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Time Series Fusion Model for Predicting Electric power tools and equipment 6

Time Series Fusion Model for Predicting Electric power tools...

引用

6th international conference on Robotics, Intelligent control and Artificial Intelligence, RICAI 2024

作者： Xue, Liang Ling, Xinghong Yang, Lei Yang, Yang Computing Science and Artificial Intelligence College Suzhou City University Suzhou China College of Computer Science and Software Engineering Hohai University Nanjing China Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare Suzhou City University Suzhou China

ISBN: (纸本)9798331541699

To effectively address the issue of inadequate accuracy and timeliness in industrial data classification, this paper proposes a fusion classification model based on time series (DT-RF-MLP) for loss prediction of electrical work equipment. Firstly, data information is extracted from the dataset and features are constructed based on the data information to enhance the diversity of data features. Secondly, redundant and highly correlated features are removed by quantifying the correlation degree between the features to simplify the model inputs. On this basis, a fusion classification model is constructed and comparative experiments are conducted with a single model to validate the advantages of the fusion model in terms of prediction performance. The experimental results demonstrate that the model markedly enhances prediction accuracy while concurrently reducing the duration of the model run. © 2024 IEEE.

关键词： Prediction models

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Leveraging Deep Transfer Learning for automatic Discovery of Polyp in Colonoscopy Imagery 5th

Leveraging Deep Transfer Learning for Automatic Discovery of...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ahamed, Mohamadi Begum Syed Riaz Savita Beemkumar, N. Singh, Arjun School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Electronic and Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

Deep Switch latest is a rising technology that uses contemporary pre-skilled convolution neural networks or CNNs as a starting point for further evaluation. It leverages already-educated deep networks to fast, and it should be analyzed big information units and has been implemented for numerous imaginative and prescient problems such as object and scene reputation, item detection, and segmentation. This era has been implemented to detect today’s polyps in colonoscopy picas automatically. It is a crucial task because timely detection of today's polyps can cause early diagnosis and treatment of trendy colorectal cancers, one of the leading causes of modern cancers demise worldwide. This paper describes a trendy deep switch technique to detect contemporary polyps in colonoscopy images automatically. Finally, we evaluate our proposed approach at the available Endoscope dataset and exhibit how our trained version can accurately locate polyps in real-international colonoscopy photos. Our trained model achieves country-modern day-the artwork accuracy, making it an able candidate for deployment in scientific settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An Innovation Development of Smart Robotic System for Autonomous Monitoring 2

An Innovation Development of Smart Robotic System for Autono...

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2nd IEEE international conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Reddy, Somashekhara Srivastava, Saurabh Kumar Department of Computer Science and Engineering Bangalore India Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India

ISBN: (纸本)9798350347456

Autonomous monitoring systems are becoming increasingly popular as they can be used to monitor a number of different processes and tasks, ranging from security and surveillance to industrial optimization. Smart robotic systems are one of the most popular types of autonomous monitoring systems and can provide an effective and efficient way to monitor different situations. A smart robotic system consists of a robot that is programmed and equipped with sensors to detect and recognize objects and conditions in its environment. The robot is able to analyze the data from the sensors and use it to make decisions based on its programming. This allows the robot to act autonomously and autonomously monitor different areas, depending on its programming. The advantages of using a smart robotic system for autonomous monitoring include increased accuracy, efficiency, and safety. For example, a smart robotic system can be used to monitor and control industrial processes, such as production lines and assembly lines. The sensors allow the robot to identify objects and conditions, such as temperature, humidity, and pressure, and then adjust the parameters accordingly. This can help to optimize the production process, resulting in a more efficient process. © 2023 IEEE.

关键词： Robot programming

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Design of automatic Game System Based on Artificial Intelligence for Three Pieces of Chess 3

Design of Automatic Game System Based on Artificial Intellig...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Li, Ziwen Lin, Dun Deng, Kaitao Li, Weijia Yang, Jiale Zhang, Lei College of Electronic Information Engineering Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798331530280

In this paper, we design an automatic three pieces game device based on STM32F407ZGT6, aiming at realizing the accurate human-computer interaction game function. The device utilizes an air pump and X, Y gantry mechanical structure and integrates YOLOv5 deep learning model. Equipped with Emm42_V5.0 closed-loop stepper motors and MG996R servo motors, the device can precisely control the placement of the pieces in 3D space. By connecting to MaixCAM to realize piece position detection and confirmation, ensuring accurate execution of the game. the introduction of the YOLOv5 model significantly improves the accuracy and speed of piece recognition, enabling the system to respond to user actions and game changes in a timely manner. Provides a friendly user interface with physical key input. Users can notify the system to start the timer by pressing the designated button after completing a round of piece placement. Meanwhile, feedback to the user after each round of the game is provided through an indicator light, which facilitates real-time monitoring of the progress of the game. © 2024 IEEE.

关键词： Stepping motors

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Adversarial Machine Learning Architecture in AI-Driven Power Systems

Adversarial Machine Learning Architecture in AI-Driven Power...

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2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Nicolas, Dowens Figueroa, Henry Wang, Yi Elmannai, Wafa Giakos, George C. Manhattan College Electrical and Computer Engineering Department New York United States

ISBN: (纸本)9798350304602

The paper investigates the use of AI in power systems to improve monitoring, control, and optimization. For successful classification tasks on various datasets, ML models are critical, boosting power system dependability and efficiency. The research focuses on ML model designs in AI-driven power systems for binary, triple, and multiclass classification. Misclassifications can destabilize grid stability, underlining the importance of precise classification in decision-making processes. To combat this, this research proposes a DNN model that uses specific parameters that were fine-tuned for greater accuracy in classification tasks. Model correctness is an important performance metric that can be improved by architecture design and fine-tuning. For successful optimization, three essential loss functions (binary cross-entropy, categorical cross-entropy, and sparse categorical cross-entropy) are investigated. We have achieved great results with utilizing the sparse categorical cross-entropy loss function to minimize the error during training periods. The study underscores the critical relevance of correct categorization and the possibility for robust AI applications in power systems, which contribute to grid stability and sustainability. © 2023 IEEE.

关键词： Classification (of information)

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An Smart Analysis of Energy Efficiency Issues in Industrial Automation 2

An Smart Analysis of Energy Efficiency Issues in Industrial ...

引用

2nd IEEE international conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Singhal, Priyank Mahesh, T.R. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Industrial automation has become increasingly important in recent years. It has allowed for increased efficiency, productivity and safety in the workplace, as well as improved quality control of products. However, with the increased use of automation, comes the need to be mindful of energy efficiency issues. Energy use in industrial automation is primarily driven by the use of electric motors, pumps and other devices. These devices consume a large amount of electricity, especially when they are running continuously or at high speeds. This can result in significant energy losses and increased costs. Additionally, when the motors are operating at different speeds, they can draw more power than they need and this can also cause energy losses. To reduce energy losses, it is important to ensure that the motors and pumps are properly sized for the application and that they are running at the correct speed. Additionally, it is important to take into account the efficiency of the motors and pumps. For example, variable speed drives can introduce energy savings in certain applications. Additionally, energy-efficient motors and pumps can reduce energy losses significantly. © 2023 IEEE.

关键词： Automation

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An Autoencoder-Driven FDIA Detection Method for Virtual Coupling Systems of Heavy-Haul Trains 21

An Autoencoder-Driven FDIA Detection Method for Virtual Coup...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ren, Wanwan Peng, Jun Zhang, Rui Rong, Jieqi Shu, Boyu Liu, Yongting Yang, Yingze Central South University School of Computer Science and Engineering Changsha China Central South University School of Electronic Information Changsha China Changsha University School of Electronic Information and Electrical Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

The virtual coupling system provides a solution for improving the transport capacity of heavy-haul trains, and its implementation relies on reliable train-to-train communication. However, communications are vulnerable to cyberattacks. Among them, false data injection attacks (FDIAs) are a common and highly concealed form of attack. They interfere with train information interaction by destroying the authenticity of data, disrupting the cooperative control objectives of the virtual coupling system, and thus affecting the stable operation of the train. In order to ensure the safe, reliable, and efficient operation of heavy-haul trains, an autoencoder-driven FDIA detection method for virtual coupling systems of heavy-haul trains is proposed. First, the virtual coupling system of heavy-haul trains is modeled. Next, the collaborative control law of the virtual coupling system is designed, and the potential impact of false data injection on the system is analyzed. Afterward, a stacked autoencoder model is developed. Using the unsupervised idea, the model is trained to learn to reconstruct normal operating data, and the reconstruction error of the trained model is used as an attack detection indicator. Finally, the effectiveness of the proposed scheme is evaluated against several attack scenarios. © 2023 IEEE.

关键词： Learning systems

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An Input Error Method for Parameter Identification of a Class of Euler-Lagrange Systems 18

An Input Error Method for Parameter Identification of a Clas...

引用

18th international conference on electrical engineering, computing science and automatic control (CCE)

作者： Perrusquia, Adolfo Garrido, Ruben Yu, Wen Cranfield Univ Sch Aerosp Transport & Mfg Bedford England IPN CINVESTAV Dept Control Automat Mexico City DF Mexico

ISBN: (纸本)9781665400299

In this paper, an input error identification algorithm for a class of Euler-Lagrange systems is proposed. The algorithm has a state-observer structure which uses the input error between the real system and an estimated model instead of the output error. Both systems are controlled by two Proportional-Derivative (PD) controllers with the same gain values. An excitation signal is added to the PD controllers to guarantee parameter estimates convergence. Stability of the complete identification method and parameter estimates convergence are assessed via Lyapunov stability theory. Simulation studies are carried out to verify the approach.

关键词： Parameter estimation Numerical simulation Stability analysis Filtering theory Numerical models PD control Noise measurement

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Knowledge Graph and Mind Mappings of Mechanisms and Machine Theory and Their Applications in Digital Education 24

Knowledge Graph and Mind Mappings of Mechanisms and Machine ...

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2024 international conference on Cloud computing and Big Data, ICCBD 2024

作者： Li, Renjun Liu, Yue Lun, Fengyan Gu, Lidong Zhang, Yiwen Xu, Meichao School of Mechanical and Electrical Engineering Changchun university of science and technology China Instructor Development Center Changchun university of science and technology China Mechanical Engineering College Beihua university China

ISBN: (纸本)9798400710223

This study addressed the issues in the course of Mechanisms and Machine Theory, such as scattered knowledge points and lack of systematic organization, in engineering universities. We applied knowledge graph and Mind mapping techniques to organize and reconstruct the knowledge points, combining with the Rain Classroom application, to explore the construction of digital education ecology. Through Pre-class, In-class and Post-class teaching practices, the results show that the combination of knowledge graph, Mind mappings and Rain Classroom not only solves the problem of students' inability to understand the knowledge system of the course fully and effectively but also promotes lecturers' understanding and control of the overall curriculum, which can provide new ideas for the construction of digital education ecology in engineering universities. © 2024 ACM.

关键词： Knowledge graph

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Rotor position estimation in a bldc motor at low speed using g-functions and extended state observers 18

Rotor position estimation in a bldc motor at low speed using...

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18th international conference on electrical engineering, computing science and automatic control (CCE)

作者： Jimenez Hernandez, Isai Garcia Rodriguez, Carlos Linares Flores, Jesus Univ Tecnol Mixteca Div Estudios Posgrad Huajuapan De Leon Oaxaca Mexico Univ Tecnol Mixteca Inst Elect & Mecatron Huajuapan De Leon Oaxaca Mexico

ISBN: (纸本)9781665400299

This article presents a rotor position estimation scheme for a brushless direct current motor. This strategy is based on the construction of the so-called G functions, which fulfill the same objective of the commonly used Hall effect sensors, which is determine the switching logic to operate BLDC motors as a simple DC motor. Since these new switching pulses, derive from the G-functions processing, can ideally be determined independently of the motor speed, thus a good low speed performance is achieved. Additionally, this methodology is improved using extended state observers to avoid problems associated with the calculation of derivatives of the phase to phase motor currents.

关键词： Resistance Brushless DC motors Rotors Estimation Switches Observers electrical engineering computing

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