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Research on Space Occupation Recognition Method of Fuel Vehicles in Charging Station Based on License Plate Recognition Algorithm 3

Research on Space Occupation Recognition Method of Fuel Vehi...

引用

3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Chi, Lei Xie, Han Wang, Shouqin Cheng, Chaoyi Yuan, Xiujuan Zhang, Yajing State Grid Chongqing Electric Power Company Chongqing400015 China Beijing Kedong Electric Power Control System Co. Ltd. Beijing100192 China Pamela Branch of Chongqing Guanghui Power Supply Service Co. Ltd. Chongqing400014 China

ISBN: (纸本)9781510657281

With the rapid development of new energy vehicles, problems such as "charging difficulty" are becoming increasingly prominent. The charging space is occupied by fuel vehicles, resulting in the inability of new energy vehicles to charge after they arrive at the charging station. This paper proposes a license plate recognition algorithm model which combines the three steps of vehicle detection, license plate detection, and license plate recognition, which improves the robustness of license plate recognition in complex scenes. In view of the lack of Chinese license plate data, the data enhancement method is adopted to improve the recognition accuracy of Chinese provincial abbreviations and common color license plates such as blue, green, and yellow. According to the characteristics of a small license plate target and easy rotation in the license plate recognition task, the WPOD-NET network is selected for license plate detection. Finally, the license plates of new energy vehicles and fuel vehicles are distinguished by recognizing the color and law of license plates, so as to provide supporting information for the generation of fuel vehicle occupancy alarm. In this paper, the data collected by the cameras of some charging stations in a city are used for verification. The verification results show that the accuracy of oil vehicle occupancy recognition is as high as 97%. © 2022 SPIE.

关键词： Optical character recognition

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Structured Outdoor Architecture Reconstruction by Exploration and Classification 18

Structured Outdoor Architecture Reconstruction by Exploratio...

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18th IEEE/CVF international conference on computer vision (ICCV)

作者： Zhang, Fuyang Xu, Xiang Nauata, Nelson Furukawa, Yasutaka Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665428125

This paper presents an explore-and-classify framework for structured architectural reconstruction from an aerial image. Starting from a potentially imperfect building reconstruction by an existing algorithm, our approach 1) explores the space of building models by modifying the reconstruction via heuristic actions;2) learns to classify the correctness of building models while generating classification labels based on the ground-truth;and 3) repeat. At test time, we iterate exploration and classification, seeking for a result with the best classification score. We evaluate the approach using initial reconstructions by two baselines and two state-of-the-art reconstruction algorithms. Qualitative and quantitative evaluations demonstrate that our approach consistently improves the reconstruction quality from every initial reconstruction.

关键词： Quality control

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Rice Leaf Disease Detection Using Deep Learning Models: A Comprehensive Approach

Rice Leaf Disease Detection Using Deep Learning Models: A Co...

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Disruptive Technologies (ICDT), international conference on

作者： Gorli Ramya Valli Kumari Vatsavayi Department Of Computer Science And Systems Engineering Andhra University College of engineering Visakhapatnam India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Rice serves as a primary food source for more than half of the global population. However, a range of diseases brought on by pathogens, including as bacteria, viruses and fungus are posing a growing threat to rice agriculture. These diseases have the potential to seriously reduce crop productivity and threaten food security. Conventional disease detection techniques mostly depend on the labor-intensive, time consuming and frequently prone to human error manual inspection performed by qualified agronomists. We concentrate on two cutting edge architectures: vision Transformers (ViT) and DenseNet169, which is well-known for its high accuracy in image classification applications and efficiency in feature reuse which make use of self-attention mechanisms to identify complex patterns in images. We developed these models to precisely categorize various rice leaf diseases, including Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Narrow Brown Spot, Neck Blast, Rice Hispa, Sheath Blight, Tungro, and healthy leaves. This was achieved using a comprehensive dataset containing 15,910 annotated images of rice leaves. Our proposed systems improve crop management practices by enabling prompt interventions based on precise predictions but it helps to guarantee ensuring global food security and maximizing overall crop yield. This study highlights the revolutionary potential of artificial intelligence in agriculture, paving the way for further advancements that can enhance sustainability and productivity.

关键词： Productivity computer vision Pathogens Accuracy Food security Transformers Feature extraction Agriculture Sustainable development Diseases

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Design of Rust Inspection Robot for Hydraulic Hoist Based on Machine vision 3

Design of Rust Inspection Robot for Hydraulic Hoist Based on...

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3rd international Symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2021

作者： Zhao, Peng He, Kai Wuhan University of Technology HuBei Wuhan China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Key Laboratory of Precision Engineering Shenzhen China

ISBN: (纸本)9781665437189

In order to realize automatic detection of corrosion on the piston rod of hydraulic hoist of hydropower station, it is important to find the location of corrosion and prevent oil leakage of hydraulic hoist and the blocking of control valve or other adverse consequences. A quick and convenient inspection robot for piston rod of hydraulic hoist is developed. Firstly, according to the specific working requirements of the piston rod of the hydraulic hoist of hydropower station and the basic principle of the robot, the structure scheme of the design of the testing robot is put forward. Then the robot control system is designed for the detection robot, and the basic idea of image recognition is introduced. Next the human-computer interaction interface is designed. Finally, the performance of the detection robot was tested to verify whether the reliability of the robot and the accuracy of image recognition meet the design objectives, and to ensure that the performance of the piston rod detection robot meets the design requirements. © 2021 IEEE.

关键词： Hydroelectric power plants

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Object Detection using OpenCV and Deep Learning 4

Object Detection using OpenCV and Deep Learning

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4th international conference on Advances in Computing, Communication control and Networking, ICAC3N 2022

作者： Tushar Kumar, Kundan Kumar, Sanjay Galgotias University School of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9781665474368

The most often adopted methodologies for contemporary machine learning techniques to execute a variety of responsibilities on embedded devices are mobile networks and multimodal neural networks. In this research, we propose a method for identifying an item that takes into account the learning - based pretrained system MobileNetV3 for an SSD (Single Shot Multibox Detector). This set of rules is utilized for constant identification, and for webcam feed to find the explanation webcam which recognizes the thing in a video transfer. Consequently, we utilize an item identification module which could find what's with inside the video transfer. To place into impact, the module, we integrate the MobileNetV3 and the SSD system for a quick and green profound getting to know-primarily based totally approach of item detection. The primary goal of our study is to investigate the effectiveness of an object identification technique SSD and the significance of MobileNetV3. The testing findings demonstrate that the Average Closeness of the set of rules to discover one of a kind training as car, person and cat is 97.45%, 95.67% and 85.59%, respectively. It enhances the accuracy of content detection at the processing speed necessary for practical identification and the needs of regular progress indoors and outdoors. © 2022 IEEE.

关键词： computer vision

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Based on Multi-Strategy Improved Harris Hawk Optimization Algorithm

Based on Multi-Strategy Improved Harris Hawk Optimization Al...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Yuliang Chen Jinhong Li College of Computer Technology North China University of Technology Beijing China Institute of Knowledge Engineering North China University of Technology Beijing China Computer Department North China University of Technology Beijing China

The paper proposes the EHHO algorithm to address the issues of reduced population diversity due to the use of random strategies for initial populations during the exploration stage of the Harris Hawk optimization (HHO) algorithm, as well as the problem of being trapped in local optima during the iterative process due to linear changes in escape energy during control development and exploration. Firstly, the Tent mapping function is introduced during the exploration stage to initialize the HHO population position. Secondly, a new formula for updating the nonlinear escape energy is used. Finally, the cosine inertia weight is utilized to improve the prey position update formula, thereby enhancing the algorithm’s local optimization ability. Experimental results demonstrate that the three optimization strategies effectively enhance the optimization performance of the HHO algorithm on 12 benchmark functions, and the improved HHO algorithm is significantly superior to other compared optimization algorithms.

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Investigation of Advanced Intelligent computer-Assisted Systems for Fault Diagnosis and control

Investigation of Advanced Intelligent Computer-Assisted Syst...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Xia Jianfeng Bian Quanlin Chen Shiyou China Railway Engineering Equipment Group Co. Ltd Zhengzhou China

The fault diagnosis system of tunnel boring machine under PDCA fault cycle is studied based on multi-source information. The system includes three modules, namely intelligent selection plan generation, fuzzy comprehensive evaluation system of the plan and fuzzy multi-attribute decision-making model. Realize the selection of tunnel boring machine and the optimization of main parameters. The research results of this project will provide technical support for tunnel boring machine fault diagnosis. Finally, the fault diagnosis method was verified by taking an actual project as an example. Research shows that integrated diagnosis methods can effectively improve the effectiveness and accuracy of tunnel boring machine fault diagnosis, thereby effectively improving the efficiency of engineering construction.

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Design Strategy of computer Network Security Storage System from the Perspective of Cloud Computing

Design Strategy of Computer Network Security Storage System ...

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Intelligent Communications and Computing (ICC), international conference on

作者： Anli Qian Chongqing College of Architecture and Technology Chongqing Municipal China

With the rapid promotion and comprehensive coverage of digital information technology, the Internet provides people with rich and convenient information exchange space. However, with the continuous expansion of technical application scenarios, the amount of data stored in the network has become increasingly huge, and the problem of data security storage has become increasingly prominent. In this regard, based on the problem of insufficient security performance of the traditional computer network storage system, this paper puts forward a set of new storage system design strategies, focusing on the application of cloud computing technology from the aspects of environment deployment, functional architecture and algorithm model, so as to improve the security of computer network storage system. The system is B/S architecture, the front-end interface is developed by VUE framework, and the back-end server is built by Django framework. The whole system is deployed in the cloud server, and the CEPH system is used to realize the cloud storage function of data, and Mysql database is used to store the basic information of users. Practice has proved that the system relies on the service control center to manage nodes, uses CP-ABE algorithm to build identity authentication module, and AES algorithm to build data encryption module, which has obviously improved the security and performance.

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Performance of Power Allocation Under Priority User in CR-NOMA

Performance of Power Allocation Under Priority User in CR-NO...

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Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Nahla Nur Elmadina Elsadig Saeid Rania A. Mokhtar Rashid A. Saeed Elmustafa Sayed Ali Othman O. Khalifa Department of Electrical Engineering Alzaiem Alazhari University Khartoum Sudan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia & Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya

A promising 5G communications technology was already known as CR-Non-Orthogonal Multiple Access (CR- NOMA). Power allocation (PA) and user pairing both affect how well NOMA systems perform. For the PA problem, the majority of recent studies offer suboptimal solutions with high computing complexity that prioritize rate maximization over performance fairness. Furthermore, a thorough search is required for the combined optimization of PA and UP. The primary contribution of this paper is the suggestion of a brand-new, minimally difficult Maximization optimization Data rate based power allocation (MODRFPA) in downlink CR-NOMA. The combined effects of SNR per subcarrier and typically combined channel gains have been thoroughly analyzed and investigated. Users' feedback on NOMA's performance in terms of user fairness and capacity is demonstrated. The outcomes of the simulation demonstrate that the suggested MODRPA can enhance the fairness and data rate of current UP techniques. Additionally, simulation findings demonstrate that the proposed coefficient power performs schemes more effectively and offers performance that is close to ideal.

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Safe Road AI: Real-Time Smart Accident Detection for Multi-Angle Crash Videos using Deep Learning Techniques and computer vision

Safe Road AI: Real-Time Smart Accident Detection for Multi-A...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： G. A Senthil R. V Lakshmi Priya S Geerthik G Karthick R Lavanya Department. of Information Technology Agni College of Technology Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

On national highways, in isolated places, or at night, many accidents go unreported. If the accident victim receives medical attention soon after the incident, about 40% of fatalities from accidents can be avoided. Among the causes of traffic deaths are increased traffic volumes, excessive speeding, careless and intoxicated driving, driver weariness, inadequate road infrastructure, and the presence of animals on roadways in forest locations. Road accident fatalities as a percentage of all deaths globally have grown by 2.2%, according to the World Health Organization (WHO). Every year, traffic accidents claim the lives of almost 1.35 million individuals. Emergency medical help is often delayed in traffic incidents that lead to the deaths of people. The novelty of research proposes IoT sensor network, GSM, GPS, that we’ll employ Deep Learning (DL) algorithms like CNN (Convolutional Neural Network) to better effectively identify auto accidents. The sensor camera captures images through IoT sensor networks and computer vision. The image resizing method is employed in data preparation to prepare the datasets. Here, deep learning (DL) algorithms will be used to train these datasets, and a model file will be produced. The police will be notified as soon as an accident is discovered via the notification. The prediction technique can accurately forecast an automobile collision when given an input image. Consequently, this study uses a deep learning system to effectively determine vehicle accidents with an accuracy of about 95%.

关键词： Deep learning computer vision Road accidents Roads Real-time systems Convolutional neural networks Accidents

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