optical fibers to detect and pinpoint vibrations, Distributed Optical Fiber Vibration Sensing (DVS) technology provides real-time monitoring and surveillance of wide-reaching areas. This field has been leveraging Convolutional Neural Networks (CNN). Recently, a study has accomplished end-to-end vibration event recognition, enabling utilization of CNN-based DVS algorithms as real-time embedded system for edge computing in practical application situations. Considering the power consumption of central processing unit (CPU) and graphics processing unit (GPU), and the inflexibility of application-specific integrated circuit (ASIC), field-Programmable gate array (FPGA) is the optimal computing platform for the system. This paper proposes to compress pre-trained network and adopt a novel hardware structure, to design a fully on-chip, pipelined inference accelerator for CNN-based DVS algorithm, without fine tuning or re-training. This design allows for real-time processing with low power consumption and system requirement. An examination has been executed on an existing DVS algorithm based on a 40-layer CNN model comprising 2.7 million parameters. It is completely implemented on-chip, pipelined, with no reduction in accuracy. © 2023 IEEE.

关键词： convolution neural networks distributed vibration sensing FPGA pruning quantization

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：