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A Technical Bas System Through Cetralised System for Grid systems in Trnsmission of Data

A Technical Bas System Through Cetralised System for Grid Sy...

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2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Kurmi, Raviraj Singh Gupta, Ashish Krishnamoorthy, Ramkumar Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India

ISBN: (纸本)9798350357769

Industrial communication protocols are used by SCADA (supervising control and data acquisition) networks and Industrial control systems (ICS) to enhance their functionality. Modbus is an example of an application protocol that facilitates communication between millions of automation units. Sadly, Modbus lacks essential security features, which makes it vulnerable to errors in both design and implementation. These flaws leave systems open to eavesdropping, replay, and man-in-the-middle assaults, among other types of attacks. Taking advantage of these vulnerabilities may have serious repercussions for businesses as well as the general public, especially if important infrastructure assets like power stations, water distribution systems, and train transportation networks are attacked. The Modbus association has published security requirements in an effort to fix the security flaws in the protocol. Through the integration of TLS (Transport Layer Security) with the conventional Modbus protocol, these standards seek to improve security. Modbus packets are encapsulated by TLS in order to safeguard message integrity and offer authentication. Digital certificates based on X.509v3 are used by the security features to authenticate the client and server. This paper proposes a new secure version of the role-based access control architecture (RBAC) in order to solve security vulnerabilities with the Modbus protocol. This approach uses a message authorized via unit ID, a unique identifier for approving the Modbus frame, and an authorization procedure via roles, introduced as an arbitrary extensions within the X.509v3 certificate, to approve only the client & the Modbus frame. To summarise, the incorporation of TLS into Modbus, in conjunction with the suggested RBAC framework, effectively tackles security issues and fortifies the protocol's general resilience against any intrusions, guaranteeing the safe and effective functioning of industrial systems. © 2023 ieee.

关键词： Industrial control systems Modbus protocol architecture role-based access control (RBAC) SCADA security requirements and centralized architecture security vulnerabilities TLS X.509v3 certificate

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A Terrain Recognition Method Based on Semantic Segmentation for Field Robot Under Sample Imbalance

A Terrain Recognition Method Based on Semantic Segmentation ...

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2023 ieee international conference on Unmanned systems, ICUS 2023

作者： Shi, Xianyu Shao, Shiliang Wang, Ting Zhang, Qi Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of the Chinese Academy of Sciences Beijing China School of Software Shenyang University of Technology Shenyang China

ISBN: (纸本)9798350316308

Vision-based terrain recognition technology can help the robot understand the surrounding environment and then adjust the control and motion strategy in time. However, most of the existing work focuses on urban environments, and little attention is paid to complex off-road environments. Moreover, the class imbalance in the off-road environment is highly dependent on the specific environment so that the class imbalance problem is more serious, which makes the semantic segmentation of rare classes more challenging. In this study, we propose a visual terrain recognition method based on different weight loss functions for ground mobile robots in the off-road environment with class imbalance. The visual information is input into the improved network for terrain recognition. In order to better identify the rare classes, different weights are assigned to different classes in the loss function of the improved network. After a series of experiments, the results show that our improvement achieves better results than the original model in the off-road environment. © 2023 ieee.

关键词： Mobile robots

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Improving Anomaly Detection with Adaptive Dynamic Threshold: A Review and Enhanced Method 8

Improving Anomaly Detection with Adaptive Dynamic Threshold:...

引用

8th international conference on System Reliability and Safety, ICSRS 2024

作者： Attar, Arman Aghaei Bao, Kaibin Hagenmeyer, Veit Fabarisov, Tagir Morozov, Andrey Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350354508

In fast-growing modern cyber-physical systems, reliability plays a vital role. Effective anomaly detection, which identifies security and safety issues at early stage, is essential for ensuring system reliability. While much research has focused on anomaly detection techniques, fewer studies address a key challenge, setting precise and responsive thresholds for anomaly detection. In this study, we conduct a comprehensive review of current threshold setting methods. Thereafter, we introduce a novel approach for adaptive threshold setting. Our method is tailored for diverse safety and security tasks and is tested on a safety-critical exoskeleton model and a cybersecurity scenario for energy systems. The results demonstrate that our approach could enhance the threshold setting for anomaly detection in CPS. © 2024 ieee.

关键词： Anomaly Detection cyber-Physical systems Threshold Setting Time Series

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Adaptive Multiobjective General Predictive controller for a Class of Uncertain Switched systems 22

Adaptive Multiobjective General Predictive Controller for a ...

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22nd ieee international Multi-conference on systems, Signals and Devices, SSD 2025

作者： Thamallah, Ali Sakly, Anis University of Monastir Department of Technology ISIMM Monastir Tunisia University of Monastir Electrical Department ENIM Monastir Tunisia

ISBN: (纸本)9798331542726

The study established within the framework of this paper concerns the issues of a class of systems having a discrete dynamic that changes its model. More precisely, the focus is on those characterized by unpredictable changing modes. This type of system is known as switched system. In this context, the paper examines the design issue of an appropriate predictive control law capable of stabilizing the system by tracking a variable reference. The control law within the framework of the current paper is designed as a dynamic optimal solution of a multi-objective problem, which considers the dynamics of all modes. To evaluate and examine the performance of the developed control scheme, several simulation tests were conducted using a benchmark example. The obtained results show that the realized method effectively addresses the control issues of the considered system class under general reference tracking. © 2025 ieee.

关键词： multi-objective op-timization problem Predictive controller switched systems unknown switching signal

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Incremental Strategy Based ANC Using Hybrid Adaptive Nonlinear Filters

Incremental Strategy Based ANC Using Hybrid Adaptive Nonline...

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2024 ieee international conference on intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Varma, Kutcharlapati Sai Prasad Kranthi, Rajapantula Vasundhara National Institute of Technology Department of Electronics and Communication Engineering Warangal Hanumakonda India

ISBN: (纸本)9798350379525

The prevalence of nonlinearities in adaptive filters poses a challenging issue for noise reduction. Several methods are introduced to enhance the effectiveness of distributed active noise control (DANC) systems while dealing with nonlinearities. In this context, DANC algorithms utilizing Mish functions are being proposed to address noise reduction challenges. The Mish function addresses the issue of the vanishing gradient problem and enhances the speed at which convergence occurs by leveraging the smoothness and unboundedness characteristics. Considering this, the present manuscript introduces a collection of nonlinear methods for the DANC system that employs the Mish framework into an incremental strategy for filtered-x least mean fourth (IMF-xLMF) and filtered-x least mean square (IMF-xLMS). The proposed approaches entail designing weight update equations specific to the developed algorithms and performing a comprehensive convergence analysis. The suggested methods yield a quantitatively significant gain of 1-4 dB in noise reduction, effectively handling nonlinearities and reference signals, all while converging faster than previous techniques. © 2024 ieee.

关键词： Adaptive filters

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An Improved Neural Classification Logic to Predict Kidney Disease Identification Using Medical Image Processing Principles

An Improved Neural Classification Logic to Predict Kidney Di...

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2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Balaji, C.G. Balachandran, G. Imran, Md. Ujwala, S.Vamsi Edlabadkar, Ajinkya P. Fawareh, Hamed Department of Digital and Telecom Management Lavale Maharashtra Pune412115 India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam Andhra Pradesh PrakasamDistrict523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Kidney disease is a significant global health concern, requiring accurate and early diagnosis to prevent progression and ensure timely treatment. This study proposes an improved neural classification logic combining an AutoEncoder and an Artificial Neural Network (ANN) to predict kidney disease using medical image processing techniques. A dataset comprising kidney images from CT, MRI, and ultrasound was utilized to train and test the model. The AutoEncoder was employed for unsupervised feature extraction, reducing dimensionality while retaining key information related to kidney abnormalities. The ANN then classified these features into healthy or diseased categories. Image preprocessing techniques such as noise reduction, resizing, normalization, and contrast enhancement were applied to ensure data consistency. Additionally, advanced segmentation algorithms like U-Net were used to isolate the kidney regions from the background, focusing on the Regions of Interest (ROI). Data augmentation was applied to improve generalization. The proposed model achieved a classification accuracy of 94.8%, outperforming baseline models such as CNN (92.1 %) and SVM (88.7%). The AutoEncoder + ANN combination demonstrated superior precision, recall, and Fl-score, with an AUC-ROC of 97.4%. The results highlight the model's robustness and potential for practical applications in the early detection of kidney disease. © 2024 ieee.

关键词： Noise abatement

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Employing Artificial Intelligence Methods for the Diagnosis of Autism Spectrum Disorder in Children 5

Employing Artificial Intelligence Methods for the Diagnosis ...

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Sridevi, R. Joy, Helen K Karthikeyan, K.J. Gopika, S.S. Biju, Neha Seirah Shriniha, P.A. Department of Computer Science Bangalore India PSG Institute of Technology and Applied Research Department of Computer Science Coimbatore India

ISBN: (纸本)9798331518097

Accurate and timely diagnosis of disorder known as autism spectrum disorder (ASD) is not an easy task due to the complicated neurodevelopmental condition's high clinical presentation variation. In order to improve the diagnostic procedure for ASD in pediatric patients, machine learning (ML) techniques have come to light as potential approaches. The previous surveys about the practice of ML algorithms for diagnosing ASD in children has been thoroughly reviewed and summarized. The supervised and unsupervised learning, feature selection, and ensemble methods used in ASD research are among the many ML techniques that is methodically examined. The necessity of large-scale, diverse datasets, cross-validation methods, and interpretability are emphasized over the advantages, disadvantages, and potential future directions of ML-based ASD diagnostic models. This study attempts to offer insights for researchers, clinicians, and other stakeholders in the field of ASD diagnosis by critically analyzing the current status of ML in ASD Diagnosis. © 2024 ieee.

关键词： Self-supervised learning

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Comparative Analysis of Machine Learning Models for Solar Power Prediction Using Time Series Weather Data 3

Comparative Analysis of Machine Learning Models for Solar Po...

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3rd ieee international conference on Power Electronics, intelligent control and Energy systems, ICPEICES 2024

作者： Kane, Sumedh Nilesh Rathore, Akshit Abhijeet Anees, Sanya Kane, Arati Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India Instrumentation and Control Engineering Bharati Vidyapeeth College of Engineering Delhi India

ISBN: (纸本)9798350387193

Smart Grids preside over traditional power grids due to their efficiency and effectiveness. Smart grid implementation has benefited all components of the power system. These grids usually generate energy from renewable energy resources. However, the stability, reliability, and seamless functioning of the grid are adversely affected by the highly weather-dependent characteristics of renewable energy sources. As a result, accurate energy generation forecasting is an essential requirement for the grid's steady and reliable operation. The forecasting techniques for the quantity of electricity produced by solar power plants are discussed in this study. First, the impact of weather on solar power generation is investigated. Second, a machine learning method for solar panel output power prediction based on weather conditions is proposed. Finally, an ensemble approach utilizing different algorithms for machine learning is utilized to boost the accuracy of the proposed model. For the objective of creating prediction models, we utilized a variety of techniques, including temporal convolutional neural networks, fine trees, support vector machines with different kernel functions, artificial neural networks, and long-short-term memory RNNs. Each model's prediction accuracy has been assessed by comparing and contrasting these different methods. A comparative investigation indicated that the Temporal Convolutional Network-based model trumps the others in terms of efficiency. The proposed ensemble model exceeded the conventional individual models after performance comparison with the standard approaches. © 2024 ieee.

关键词： Prediction models

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An Automated Railway Unguarded Level Crossing using Lab View 5

An Automated Railway Unguarded Level Crossing using Lab View

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Mohandass, M.P. Dhanush Nigandan Sri, N. Saron Raj, Y. Stephan, A. KIT-KalaignarKarunanidhi Institute of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331518097

This research study introduces an advanced automated system for unguarded railway level crossings, developed using LabView,with the primary objective of enhancing safety and mitigating accident risks. The TDS employs vibration and inductive proximity sensors to reliably detect the approach of trains, providing timely alerts. In parallel, the LCS utilizes a camera module that integrates sophisticated image processing and machine learning algorithms to identify and track vehicles at the crossing accurately. Secure wireless communication between the TDS and LCS is achieved through the AES algorithm, ensuring robust data transmission while minimizing potential vulnerabilities. This automated solution not only delivers real-time alerts for train operators and road users but also enhances the overall efficiency of railway operations. By tackling the significant safety issues posed by unguarded level crossings, The design suggested provides an economical and scalable approach this may greatly enhance public safety and foster sustainable development within the railway infrastructure. © 2024 ieee.

关键词： Proximity sensors

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Smart-Home System Using the Fuzzy Logic Method on the Nodemcu Platform 9

Smart-Home System Using the Fuzzy Logic Method on the Nodemc...

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9th international conference on Information Management and technology, ICIMTech 2024

作者： Suswanti Santi, Novianti Fitti Hilman, Maulana Rizal Ilma, Zidni Kusumastuti, Dwi Listriana Yossy, Emny Harna BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

One of the significant technological advances is smart home systems, which aim to increase comfort, productivity, and security at home. The fuzzy logic method is used in this research to provide adaptive decision-making capabilities based on data from various sensors. Additionally, the NodeMCU platform allows us to integrate various sensors in smart-home systems easily. This study shows how Internet of Things (IoT) technology can help everyday life by making homes more intelligent and more comfortable. The implementation and evaluation results show that using the fuzzy logic method and NodeMCU in the context of a smart home improves the user experience, creating a home that is smarter, more comfortable, and responsive to the needs of residents. This research opens the door to further developing how to use the Internet of Things (IoT) to improve the performance and intelligence of smart home systems. The testing method in this research uses integration and system testing, and an evaluation method uses black box testing. The test results confirm the successful implementation of automatic control of lights and fans based on environmental conditions, such as darkness and temperature, and show the user's ability to control the device via a smartphone application, monitor temperature, and check the status of the lights. © 2024 ieee.

关键词： Smart homes

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