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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Load Balancing in Cloud Computing Environment using Butterfly Optimization Algorithm 3

Load Balancing in Cloud Computing Environment using Butterfl...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Alzubaidi, Laith H. Arabi, Mohammed Yousif Kumar, Bura Vijay Kumar, M. Hema Almoussawi, Zainab Abed The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Electronics and Communication Engineering Sona College of Technology Salem India College of Islamic Science Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Load balancing is the process of distributing the incoming traffic of the network among various resources and servers. When one of the servers fails, the load balancer transfers the network traffic to the other servers across the group. As soon as a server is joined to the group, the load balancer begins delivering traffic to that server as part of its balancing mechanism. It is a challenging effort to use distributed storage systems to manage a big amount of data and the synchronisation issue between many consumers or producers. In order to address this issue, this research proposal offers payload benchmarking with dynamic load balancing in distributed streaming storage systems using Butterfly Optimization Algorithm (BOA). The main objective of the optimization technique is to analyze the benchmarking data and find the failure prediction of Kafka benchmarking. The BOA is used to solve optimization problems, particularly when there is insufficient or faulty data. To achieve the highest throughput from the streaming storage system, the performance of the benchmarking tool for distribution has been enhanced. The performance of the Kafka and DevOps systems is validated by the parameters of network latency as 3800ms, Execution time as 970ms, Memory size as 385kb, and Bandwidth as 380kb. The performance of the benchmarking tool for distributing the streaming storage system has been improved to achieve the maximum possible throughput from the streaming storage system. © 2023 IEEE.

关键词： Butterfly Optimization Algorithm Cloud computing Distributed storage systems Kafka benchmark Load balancing

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Health detection of High-speed Train Pantograph Based on Deep Belief Network 3

Health Detection of High-speed Train Pantograph Based on Dee...

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3rd international conference on Testing technology and Automation Engineering, TTAE 2023

作者： Wu, Zhihe Feng, Qingsheng School of Automation and Electrical Engineering Dalian Jiaotong University Dalian116028 China

ISBN: (纸本)9781510674769

Pantograph is an important subsystem in the Pantograph-OCS system, and its health condition is related to the smooth running of high-speed train. This article, the structure of high-speed railway pantograph model is first optimized based on finite element analysis. After optimization, the average low-order vibration frequency was reduced by 7.75%, and it was concluded that the optimal pantograph sliding plate consists of four equally symmetrical structures. Then, the weak links in the pantograph structure were obtained by comprehensive static strength analysis, and acceleration sensors were arranged to collect the working status data of the pantograph. Finally, a deep belief network (DBN) is used to detect the health status of key pantograph components. The vibration signals collected by multiple sensors were segmented and extracted, and feature extracted for unsupervised DBN training. By comparing and analyzing the prediction results, an appropriate number of samples was selected to improve the diagnostic accuracy of the model to 97.87%. The experimental results show that the fully trained pantograph DBN network model can achieve high accuracy in identifying and classifying fatigue conditions. © 2024 SPIE.

关键词： Structural optimization

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Social Distance and Face Mask detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： Computer vision

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A Hybrid Approach for Efficient Traffic Sign detection Using Yolov8 And SAM 24

A Hybrid Approach for Efficient Traffic Sign Detection Using...

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3rd Asia conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Zhang, Kai Chen, Junzhou Zhang, Ronghui Hu, Chuan Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Guangdong Guangzhou510275 China Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou Guangdong Guangzhou510275 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai Shanghai200240 China

ISBN: (纸本)9798400716416

In this study, we present an innovative hybrid approach for traffic sign detection in autonomous driving, combining YOLOv8's real-time detection capabilities with the Segment Anything Model (SAM), enhanced through Visual Prompt Tuning. This methodology addresses the challenge of accurately identifying diverse traffic signs, particularly those that are less common or visually complex. After the initial detection by YOLOv8, the SAM (Segment Anything Model), enhanced with Visual Prompt Tuning, is then applied to refine and improve the accuracy of these detections, especially for signs that are less common or have complex visual features. This two-step process, combining the quick detection capabilities of YOLOv8 with the detailed segmentation ability of SAM, aims to achieve high accuracy in traffic sign recognition, which is essential for the safe operation of autonomous vehicles. © 2024 ACM.

关键词： Model Fusion

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Mathematical intelligence Blended Teaching Model Based on Knowledge Graph – A Case Study of Surgical Nursing 3

Mathematical Intelligence Blended Teaching Model Based on Kn...

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3rd international conference on Digitalization and Management Innovation, DMI 2024

作者： Qiu, Xiaoran Deng, Haiyan Li, Pan He, Wenxia School of Nursing Xiangnan University Hunan Chenzhou China

ISBN: (纸本)9781643685823

Digital intelligence refers to the integration of digital technologies and intelligent digitalization, which encompasses the development and application of data as a new productive force for societal benefit. Blended teaching has become essential in adapting to the educational advancements of the digital intelligence era. The deep integration of digital intelligence technology and classroom teaching has become the new norm of education and teaching reform. As the core technology of artificial intelligence (AI), a knowledge graph can express the knowledge system visually, integrate educational resources, collect learning process data to realize the monitoring and diagnosis of the learning process and meet the diversified needs of intelligent education applications. It is an effective tool for deeply integrating nursing education and AI. As healthcare technology continues to evolve and knowledge is rapidly updated, students need to continually learn and update their knowledge base so as to keep up with professional development. Therefore, this study explored the practical strategy of digital–intelligent blended teaching of Surgical Nursing by reshaping and integrating the course content, sorting out the course knowledge system, and constructing the course knowledge graph to enable students to systematically master the learning content and access high-quality learning resources. It provided a reference for the digital transformation of higher education, with a view to promoting the consolidation of students’ mastery of basic knowledge, improving students’ clinical thinking and comprehensive ability to implement holistic care for patients, and cultivating students’ ability to acquire and use cutting-edge knowledge. © 2025 The Authors.

关键词： Knowledge graph

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Natural scene text detection algorithm based on the regional proposal 3

Natural scene text detection algorithm based on the regional...

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2023 3rd international conference on Artificial intelligence and Industrial technology Applications, AIITA 2023

作者： Wu, Guangyue Zeng, Qingtao Zhao, Jiefeng Yang, Zibo School of Information Engineering Beijing Institute of Graphic Communication Beijing China

Text detection in natural scenes is widely used in various fields. The combination of deep learning models and text detection improves the accuracy and efficiency of detection. Its algorithm is also endless, with continuous optimization and improvement. On this basis, some algorithms for text detection in natural scenes are summarized from the two aspects of the Faster R-CNN method and SSD method based on regional suggestion. These algorithms and experimental data are analyzed and compared to further summarize the challenges faced by text detection in natural scenes and the direction of improvement. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Severity Levels of Apple Disease Recognition Using CNN and Random Forest: An Integrated Approach 3

Severity Levels of Apple Disease Recognition Using CNN and R...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yashu Kukreja, Vinay Chamoli, Sushant Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350319125

The primary objective of this research is to examine the critical undertaking of promptly identifying and categorising apple leaf diseases within the context of precision agriculture. The cornerstone of this study is a diversified dataset consisting of 4,976 photos collected from several digital platforms. The suggested model is a precisely built combination of Convolutional Neural Networks (CNNs) and Random Forest, which incorporates special architectural characteristics. Results showed an overall accuracy of 72.50%, illustrating the model's competence in illness identification. The metrics of precision, recall, and F1-score provide a thorough assessment of the performance of the system across all degrees of illness severity. The integration of Convolutional Neural Networks (CNNs) and Random Forests in the model capitalises on the respective advantages of each, hence exhibiting potential for wider use in the field of agriculture. The current study signifies a notable progression in the field of automated plant disease identification, hence making a valuable contribution towards improving crop management and promoting sustainability in the domain of precision agriculture. © 2023 IEEE.

关键词： agriculture Apple leaf CNN-Random forest crop Deep Learning disease detection

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Dynamic Load Emulation for Evaluating EVs Machines Based on Sliding Mode Control 3

Dynamic Load Emulation for Evaluating EVs Machines Based on ...

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3rd international conference on Electrical Machines and Drives, ICEMD 2023

作者： Adib, Mohammadreza Vahedi, Abolfazl Iran University of Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

Dynamometers are widely used for electric machines and their drive systems assessment, reducing prototyping time for many applications, such as Electric Vehicles machines. Having the same mechanical topology allows changing the control algorithm of the dynamometer to significantly improve the overall functionality and precision of mechanical load emulation imposed on motor under test. Hence, this paper proposes dynamic load emulation (DLE) by utilizing sliding mode control in the dynamometer. The proposed method is characterized by its simplicity, robustness, and low computational effort that meets industry requirements. First a proper sliding manifold is designed in which the sliding manifold contains the desired dynamic. The control signal is derived from that, which consists of two parts;the first part is based on the dynamics of the system, and the second part is a switching function, responsible for the rejection of disturbances. Thus, dynamic load emulation can be attained with a fast and precise response to various linear and nonlinear loads based on the mathematical model of the desired load. The desired model of mechanical load provides the speed reference to achieve the desired dynamic by forcing the shaft to rotate at a specific speed. The approach has been validated using a linear and a nonlinear load models simulated in MATLAB/Simulink. The perfect tracking of the reference has shown that emulation can still be carried out despite uncertainty in the model and disturbances. © 2023 IEEE.

关键词： Fault detection

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Pool Fire Rendering Method Based on Improved Particle system with Collision detection Algorithm 3

Pool Fire Rendering Method Based on Improved Particle System...

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3rd international conference on Machine Learning and Computer Application, ICMLCA 2022

作者： Su, Mo Guo, Xiangkun Li, Shu Liu, Meng Liu, Chang Shenyang Ligong University Shenyang China Shenyang Institute of Computing Technology Co.Ltd. CAS Shenyang China

ISBN: (纸本)9781510664814

In order to solve the problem that pool fire simulation is difficult to achieve real-time and realistic performance, in this paper, a pool fire rendering method based on improved particle system with collision detection algorithm is proposed. Firstly, mathematical model of pool fire is analysed, on this basis, the improved particle system model is established taking outside factors (such as wind speed, etc.) into consideration, and then via pool fire collision detection model based on adaptive binary tree, this paper uses partition algorithm based on adaptive binary tree to optimize the speed of burning liquid collision detection, this plays a crucial role for the quality and efficiency of subsequent rendering, thus realizes the pool fire rendering process optimization. Experimental results show that this method is simple to implement and the display effect of pool fire rendering is more real. © 2023 SPIE.

关键词： Binary trees

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High performance of liquid nitrogen zero boil-off system based on high-purity germanium detection 3

High performance of liquid nitrogen zero boil-off system bas...

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3rd international conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2023

作者： Zhang, Kai Zhou, Jiayi Wang, Dongdong Su, Yulei Ding, Huaikuang Zhang, Huabiao Anhui Vacree Technologies Co. Ltd Hefei230088 China The 16th Institute of China Electronics Technology Group Corporation Hefei230088 China

ISBN: (纸本)9781510666689

In this paper, a liquid nitrogen zero boil-off (ZBO) system is designed for the detection of high-purity germanium(HPGe). The system has the characteristics of lightweight, low heat leakage, and high intensity. Its no-load static evaporation rate is 0.12L/d, which is about 50% of the traditional biological container. When simulating the heat leakage of 1L/d of HPGe detector, but the actual 0.69L/d, the system's static evaporation rate test data is about 0.78L/d, and the relative error is about 4% with high consistency. Therefore, when the actual 1L/d heat leakage, the total heat leakage is about 1.2L/d, which is 30%-40% of similar products, and the performance is greatly improved. The system controls the output power of the refrigerator through the change of pressure. With the special air duct, the performance of the refrigerator is improved, and the high performance of ZBO is realized, which provides the necessary conditions for the continuous detection of HPGe. © 2023 SPIE. All rights reserved.

关键词： Liquid nitrogen

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