检索结果-内蒙古大学图书馆

Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

来源：评论

学校读者我要写书评

暂无评论

Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

来源：评论

学校读者我要写书评

暂无评论

Investigation of Feedback Level Limiter Algorithm to Anticipate the Deep Fade Conditions on Closed Loop Rotate Modulations for Multicarrier Systems 7

Investigation of Feedback Level Limiter Algorithm to Anticip...

引用

7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Prasetya, Budi Rohmah, Yuyun Siti Dziyauddin, Rudzidatul Akmam School of Electrical Engineering Telkom University Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (纸本)9798350392074

This closed loop rotate modulation goal is to improve the multicarrier systems performance in slow fading channels, by utilizing complex channel state information feedback. The rotate modulation process uses the precoding principles applied to multicarrier systems with the Inverse Discrete Fourier Transform (IDFT) and Discrete Fourier Transform (DFT). If the deep fade channel is an input for the rotate modulation process that is inversely proportional to the channel response of each subcarrier, it will produce a signal with a large power. To limit the peak power of the system output, we propose the level limiter algorithm for feedback channels to anticipate deep fade conditions. The simulation results show the addition of a limiter can reduce the peak of the output power of the multicarrier modulator, which means the peak-to-average power can be reduced. However, it has a consequence that the bigger the limiter will result in an increased probability error, so we propose the optimal limiter level is 0.01 volt. The probability of error in low-level modulation like Binary Phase Keying shows good results even though the limiter's level process is added. At high-level modulation like 16-quadrature Amplitude Modulation, the greater level limiter causes the probability of error to worsen. © 2024 IEEE.

关键词： Quadrature amplitude modulation

来源：评论

学校读者我要写书评

暂无评论

An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

引用

Computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

来源：评论

学校读者我要写书评

暂无评论

Polyvinyl chloride-reduced graphene oxide based chemiresistive sensor for sensitive detection of ammonia

引用

Journal of Materials science: Materials in Electronics 2024年第3期35卷 1-18页

作者： Gaikwad, Sumita S. More, Mayuri S. Khune, Abhaysinh S. Mohammed, Hamed Y. Tsai, Meng-Lin Hianik, Tibor Shirsat, Mahendra D. RUSA Centre for Advanced Sensor Technology Department of Physics Dr Babasaheb Ambedkar Marathwada University MS Aurangabad431 004 India Department of Materials Science and Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Nuclear Physics and Biophysics Faculty of Mathematics Physics and Informatics Comenius University Bratislava842 48 Slovakia

In this study, we present the innovative development of sensitive chemiresistive sensor utilizing a composite material composed of polyvinyl chloride (PVC) and reduced graphene oxide (rGO) for the highly responsive detection of ammonia (NH3) at ambient conditions. Graphene oxide (GO) was meticulously synthesized employing an enhanced version of Hummer’s method, and subsequently, rGO was prepared via thermal reduction from GO. The PVC/rGO composite was synthesized using a precise chemical pathway. The synthesized material was comprehensively characterized using advanced techniques including X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), UV–visible spectroscopy, Raman spectroscopy, atomic force microscopy (AFM), and I–V measurements, unveiling its structural, spectroscopic, morphological, and electrical attributes. The chemiresistive sensor was fabricated through the meticulous drop-casting of PVC/rGO composite onto an economical Cu interdigitated electrode (IDE) pattern. Remarkably, this PVC/rGO-based chemiresistive sensor exhibited superior performance in detecting corrosive NH3. The sensor demonstrated an exceptional response time of 46 s and a recovery time of 88 s, coupled with an impressively low detection limit of 1 ppm, surpassing the Occupational Safety and Health Administration (OSHA) permissible exposure limit (PEL). Notably, this sensor displayed a remarkable selectivity towards NH3 at ambient conditions, outperforming its response to other gases. Furthermore, the sensor exhibited outstanding reproducibility, repeatability, linearity, sensitivity, and stability in its response to NH3. This research showcases a groundbreaking advancement in the field of gas sensing technology, offering a highly sensitive and selective chemiresistive sensor for NH3 detection at room temperature (RT). The synergistic combination of PVC and rGO in the composite material, along with the precise fabrication techniques employed, has resulted in a sensor with u

关键词： Ammonia

来源：评论

学校读者我要写书评

暂无评论

TransRAGfor parallel transportation:toward reliable and trustworthy transportation systems via retrieval-augmented generation

引用

Frontiers of Information technology & Electronic engineering 2025年第1期26卷 20-26页

作者： Jing YANG Xingyuan DAI Yisheng LV Levente KOVáCS Fei-Yue WANG Institute of Automation Chinese Academy of SciencesBeijing 100190China John von Neumann Faculty of Informatics Obuda UniversityBudapest H-1034Hungary Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

Parallel transportation serves as a holistic paradigm for achieving intelligent traffic management and control,focusing on addressing the complexity of human and social ***,the emergence and development of foundational models(FMs)have ushered in a new era for the realization of parallel ***,the inherent issues of“hallucinations,”outdated knowledge,and the“black-box”nature of FMs render their generated decisions unreliable and *** address these issues,we propose a TransRAG framework for parallel transportation based on retrieval-augmented generation and chain-of-thought(CoT)*** is composed of three interacting layers,storage,management,and execution,which work together to deliver personalized and diverse traffic services to *** external knowledge from the storage layer is incorporated to augment the FM in management layers for computational experiments.

关键词： prompt services transportation

来源：评论

学校读者我要写书评

暂无评论

Shortest Beer Path Queries Based on Graph Decomposition 34

Shortest Beer Path Queries Based on Graph Decomposition

引用

34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Hanaka, Tesshu Ono, Hirotaka Sadakane, Kunihiko Sugiyama, Kosuke Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Graduate School of Informatics Nagoya University Japan Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9783959772891

Given a directed edge-weighted graph G = (V, E) with beer vertices B ⊆ V , a beer path between two vertices u and v is a path between u and v that visits at least one beer vertex in B, and the beer distance between two vertices is the shortest length of beer paths. We consider indexing problems on beer paths, that is, a graph is given a priori, and we construct some data structures (called indexes) for the graph. Then later, we are given two vertices, and we find the beer distance or beer path between them using the data structure. For such a scheme, efficient algorithms using indexes for the beer distance and beer path queries have been proposed for outerplanar graphs and interval graphs. For example, Bacic et al. (2021) present indexes with size O(n) for outerplanar graphs and an algorithm using them that answers the beer distance between given two vertices in O(α(n)) time, where α(·) is the inverse Ackermann function;the performance is shown to be optimal. This paper proposes indexing data structures and algorithms for beer path queries on general graphs based on two types of graph decomposition: the tree decomposition and the triconnected component decomposition. We propose indexes with size O(m + nr2) based on the triconnected component decomposition, where r is the size of the largest triconnected component. For a given query u, v ∈ V , our algorithm using the indexes can output the beer distance in query time O(α(m)). In particular, our indexing data structures and algorithms achieve the optimal performance (the space and the query time) for series-parallel graphs, which is a wider class of outerplanar graphs. © Tesshu Hanaka, Hirotaka Ono, Kunihiko Sadakane, and Kosuke Sugiyama;licensed under Creative Commons License CC-BY 4.0.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Vision-Based PM2.5 Concentration Estimation with Natural Scene Statistical Analysis

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2805-2815页

作者： Wang, Guangcheng Shi, Quan Wang, Han Gu, Ke Wei, Mengting Wong, Lai Kuan Wang, Mingxing Nantong University School of Transportation and Civil Engineering Nantong226019 China Beijing University of Technology Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Artificial Intelligence Institute Beijing100124 China Chinese Academy of Sciences Institute of Software Beijing100045 China Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Environmental Science and Technology Company Ltd. Beijing102100 China

As the primary pollutant in China's urban atmosphere, particulate matter (PM)2.5 poses a great threat to the health of residents and ecological stability. An efficient and effective PM2.5 concentration monitoring is essential. Nonetheless, the popular devices for PM2.5 monitoring are developed based on two standards: the microoscillation balance method and the β-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM2.5 concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the 'haze' formed by PM2.5. Moreover, we develop a lightweight deep belief network-deep neural network-based PM2.5 concentration estimation model, which learns the mapping from the designed visual priors to PM2.5 concentrations. Experiments confirm the superiority of our vision-based PM2.5 concentration estimation method by comparison with state-of-the-art photo-based PM2.5 monitoring methods. © 2020 IEEE.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Analysis of Virtual Reality Based on the Internet of Things on Human Psychology ‘Internet of Thoughts’ (IoThs) for Rich Content Extraction Applied Natural Language Processing and Deep Learning 20th

Analysis of Virtual Reality Based on the Internet of Things ...

引用

20th International Conference on Smart Living and Public Health, ICOST 2023

作者： Mah, Pascal Muam Skalna, Iwona Pelech-Pilichowski, Tomasz Derlecki, Tomasz Nasr, Mahmoud Munyeshuri, Eric Amuzang, Gilly Njoh Itoe, Micheal Blake Somaah Tah, Ning Frida Department of Technical Informatics and Telecommunications AGH University of Science and Technology Krakow Poland Department of Business Informatics and Management Engineering AGH University of Science and Technology Krakow Poland Institute of Computer Science AGH University of Science and Technology Krakow Poland Department of Economics and Management Industry AGH University of Science and Technology Krakow Poland Department of Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Engineering Mathematics and Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Production Engineering and Logistics Enterprise Silesian University of Technology Gliwice Poland Department of Logistics Management Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031439490

In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think "Internet of thoughts" reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology "Internet of Thoughts". © 2023, The Author(s).

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

引用

Computer Modeling in engineering & sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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：