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Research on Data Operation Monitoring and Analysis System of computer Intelligent Cloud Platform

Research on Data Operation Monitoring and Analysis System of...

引用

Data Science and computer Application (ICDSCA), IEEE international conference on

作者： Lin Dewei Jiang Meiling Zhang Helin Xu Yiming Yan An Information and Communication Branch of State Grid Fujian Electric Power Co. Ltd Fuzhou Fujian China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. Fuzhou Fujian China

This paper proposes an application anomaly detection and bottleneck identification system (AAD-PSC) based on cloud platform service components. The system can monitor and analyze applications on multi-layered cloud platforms with customized index values. The embedded Ethernet interface is composed of MCU and Ethernet control chip W5500. The three-layer architecture of "cloud server, device, sensor and controller" is adopted to realize no difference monitoring at any time, place and terminal. The monitoring module dynamically adjusts the sampling period so that the sampling points are concentrated in the period when the resource usage of the application system fluctuates greatly. Use UDP/IP protocol to exchange information between the local and the cloud, and analyze and process the data in the cloud. The test results show that the system has the characteristics of low cost, small size, low power consumption and high data transmission accuracy.

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Rabble Based Autonomous Assistance Using Machine Learning Algorithms

Rabble Based Autonomous Assistance Using Machine Learning Al...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Kamal Sutariya Mohan Vishal Gupta Bechoo Lal Sura Rahim Alatba G.V. Sriramakrishnan Vikas Tripathi Department of CSE Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India College of Computing Science and Information Technology Teerthanker Mahaveer University Moradabad Uttar Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation (KLEF) KL Deemed University Vijayawada Campus AP India Computer Science Department Al-Turath Universiy College Baghdad Iraq CSE Mohan Babu University Tirupati India Department of CSE Graphic Era Deemed To Be University Dehradun Uttarakhand India

In order to properly control rabbles, we shall investigate computer vision and mechanical aid technologies with lending devices here. Machine intelligence, commonly referred to as AI, or algorithms, enables software to improve prediction performance while requiring a deliberate goal to do so. Machine-learning algorithms anticipate new output using historical data as their input. Since information is so crucial, creating novel approaches for effectively administering the neighbourhood, pervasive data gathering systems of today is a crucial next stage for actors and thus is entirely independent. Therefore, deep learning and its autonomous rabbel control support network will be discussed here. With the availability of information expanding through time, education for application in numerous sectors has become more widespread. Many fields have benefited from the use of machine vision, encompassing research and academia database and process visualisation, biomedical imaging, and more. These methods have been applied to safety, computer components, internet websites, and mobile applications. Thus, we can draw an opinion regarding an autonomous assistance system for effective kernel maintenance that is based upon deep ***, COVID-19 is causing mayhem throughout the world. There have found over than 110 million cases, or the number grows daily. Due to the outbreak, or more 4 million people died. The three most affected nations are the Usa, India, and Brazil, and many agencies are working to develop a vaccine. The world is seeking get back to normal after a string of shutdowns while upholding safety precautions. As a consequence, it is necessary to keep an eye on anyone's hat and temperature within public spaces like malls, hospitals, offices, shrines, and the like. Costs increase as a consequence of the lengthy process and extensive contact with people. Consequently, a system is required that can detect respirators, gauge temperatures, and manage the crowd tha

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Digital Ion Trap Mass Spectrometer Measurement System

Digital Ion Trap Mass Spectrometer Measurement System

引用

Computing, Communication, Perception and Quantum Technology (CCPQT), international conference on

作者： Xianguang Fan Lanhao Tu Xin Wang Yingjie Xu Yong Zuo School of Aerospace Engineering Xiamen University Xiamen China School of Aerospace Engineering Beijing Changcheng Institute of Metrology and Measurement Beijing China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

This paper builds a portable digital ion trap mass spectrometer prototype by designing the vacuum system, control circuit system, and host computer of the ion trap mass spectrometer. Dextromethorphan and ketamine samples were tested. At a scanning rate of 5000Th/s, the resolution of the dextromethorphan sample reached 0.26Th, and the resolution of the ketamine sample reached 0.33Th. The actual mass spectrum collected by the prototype is basically consistent with the theoretical mass spectrum.

关键词： Vacuum systems Quantum computing Prototypes Ions control systems Circuit stability Reliability

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An Efficient Weed Detection using Adaboost and CNN Hybrid Model

An Efficient Weed Detection using Adaboost and CNN Hybrid Mo...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Divya R Heena SK Shailaja Y Ramesh S. M Ashok Kumar S Kumar V Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore India Department of Computer Science Engineering QIS College of Engineering and Technology Ongole India Department of ME QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Kangeyam India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The detection of weeds is an essential component of precision agriculture because it provides farmers with the tools necessary to efficiently manage and control weed infestation in agricultural areas. Manual work is laborious and error-prone in traditional weed-detecting methods. computer vision and machine learning have shown promise in automating weed detection in recent years. This study introduces a hybrid model that combines AdaBoost and CNN to detect weeds. The hybrid model uses AdaBoost and CNN to boost weed detection accuracy. AdaBoost is used as a feature selector to identify input images with discriminative properties. CNNs, or convolutional neural networks, are popular deep learning models that can extract complex spatial features from images. This CNN receives the selected features. The CNN learns and improves features, enabling precise weed classification. A large dataset from Kaggle with crop field images of various weed species and backdrops is used to evaluate the hybrid model. The dataset is hand-annotated to provide ground truth labels for training and testing. The dataset trains the hybrid model, which is optimized for precision, recall, and F1 score. Experimental comparisons show that the proposed AdaBoost and CNN hybrid model outperforms individual models and other weed detection methods in accuracy and computing economy.

关键词： Precision agriculture Training Accuracy Automation Computational modeling Crops Production Feature extraction Data models Convolutional neural networks

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A Moving-Horizon Estimator of the Unmeasured States for Fuel Sloshing Dynamics in Close Proximity Operation

A Moving-Horizon Estimator of the Unmeasured States for Fuel...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Ibrahim Olawale Muritala Muhammed B Mu’azu Ahmed T. Salawudeen Ime J. Umoh Habeeb Bello-Salau Zaharuddeen Haruna Department of Computer Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical Engineering University of Jos Jos Nigeria Department of Computer Ahmadu Bello University Zaria Nigeria

Understanding how measurement noise influences sloshing dynamics is required to comprehend how it affects space vehicles in proximity operations. Despite researchers’ interest in space vehicle dynamics in proximity operations, few studies have explored the implications of measurement noise on its dynamics. However, one key problem encountered is poor estimation of the system state variable, which leads to poor control of the system. As a result, this study attempts to solve this issue by offering a scheme for providing critical information on the optimal estimation of the unmeasured states of the fuel sloshing dynamics. The approach was primarily based on moving horizon estimator-based model predictive control. The results obtained demonstrated the efficiency of the moving-horizon estimator in estimating unmeasured state variables. Hence, when the effects of measurement noise are considered, the moving horizon estimator provides a good assessment of the unmeasured states in a proximity operation.

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Application of Classification and Regression Tree and Spectral Clustering to Breast Cancer Prediction: Optimizing the Precision-Recall Trade-Off

Application of Classification and Regression Tree and Spectr...

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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： Asma Agaal Mansour Essgaer Almahdi Alshareef Hasan Alkhadafe Yahyia BenYahmed Department of Computer Science Faculty of Sciences Sebha University Sebha Libya Department of Artificial Intelligence Faculty of Information Technology Sebha University Sebha Libya Department of Information Systems Faculty of Information Technology Sebha University Sebha Libya Department of Computer Science College of Education Traghen University of Fezzan Murzuq Libya

The Receiver Operator Characteristic (ROC) test is often used to evaluate classification performance. However, it calls for special consideration when applied to the class-imbalanced data. The Precision-Recall Curve (PRC) is dependent on the class imbalance ratio. It summarizes the trade-off between the true positive rate and the positive predictive value for a predictive model using different probability thresholds. In this work, PRC is used to enhance the precision and recall of the Classification And Regression Tree (CART) algorithm to diagnose breast cancer; this enhanced the CART recall’s capacity to predict wrongly classified samples by 10%, from 89% to 99%, when compared to the standard CART algorithm. On the other hand, when evaluating the performance of the improved CART on unseen data, it achieved a recall of 95%. Thus, the recall of the CART decreased by 4% from 99% to 95%. This drawback can be attributed to the small size of the training data, which has been labeled using one of the clustering methods, spectral clustering, which achieved the best Silhouette in separating data into two clustering with a score of 0.43.

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Research on The Whole Process Management of Monitoring Information Based on The Integration of Four Tables

Research on The Whole Process Management of Monitoring Infor...

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Intelligent Power and Systems (ICIPS), international conference on

作者： Baifeng Chen Deshun Jia Jingfeng Zhao Zhihua Zhu Shuxin Huang Changming Ge Jian Yang Haichao Ji NARI Group Co. (State Grid Electric Power Research Institute) NARI Technology Development Co. Ltd NARI Control System Co. Ltd Nanjing China

With the rapid development of the national economy, the number of substations and the amount of substation information have doubled, but the monitoring information table of the station monitoring system, gateway computer, centralized control system and PMS3.0 management module has not yet established the functions of automatic verification, exception reminder and centralized management. There is a lack of automatic support means for the standardization and integrity verification of the information point table, and there are risks in the integrity and correctness of the monitoring information, It is not conducive to the daily work of equipment management discipline. Therefore, relying on the overall architecture of PMS3.0 and centralized control system, in accordance with the basic principle of "multi-level application and data penetration", through technical means and management methods, we can realize the automatic verification of the consistency of "four tables", strengthen the standardized management of the whole process, and improve the intrinsic safety of substation operation and maintenance monitoring.

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Unpaired Low-Dose CT Denoising using Conditional GAN with Structural Loss 3

Unpaired Low-Dose CT Denoising using Conditional GAN with St...

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3rd international conference on Wireless Communications and Smart Grid, ICWCSG 2021

作者： Yang, Zhao Yan, Chenggeng Chen, Hu Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China Sichuan University College of Computer Science Chengdu China

ISBN: (纸本)9781665425988

Low-dose CT has been a popular diagnostic imaging for its high availability and less radiation than normal-dose CT. Reducing the noise and reconstructing a noise-free CT image is a hotspot for researchers. The existing methods cannot deal with unpaired data well yet while the paired data is difficult to obtain. With unpaired data, we proposed an effective method using conditional generative adversarial network with Structural Loss to solve this problem. Our method is simpler and more efficient on computation than other unpaired methods and our experiments showed that it is comparable to other deep learning methods on denoising performance. © 2021 IEEE.

关键词： Generative adversarial networks

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Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction 3

Comparison of Conventional and Automated Machine Learning ap...

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3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Akaramuthalvi, J.B. Palaniswamy, Suja Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9780738146270

Breast cancer is a type of cancer in which the breast cells grow out of control. It is one of the leading cause for the high pace of death in women. Breast cancer classification is mainly done with the help of Machine Learning (ML) algorithms. In this work, we did a comparative analysis by creating a framework using ML and Auto ML algorithms (genetic programming) to accurately classify the cells in the breast as cancerous or non-cancerous. The work focused on automating and optimizing the algorithms for better prediction of cancerous cells. In Auto ML, Tree- based Pipeline Optimization Tool (TPOT), a genetic programming approach is used for finding the suitable classifiers and to automatically select the significant features and parameter values associated with the classifiers. Wisconsin Breast cancer diagnostic dataset, which comprises of digitized images taken from fine needle aspirate of breast mass has been used in this work. Evaluation based on recall, precision and accuracy have showed good results. © 2021 IEEE.

关键词： Genetic programming

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Pedestrian Positioning Algorithm Based on Zero-Velocity Correction

Pedestrian Positioning Algorithm Based on Zero-Velocity Corr...

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Automation, Robotics and computer Engineering (ICARCE), international conference on

作者： Shiying Huang Haolin Li Xueyan Zhang Hongwei Liu Tianhua Fu Department of Instrumentation Science and Opto-Electronics Engineering Beihang University Beijing China Department of Computer Science Beihang University Beijing China Harbin Medical University Harbin China Beijing Institute of Precision Machinery and Control Beijing China Beijing Experimental Factory Co. Ltd Beijing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Pedestrian positioning technology is a key technology that has emerged in recent years in the field of navigation. It is of great significance in indoor, underground and other navigation fields. In this paper, aiming at the problem of error accumulation in pedestrian positioning systems based on MEMS devices, a pedestrian positioning method based on the inertial navigation scheme is studied, and the zero-velocity correction method is used to suppress the accumulation of velocity errors, thereby improving the positioning accuracy. The effectiveness of the method in this paper is verified by experiments.

关键词： Micromechanical devices Meters Pedestrians Accuracy Automation Inertial navigation Hardware Robots

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