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8th IEEE International conference on Computing, Communication, control and Automation, ICCUBEA 2024

作者： Mer, Hiren V. Suthar, Anil Gujarat Technological University Ahmedabad India New Lj Institute of Technology & Engineering Ahmedabad India

ISBN: (纸本)9798350391770

In IoT Security risks are, however, presented to devices and services by this integration. To give machine learning (ML) credit for being a novel approach, this paper surveys Intrusion Detection systems (IDS) for the Traditional network and internet of Things. With 41 articles review, Machine Learning can adapt to the changing IoT environment, Deep Learning (DL) in particular shows promise as a method. IoT device with security problem with complex IoT landscape is too much for traditional rule-based IDS to handle, which has led to a rise in interest in machine learning approaches. To solve issues used different techniques are investigated. The research discusses important feature selection techniques related to IoT intrusion detection, emphasizing the value of feature engineering and classification. With newly created datasets both normal and malicious scenarios use performance measures such as F1-score, accuracy, precision, recall, and AUC-ROC. © 2024 IEEE.

关键词： network intrusion

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A Comparative Study of control Valves for quality Assurance in Improving Oil–Water Flow Separation Efficiency

A Comparative Study of Control Valves for Quality Assurance ...

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UNIfied conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and performance Engineering network, TEPEN 2023

作者： Atif, Muhammad Mishra, Rakesh Charlton, Matthew Limebear, Andrew School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Koso Kent Introl BrighouseHD6 1QG United Kingdom

ISBN: (纸本)9783031494208

control valves are critical components in the oil and gas industry, as they regulate the flow parameters in the downstream separation processes. The design and performance of these control valves are essential to maintaining the flow quality of the multiphase mixture downstream. However, the complex geometry of control valves results in the generation of high shear forces and turbulence, causing strong emulsion to form, which can make the separation difficult and increase the time, resources, and chemical additives required for the process. The present study used computational fluid dynamics to analyse and compare the shear performance of four types of control valves namely, a globe valve, an angle valve, an off-line valve, and a z-type valve. The valves are fitted with the same standard high friction (HF) trim with a designed flow capacity (Cv) of 90 at 100% valve opening. The effect of geometric features on the distribution of pressure, velocity and volume fraction has been enumerated. A mixing coefficient (MC) has been used to quantify oil distribution and separation performance based on the in-situ properties of each of the valves studied. The results show that the z-type valve provides the lowest pressure drop and highest flow capacity while the globe valve provides high separation performance amongst all the valves studied. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computational fluid dynamics

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An Enhancement of Power quality in Grid-Connected Renewable Energy systems Using Fuzzy Logic control 2

An Enhancement of Power Quality in Grid-Connected Renewable ...

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2nd IEEE International conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Kumar, Rajesh Agarwal, Nirmal Kumar Kumar, Aman Deshwal, Mudit Singh, Amritesh Srivastava, Nishant Jss Academy of Technical Education Department of Electrical Engineering Noida India

ISBN: (纸本)9798350390179

The article represents an approach to enhance grid-connected renewable energy systems' power quality by utilizing advanced control techniques for photovoltaic (PV) arrays and wind turbines. The maximum power point tracking method for PV arrays is neural network-based, and adaptive to changes in temperature and sunlight. This is combined with a boost converter operated through Pulse Width Modulation (PWM) and managed by a Proportional-Integral-Derivative (PID) controller to reduce oscillations and enhance response times. For wind turbines, an MPPT algorithm based on Fuzzy Logic control (FLC) optimizes power output by adapting to wind speed changes, and a boost converter maintains voltage output for efficiency. Power synchronization from PV, wind, and thermal plants with the AC grid is achieved using a current-controlled inverter. Additionally, the unified power quality conditioner (UPQC) utilizing FLC mitigates voltage sags, swells, and harmonic distortions, improving grid stability. The performance evaluation focuses on the percentage of total harmonic distortion (THD), voltage stability, and load sensitivity, showcasing the effectiveness of these control strategies in boosting the dependability and effectiveness of renewable energy systems in conjunction with the grid, thus supporting a sustainable energy infrastructure. © 2024 IEEE.

关键词： Harmonic distortion

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Collaborative Access control for IoT - A Blockchain Approach 3

Collaborative Access Control for IoT - A Blockchain Approach

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3rd IEEE International conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Huang, Yongtao Yen, I-Ling Bastani, Farokh University of Texas Dallas United States

ISBN: (纸本)9798350354348

The internet of Things (IoT) necessitates robust access control mechanisms to secure a vast array of interconnected devices. We adopt the blockchain based decentralized access control approach and identify the gaps in existing works. Then, We develop a blockchain embedded access control (BEAC) framework to bridge the gaps. First, blockchain based solutions for access control require an enabling P2P network while existing P2P overlays do not support some required features. We develop a novel P2P infrastructure to seamlessly support our BEAC framework. Second, most of the works consider blockchain based access control for a single access control model, and we develop a generic blockchain mechanism and show that it can support the embedding of various access control models. Finally, existing works adopt existing blockchain platforms which may incur a high communication overhead. We design a more efficient consortium blockchain solution by leveraging more advanced components to achieve better performance. Our experiments demonstrate that our design can cut the communication cost to half compared to the most recent Hyperledger Fabric platform. © 2024 IEEE.

关键词： Decentralized control

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Fractional order for Multi-criteria control performance Assessment 12

Fractional order for Multi-criteria Control Performance Asse...

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12th IFAC conference on Fractional Differentiation and its Application (ICFDA)

作者： Domanski, Pawel D. Warsaw Univ Technol Inst Control & Computat Engn Ul Nowowiejska 15-19 PL-00665 Warsaw Poland

This paper presents an alternative way to do control performance assessment (CPA). The task to measure controller quality has to meet contradictory goals. Generally, the tuning of any controller means reaching a compromise between the accuracy and speed. The demanded ratio between them depends on the process requirements, technological limitations and just the engineering skills. Two fundamental indexes, like the overshoot and settling time fit perfectly into the picture. Considered investigations follow this research path, but with the use of modern measures: L-moments, tail index and ARFIMA filter fractional order estimator. The assessment uses two dimensional Index Ratio Diagrams (IRD), which allow to present and compare contradictory measures in a single diagram. They also allow to define new multi-criteria index able to compare different loops. The validation is compared against commonly used integral measures: means square and absolute errors. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： control performance assessment fractional order L-moments tail index ARFIMA

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Enhancing Cybersecurity Through Machine Learning-Based Classification of IoT network Traffic

Enhancing Cybersecurity Through Machine Learning-Based Class...

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2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Ravi Teja, S. Janardhana, D.R. Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350315455

An exponential increase in smart devices connected to the internet leads to progress in the growth of internet of Things (IoT) technology that has become integral to part of our daily lives. IoT plays a pivotal role in manufacturing, enabling companies to monitor machine statuses in real-time, assess product quality, and track environmental variables within factories. This not only empowers managers to mitigate risks and minimize losses but also facilitates decision-making from a broader operational perspective. However, the proliferation of IoT also introduces security and safety concerns, making anomaly detection crucial for IoT networks. Detecting anomalies promptly and alerting users is essential to prevent potential damage or losses. In this work, utilizing the Machine Learning and Deep Learning techniques for detecting anomalies in the IoT network and the dataset used to conduct the experimentation on IoT-23 dataset. Finally, identifying the best model for detecting the anomalies in the IoT network by considering the performance of the model and its training time of different models. In the proposed machine and deep learning models, decision tree outperformed with other models in detecting the anomalies with an accuracy of 73% and model time of 7 seconds. © 2023 IEEE.

关键词： and Naive Bayes Decision Tree internet of Things (IoT) Machine Learning (ML) Random Forest Support Vector Machine (SVM)

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Remote Monitoring and control of Battery Management System using CAN Communication and IoT Device for Cloud Based Analysis and Management 7

Remote Monitoring and Control of Battery Management System u...

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7th IEEE conference on Information and Communication Technology, CICT 2023

作者： Earthineni, Nirupam Sivavenkateswara Rao, V. Indian Institute of Information Technology Electronics and Communication Engineering Surat India

ISBN: (纸本)9798350305173

In this research article, two methods suitable for remote monitoring and control of battery management system (BMS), respectively are proposed. The methods use controller area network (CAN) communication and internet of things (IoT) device for cloud-based analysis and management. The proposed method has several advantages over traditional onboard BMS systems, such as remote monitoring and control, scalability, analytics, and flexibility. Additionally, it facilitate various features like optimized battery performance, reduce maintenance costs, and improve safety and reliability in electric vehicles and other applications. With the necessary hardware and software components, the proposed methods are tested, and the results are presented. © 2023 IEEE.

关键词： internet of things

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Intelligent Manufacturing Flexibility Scheduling Optimization Based on Hybrid NSGA-II Algorithm 7

Intelligent Manufacturing Flexibility Scheduling Optimizatio...

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7th International conference on Mechatronics, Robotics and Automation

作者： Hu, Zhuoya Liu, Jun Xu, Ji Ye, Chaoqian Wuhan Inst Engn Sch Comp Sci & Engn Wuhan 430205 Peoples R China Key Lab Biomarkers & In Vitro Diag Translat Zheji Hangzhou 310063 Peoples R China Hubei Cent China Technol Dev Elect Power Co Ltd Hubei Engn Res Ctr Intelligent Digital Technol Ne Wuhan 430074 Peoples R China

ISBN: (纸本)9798350352481;9798350352474

The flexible job-shop problem is one of the classical problems that have attracted much attention in the field of industrial automation and control. For the Multi-Objective Flexible Job-shop Scheduling Problem (MOFJSP), this paper proposes a hybrid particle swarm Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem. The high-quality solution of the problem is obtained through particle swarm optimization as part of the initial population of NSGA-II, and an elite strategy is used to prevent the loss of outstanding individuals by mixing all the individuals of the parent and the offspring in a non-dominated sorting method. This approach not only facilitates automation in manufacturing and production but also enhances the control and optimization of complex systems. Furthermore, integrating intelligent transportation systems and the internet of Things (IoT) into the scheduling process ensures real-time fault diagnosis and fault-tolerant control, thereby improving the overall efficiency and reliability of the production system. The experimental results show that the hybrid optimization approach combining PSO and NSGA-II exhibits superior performance in solving MOFJSP. This research contributes to the advancement of cyber-physical systems and the automation of complex industrial processes.

关键词： flexible job shop scheduling NSGA-II PSO industrial automation control and optimization

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Adaptive Neural network Based Monitoring of Wastewater Treatment Plants

Adaptive Neural Network Based Monitoring of Wastewater Treat...

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American control conference (ACC)

作者： Alharbi, Mohammed S. Hong, Pei-Ying Kirati, Taous-Meriem Laleg King Abdullah Univ Sci & Technol Elect & Comp Engn Dept Thuwal Saudi Arabia King Abdullah Univ Sci & Technol KAUST Biol & Environm Sci & Engn Div BESE Thuwal 239556900 Saudi Arabia

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

The quality of the treated wastewater is conditioned by the performance of wastewater treatment processes. However, real-time monitoring of quality variables in wastewater treatment plants (WWTP) is a challenging problem. In this paper, an adaptive online monitoring approach that is based on long short term memory (LSTM) neural network is proposed to estimate the bacterial concentration, mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) in WWTP. Due to the lack of a large dataset and difficulties in measuring quality variables, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is designed to generate synthetic data for training. Tuned hyperparameters are obtained for the proposed method. In addition, the performance is compared with the traditional LSTM using two datasets. Finally, the results indicate that WGAN successfully generates realistic training samples and quality variables are monitored with satisfactory performance.

关键词： Training Adaptive systems Microorganisms Neural networks Water quality Solids Real-time systems

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PPO-Based PDACB Traffic control Scheme for Massive IoV Communications

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第1期24卷 1116-1125页

作者： Zhang, Haijun Jiang, Minghui Liu, Xiangnan Wen, Xiangming Wang, Ning Long, Keping Univ Sci & Technol Beijing Beijing Engn & Technol Res Ctr Convergence Networ Beijing 100083 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Sch Informat & Commun Engn Beijing 100876 Peoples R China Zhengzhou Univ Sch Informat Engn Henan Joint Int Res Lab Intelligent Networking & Zhengzhou 450001 Peoples R China

Traffic control is regarded as a key issue to alleviate congestion in internet of vehicles (IoV) machine-type communications (MTC). Recently, many traffic control schemes have been studied, such as access class barring (ACB) scheme and back-off (BO) scheme. However, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid scheme, combining the priority dynamic ACB (PDACB) scheme and BO scheme. The IoV devices are classified depending on different delay characteristics, where the delay-sensitive devices are classified as high priority. The target is to maximum the successful transmission of packets with the success rate constraint by adjusting the various ACB factors. Proximal policy optimization (PPO) algorithm as a unique deep reinforcement learning (DRL) method is utilized in this paper, which can obtain continuous action space and solve for the optimal ACB factors without estimating backlog of nodes. A quick convergence is achieved by designing sensible state space, action space and reward. The access capability of the PDACB traffic control scheme is verified by simulations.

关键词： Delays Heuristic algorithms Uplink performance evaluation Resource management quality of service Optimization internet of vehicles machine-type communications traffic control proximal policy optimization access class barring

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