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Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

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International Journal of applied and Computational mathematics 2024年第4期10卷 1-15页

作者： Venkatesh, R. Rao, Deepak Dasaratha Sangeetha, V. Subbalakshmi, Ch. Bala Dhandayuthapani, V. Mekala, R. Department of Mathematics Hindusthan Institute of Technology Coimbatore 641032 India Indian Institute of Technology Patna India Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Shinas Oman Department of Information Technology M.Kumarasamy College of Engineering Tamilnadu Karur India

Traditional control methods may struggle to adapt to the nonlinear and uncertain characteristics in Autonomous Vehicle Control. In recent years, fuzzy control techniques, such as the Takagi–Sugeno fuzzy controller, have emerged as promising approaches for handling such complexities. Fuzzy controllers utilize linguistic variables and fuzzy logic to model system behavior, offering flexibility and robustness in dealing with uncertainties. Furthermore, Lyapunov function analysis provides a powerful tool for assessing the stability of dynamical systems. By employing Lyapunov functions, researchers can mathematically prove the stability of a system and derive stability criteria, contributing to a deeper understanding of system behavior. This paper investigates the enhancement of stability in control systems by employing Fuzzy Gain Scheduling combined with Lyapunov function analysis. Stability is a crucial aspect of control systems, ensuring their reliable and efficient operation in various dynamic environments. Traditional control techniques often struggle to handle the nonlinear and uncertain nature of modern systems. FGS offers a flexible and adaptive approach to control by adjusting controller gains based on system operating conditions. Additionally, Lyapunov function analysis provides a rigorous mathematical framework for stability assessment, enabling the verification of system stability properties. By integrating FGS and Lyapunov function analysis, this research aims to develop a robust control strategy capable of ensuring stability across a range of operating conditions. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed approach in enhancing stability and performance in control systems. Specifically, the settling time was reduced by 20%, and overshoot was minimized to 5% of the steady-state value. Furthermore, in experimental tests conducted on a real-world control system setup, the proposed approach demonstrated rob

关键词： Control systems Fuzzy gain scheduling (FGS) Lyapunov function analysis Performance Simulation Stability

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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Artificial Intelligence, computer, Data sciences and Applications (ACDSA), International Conference on

作者： Deepthi Tabitha Bennet Preethi Samantha Bennet Priya Thiagarajan Sundarakantham. K Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India Dept. of Computer Science and Business Systems Thiagarajar College of Engineering Madurai India Dept. of Computer Science and Engineering Thiagarajar College of Engineering Madurai India

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better.

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CAPE: Commitment-based Privacy-Preserving Payment Channel Scheme in Blockchain

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Gai, Keke Guo, Yunwei Yu, Jing Chan, Weilin Zhu, Liehuang Zhang, Yinqian Meng, Weizhi The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Institute of Information Engineering Chinese Academy of Sciences Beijing China The Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Applied Mathematics and Computer Science Cybersecurity Engineering Technical University of Denmark Kongens Lyngby Denmark

Ensuring scalability in cryptocurrency systems is significant in guaranteeing real-world utility along with the remarkable increment of cryptographic currency. As an alternative in solving scalability issue, payment channel allows users to deliver extensive offline transactions without uploading massive transaction details to the blockchain, such that increasing efficiency can be achieved. However, the implementation of payment channel still encounters privacy concerns when considering the publicly available transaction amounts and the potentials in mining associations between transaction parties. In this paper, we propose a novel payment channel scheme, entitled Commitment-based Anonymous Payment ChannEl (CAPE), to facilitate unlimited off-chain bidirectional payments while guaranteeing participants’ privacy. The proposed scheme adopts zero-knowledge proof (zk-SNARKs) and verifiable timed (VTD) commitments to ensure the anonymity of the relationship between on-chain and off-chain transactions, privacy of transaction amounts, and security of balances. We comprehensively formalize security definitions and present rigorous proofs for each security attribute. Experiment results further demonstrate the practical viability of CAPE. © 2025 IEEE. All rights reserved.

关键词： Differential privacy

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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2277-2293页

作者： Wang, Shih-Yuan Li, Che-Ming Liong, Sze-Teng Sheng, Yu-Ting Huang, Yen-Chang Liong, Gen-Bing Gan, Yee-Siang Graduate Institute of Architecture National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Architecture Feng Chia University Taichung40724 Taiwan Department of Applied Mathematics National University of Tainan Tainan Taiwan Faculty of Computer Science & amp Information Technology University of Malaya Kuala Lumpur50603 Malaysia

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein

关键词： Mobile robots

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Investigation of district heating peak load reduction in residential neighbourhoods 18

Investigation of district heating peak load reduction in res...

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18th IBPSA Conference on Building Simulation, BS 2023

作者： Mokhtari, Reza Filonenko, Konstantin Junker, Rune Grønborg Madsen, Henrik Li, Rongling Department of Civil Engineering and Mechanical Engineering Technical University of Denmark Brovej 118 Kgs. Lyngby2800 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 Kgs. Lyngby2800 Denmark

This study explores the potential of utilizing the energy flexibility of heat customers to reduce peak loads and improve the efficiency of district heating systems. Using Modelica and compiled in Dymola, a rule-based controller and dynamic heat price signals were developed to unlock the energy flexibility of buildings. Simulations were conducted to estimate peak load reduction and potential energy savings that could be realized. To investigate this opportunity, we conducted a simulation-based modeling study of a case study consisting of 19 apartment blocks connected to a local district heating network in a certain location. © 2023 *** rights reserved.

关键词： District heating

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Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

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npj Computational Materials 2023年第1期9卷 733-743页

作者： Evan R.Antoniuk Gowoon Cheon George Wang Daniel Bernstein William Cai Evan J.Reed Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Department of Chemistry Stanford UniversityStanfordCAUSA Department of Applied Physics Stanford UniversityStanfordCAUSA Google Research Mountain ViewCAUSA Department of Physics Stanford UniversityStanfordCAUSA Department of Mathematics Stanford UniversityStanfordCAUSA Department of Computer Science Stanford UniversityStanfordCAUSA Department of Materials Science and Engineering Stanford UniversityStanfordCAUSA

Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials *** this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical *** reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation *** a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human ***,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability *** development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

关键词： inorganic crystalline utilize

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A heterogeneous graph cross-omics attention model for single-cell representation learning

A heterogeneous graph cross-omics attention model for single...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yue Zhang, Junfeng Wang, Shulin Zhang, Wei Zeng, Xiangxiang Kwoh, Chee Keong Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China Hangzhou Machine Learning Research and Development Center Sina Weibo Zhejiang Hangzhou China Changsha University College of Computer Engineering & Applied Mathematics Hunan Changsha China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9781665468190

Single-cell multi-omics sequencing technologies allow simultaneous measurement of transcriptome and epigenome profiles in the same cell, providing unprecedented opportunities to dissect cell heterogeneity. Despite great efforts, conjoint analysis of single-cell multi-omics data still suffers from sparsity, high dimensionality and binary. In this study, we present a heterogeneous graph cross-omics attention model (scHGA), a computational tool based on a heterogeneous graph neural network combining two attention mechanisms to jointly analyze single-cell multi-omics data based on different protocols data, including SNARE-seq, scMT-seq and sci-CAR. To avoid the cell heterogeneity of single-omics data, scHGA automatically learns a cell association graph to capture neighbor information. The latent representation of aggregated cells generated by hierarchical attention can fuse knowledge across different omics to dissect cellular heterogeneity, providing a better scheme to characterize the features of cells. scHGA is an effective exploration of graph neural networks in single-cell multi-omics analysis, providing new insights into the understanding of single-cell sequencing data. © 2022 IEEE.

关键词： Graph neural networks

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Bishoy Gerges Barbara Bazzana Nicolò Botteghi Youssef Aboudorra Antonio Franchi Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands Department of Applied Mathematics University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.

关键词： Location awareness Visualization Pipelines Noise reduction Autonomous aerial vehicles Visual servoing Trajectory Planning Standards Robots

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BGP Typo: A Longitudinal Study and Remedies

arXiv

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arXiv 2023年

作者： David, Liron Shavitt, Yuval Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science School of Electrical Engineering Tel-Aviv University

BGP is the protocol that keeps Internet connected. Operators use it by announcing Address Prefixes (APs), namely IP address blocks, that they own or that they agree to serve as transit for. BGP enables ISPs to devise complex policies to control what AP announcements to accept (import policy), the route selection, and what AP to announce and to whom (export policy). In addition, BGP is also used to coarse traffic engineering for incoming traffic via the prepend mechanism. However, there are no wide-spread good tools for managing BGP and much of the complex configuration is done by home-brewed scripts or simply by manually configuring router with bare-bone terminal interface. This process generates many configuration mistakes. In this study, we examine typos that propagates in BGP announcements and can be found in many of the public databases. We classify them and quantify their presence, and surprisingly found tens of ASNs and hundreds of APs affected by typos on any given time. In addition, we suggest a simple algorithm that can detect (and clean) most of them with almost no false positives. © 2023, CC BY.

关键词： Border Gateway Protocol

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Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detection in the Construction Industry

Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detect...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Blessie, A. Alice Sankarasubramanian, R.S. John, Joseph Krishnapriya, S. Bhuvanesh, A. Keerthanadevi, R. Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Psg Institute of Technology and Applied Research Department of Mathematics Coimbatore India Gregorian Institute of Technology Department of Mechnanical Engineering Kottayam India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

Wearing safety helmets is an effective way to keep construction workers safe on the job. Workers often opt to remove their helmets due to discomfort or a lack of security knowledge, leaving them vulnerable to unsaid threats. A worker's risk of injury from falls, both physical and otherwise, increases when they do not wear a safety helmet. Because detecting whether workers are wearing safety helmets is an important aspect of managing safety on construction sites, a fast and accurate detector is required. However, standard manual monitors aren't particularly popular, and attaching sensors to a safety helmet is a laborious operation. With that in mind, this study presents EM-YOLOv7, a DL method for autonomous safety helmet recognition in real-time that has been developed for use on construction sites. A multi-scale non-local attention module (MSNA) is incorporated into the system to improve detection accuracy in complicated situations. This module enhances feature recovery by aggregating semantic context across multiple scales. Further, to improve detection performance and handle mutual occlusion, a loss function named Wise-IoUv3 (WIoUv3) is used. This method adjusts the loss depending on the overlap between the predicted and ground truth bounding boxes. To train and test the model, we will utilize the safety helmet-wearing (SHWD) dataset, which includes a wide variety of construction workers and whether or not they wore helmets. The goal of this technique is to provide a 99% accurate solution for real-time helmet recognition, which would greatly improve safety management and decrease the likelihood of accidents on construction sites. © 2024 IEEE.

关键词： Construction industry

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