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共 5993 条记录，以下是71-80 订阅

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Fuzzy Lateral control for Unmanned Vehicles: Addressing Probabilistic Packet Loss Issue Under Random Sampling 8th

Fuzzy Lateral Control for Unmanned Vehicles: Addressing Pro...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Hu, Aogui Cao, Zhiru Peng, Chen Wu, Jiancun School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this paper, the fuzzy control problem of unmanned vehicles is studied. In the unmanned vehicle systems, the lateral offset, yaw rate and heading offset are sampled and sent to60;the control center, and may unfor... 详细信息

ISBN: (纸本)9789819602933

In this paper, the fuzzy control problem of unmanned vehicles is studied. In the unmanned vehicle systems, the lateral offset, yaw rate and heading offset are sampled and sent to the control center, and may unfortunately be dropped due to transmission conflicts. To solve the packet loss issues, a fuzzy model is first established to approximate the nonlinear lateral dynamics of the unmanned vehicles. the measured signals are assumed to be sampled in a random way, induced by which, the phenomenon of probabilistic packet loss is considered. then, a method of input delay considering double stochasticity delay is used to model a probability distribution of the packet loss problem under stochastic sampling. Furthermore, relaxed sufficient conditions are given to ensure the robust mean square exponential stability and H∞ performance of the closed-loop system. Finally, the effectiveness of the lateral controller design is verified via a simulation example. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Packet loss

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Construction of PCOS Prediction Model Based on BP Neural Network

Construction of PCOS Prediction Model Based on BP Neural Net...

引用

Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Xianqing Hu Amit Yadav Asif Khan Abhishek Pratap Sah Sami Azam School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP N eural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

关键词： Training Adaptation models Accuracy Biological system modeling Computational modeling Neural networks Predictive models Data models Robustness Tuning

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Identification of Medicinal Plants and Related Fungal Diseases Based on Deep Learning Model

Identification of Medicinal Plants and Related Fungal Diseas...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Mathina Kani M Mahalakshmi R Shunmugavel V Abdul Kareem M Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Computer Science and Business Systems Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In today's technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. these readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multi-platform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms.

关键词： Medicinal plants Deep learning Measurement Visualization Accuracy Medical services Manuals Rapid prototyping Mobile computing Diseases

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Human Emotion Recognition from EEG-brain Signals using Enhanced Machine Learning Method

Human Emotion Recognition from EEG-brain Signals using Enhan...

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international conference on Advancements in Computational sciences (ICACS)

作者： Ghulam Ghous Shaheryar Najam Ahmad Jalal Dept. of Electrical & Computer Engineering Riphah International University Islamabad Pakistan Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

this study explores the application of advanced machine learning techniques to EEG data for detecting emotions in individuals with cognitive disabilities. Utilizing the SEED-IV dataset, we analyze EEG recordings from 15 participants across multiple sessions to identify emotional states such as happiness, sadness, fear, and neutrality. To ensure data quality, we employ preprocessing techniques including bandpass filtering and downsampling. Our proposed model integrates multi-class Support Vector Machine (SVM) with AAFST, an innovative feature selection and transformation mechanism. the effectiveness of this approach is demonstrated by an SVM accuracy of 85%, showcasing its capability to extract subtle emotional responses from EEG data. this research contributes to the growing field of affective computing, emphasizing the advantages of combining machine learning with EEG analysis to enhance the detection and understanding of emotions in individuals with cognitive disabilities.

关键词： Support vector machines Emotion recognition Accuracy Filtering Machine learning Feature extraction Electroencephalography Physiology Recording Emotional responses

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Performance Evaluation of Load Balancer in Storage Level Service Offering Applications 5th

Performance Evaluation of Load Balancer in Storage Level Ser...

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5th international conference on Recent Developments in control, automation and Power engineering, RDCAPE 2023

作者： Vianny, Maria Manuel Bhardwaj, Rajat Bhardwaj, Vivek Sharma, Vikrant Virender Manikandan, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India Department of Computer Science and Engineering ASET Amity University Karnataka Bengaluru India School of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Chandigarh University Punjab Mohali India Department of Information Technology E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam India

ISBN: (纸本)9789819600465

Cloud computing is a world view appropriately characterized for IT contributions where data and resources are aggregated by web-based cloud administration organizations using planned hardware and packages, primarily based on the web. People could use at a reasonable cost. Combining resources may affect the ease with which those resources are accessed, which might result in an issue. Load control is the process of distributing network traffic among several servers. No server will be overburdened as a result of this. In order to increase user responsiveness, load regulation equitably distributes the tasks. Additionally, it improves client accessibility to projects and websites. this archive is being kept in order to better understand charge control. It is connected to other structures via correspondence bodies like the Internet. Load balancing is an important distributed computing space to stay away from work beyond the workload and provide equally important support. Different calculations are used to determine the complexity of the system. Our suggested technique selects 100 virtual computers and, with 92% accuracy, determines where an ideal storage facility is for use in real time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud storage

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Quantum-Driven Hybrid Network Architecture Leveraging 5G for High-Speed and Resilient Data Transmission

Quantum-Driven Hybrid Network Architecture Leveraging 5G for...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Kusuma Ashok Kumar R. Mohandas Sajja Suneel M.R. Mahalakshmi Avinash Kumar Manmohan Singh Department of Physics B V Raju Institute of Technology Telangana state India Electronics and Communication Engineering Chennai Institute of Technology Chennai Department of CSE (Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of ECE Sri Muthukumaran Institute of Technology Chennai Tamil Nadu Department of Computer Science and Engineering IES College of Technology Bhopal

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the integration of quantum communication principles with 5G technology presents a groundbreaking approach to addressing the growing demand for high-speed, secure, and resilient communication systems. this paper proposes a Quantum-Driven Hybrid Network Architecture that leverages Quantum Key Distribution (QKD) for unparalleled security and combines it with 5G's ultra-fast data transmission capabilities. the architecture integrates key components such as QKD modules, 5G infrastructure, and edge computing, organized into a layered framework comprising quantum, network control, and application layers. Simulation results demonstrate significant improvements in throughput (up to 23.5%), latency reduction (up to 25%), and error rate reduction in high-noise environments (from 0.25% to 0.05%). the proposed system also exhibits enhanced security, achieving a 100% success rate in mitigating eavesdropping and man-in-the-middle attacks. Using resource-efficient mechanisms, the hybrid network achieves an overall accuracy of 97.8% in secure and resilient data transmission. this work validates the potential of quantum-enhanced 5G systems to meet the challenges of future communication networks.

关键词： 5G mobile communication Scalability Computer architecture Network architecture throughput Quantum key distribution Data communication Resource management Quantum networks Edge computing

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Adversarial Encoder-Driven Filter for Targeted Image Tagging: A Novel Approach to Visual Content Manipulation

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

international Image Processing, Applications and Systems conference (IPAS)

作者： Cole Mckee Dominic Flowers Jesse Wood Ethan Shafer Department of Electrical Engineering and Computer Science United States Military Academy West Point New York

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. this paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon’s Rekognition image tagging algorithm using a black box approach.

关键词： Training Resistance Computer vision Visualization Law enforcement Military computing Computational modeling Autoencoders Image annotation Closed box

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Enhancing Predictive Maintenance with AI: Applications and Impact

Enhancing Predictive Maintenance with AI: Applications and I...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Chitranjanjit Kaur Anurag Sharma dept. School of Engineering Design and Automation (Computer Science and Engineering) GNA University Phagwara India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Industrial maintenance is crucial for companies due to its significant impact on operational costs and efficiency. Many industrial firms find that a substantial portion of their expenses stems from equipment breakdowns, failures, or suboptimal performance over time. these costs can sometimes account for up to 50% of overall expenditures. Additionally, companies face various losses, including downtime from breakdowns, challenges in inventory and purchase management, and potential risks of injuries. the objective of this writing is to investigate how Predive maintenance is a footstep towards industry 5.0 where automation of predictive maintenance is evolved to autonomy in maintenance. In addition to it various machine learning algorithms are suggested to identify between healthy and poor data of a machine which is first step in Predictive maintenance then Deep learning algorithms to construct XAI (Explainable artificial Intelligence). Predictive maintenance, production scheduling, problem detection, predictive quality, and increased energy efficiency are highlighted in the typical use cases of the selected AI applications. Data from the real environment is transmitted to be virtually recreated. Efficient industrial maintenance helps mitigate these costs by reducing downtime, improving equipment reliability, and lowering the need for emergency repairs. By implementing a well-designed and optimized maintenance strategy, production plants can ensure their equipment operates as reliably as possible, minimizing disruptions and enhancing overall operational efficiency. Machine Learning (ML) methods have been appeared as a crucial tool in Predictive Maintenance (PdM) applications to prevent failures in equipment that make up the production lines. However, the performance of PdM applications depends on the appropriate choice of the ML method. To save expenses, find inefficiencies, duplicate tool tracking systems, and do other tasks, Digital Twin evaluates material utilization

关键词： Adaptation models Costs Machine learning algorithms Electric breakdown Stacking Companies Writing Vectors Maintenance Predictive maintenance

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Wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse control 8th

Wind Power Scheduling Deviation Compensation Based on Dual-F...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhu, Jianhong Li, Han Gu, Juping Mao, Linxin Chen, Shaoxuan He, Yu School of Electrical Engineering and Automation Nantong University Nantong226019 China Suzhou University of Science and Technology Suzhou215009 China

ISBN: (纸本)9789819602315

the intermittency of wind power fluctuations and the low reliability of reporting scheduling plans have always affected the friendly access of grid connected wind power. "One machine, one storage" provides an effective solution. An innovative control method for dual-frequency PWM pulses in an improved bidirectional DC/DC Zeta Sepic circuit model is proposed to address the controllability of energy throughput in energy storage systems with dynamic deviation compensation for wind power planning. Firstly, by analyzing the working timing logic mechanism of the energy transfer converter, the feasibility of the energy storage throughput circuit is verified. then, by analyzing the small signal control model of the bidirectional DC/DC converter during charging and discharging operation, the dual-frequency PWM automatic control of charging and discharging conditions is achieved, and the controllability of power exchange of the energy storage system at the wind turbine grid connection point is achieved. Finally, the model predictive control (MPC) method is adopted to effectively compensate the deviation of wind power plans, which can be accurately tracked. the simulation experiment demonstrates the effectiveness and feasibility of the converter model structure and control algorithm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： controllability

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Unmasking Hypertension through a Fusion of Human Lifestyle Data Using Machine Learning Algorithms

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： V. Vishnu Priya Nisanth G Nivethaa R P Srinidhi Vijay Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Computer Applications Kongu Engineering College Erode India Department of Computer Technology-UG Kongu Engineering College Erode India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. the volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. the World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. Machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. the techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance.

关键词： Hypertension Deep learning Machine learning algorithms Time series analysis Prediction algorithms Ensemble learning Blood Random forests Medical diagnostic imaging Arteries

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