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检索条件"任意字段=Conference on Internet Performance and Control of Network Systems II"

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In-Vehicle network Anomaly Detection Using Extreme Gradient Boosting Machine 11

In-Vehicle Network Anomaly Detection Using Extreme Gradient ...

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + systems and internet of Things (CPS and IoT)

作者： Anjum, Afia Agbaje, Paul Hounsinou, Sena Olufowobi, Habeeb Univ Texas Arlington Arlington TX 76019 USA Univ Colorado Colorado Springs CO 80907 USA

ISBN: (纸本)9781665468282

Modern vehicles have significantly increased the number of internet of Things (IoT) devices called electronic control units (ECUs) connected by in-vehicle networks to provide enhanced features and safety. These devices communicate with the environment and have brought the notion of the internet of vehicles. The controller area network (CAN) bus facilitates efficient ECU communications and is the standard protocol used by every vehicle, but it is susceptible to remote attacks. Consequently, it is desirable to monitor the CAN bus for malicious activities, such as data injection attacks, that can compromise the vehicular operations. We present an anomaly detection technique that uses an extreme gradient boosting machine (GBM) learning algorithm to categorize unexpected occurrences in the CAN data payload. We further combine GBM with a ten-fold cross-validation method to improve prediction performance. Moreover, we use the early-stopping and grid search approaches to overcome overfitting without affecting model accuracy. We evaluate our detection approach on real CAN bus datasets collected from Hyundai Sonata, a KIA Soul, and Chevrolet Spark with different attack scenarios, such as Denial-of-Service (DoS), fuzzy, spoofing, and malfunction attacks. Using standard metrics, such as accuracy, recall, precision, F1 score, and false-positive rate, the performance analysis of the proposed model achieved an overall accuracy of over 99 percent.

关键词： Protocols Computational modeling Prediction algorithms Boosting Safety Sparks Anomaly detection

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Development of IoT Based controlled Bench Power Generation System

Development of IoT Based Controlled Bench Power Generation S...

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World conference on Information systems for Business Management, ISBM 2023

作者： Okokpujie, Imhade P. Kinggs, Kien-Olali A. Adetunla, Adedotun Okokpujie, Kennedy Awomoyi, Morayo E. Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado Ekiti Nigeria Department of Mechanical and Industrial Engineering Technology University of Johannesburg Johannesburg2028 South Africa Department of Electrical and Information Engineering Covenant University Ogun State Ota Nigeria School of International Service American University Washington United States

ISBN: (纸本)9789819986118

The need for automation in the power sector is growing as technological advancement increases. Automatic control is required for tasks like turning on and off a power supply unit (inverter, generator, etc.). Therefore, this project aims to develop an IoT-based controlled bench power generation system. The study employed php scripting language, MySQL, and other web technologies to develop an IoT-based control program to resolve the issue and create a web application for interaction with the bench power supply used in this project. The web application interacted with a database which was the driver responsible for sending the Boolean values for switching or off to the physical power supply controller. The experimental findings showed that the IoT-based controlled bench power generation system is viable and fast in operations. From the analysis of the results compared with the manually operated generator, the performance is excellent, with a 75% deviation. The average time to start the IoT system is 28.28 s, and the manually operated power system with 261.6 s. This made it possible to develop a web application that, depending on the Wi-Fi network's availability, allowed users to manage a bench power supply from various locations with very little time and ease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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A Lithium Battery SOC Estimation Method Based on AEKF-Composite Neural network Hybrid Driven Model 7

A Lithium Battery SOC Estimation Method Based on AEKF-Compos...

引用

7th IEEE conference on Energy internet and Energy System Integration, EI2 2023

作者： Dong, Cun Li, Peng Li, Kecheng Sang, Bingyu Ge, Ruzhe Wang, Shibo Pan, Youpeng Wang, Jiahao National Electric Power Dispatching and Control Center Beijing China School of Electrical and Electronic Engineering North China Electric Power University Baoding China China Electric Power Research Institute Nanjing China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350345094

Lithium-ion battery State of Charge signifies the battery's remaining usable capacity and significantly impacts the stability and efficiency of new energy systems. Accurate State of charge (SOC) estimation is critical as renewable energy integration into the grid increases. This study explores a method for precise SOC estimation in lithium-ion batteries. We utilize the adaptive extended Kalman filter algorithm (AEKF), a model-based approach that adjusts noise covariance matrices based on measurement residuals, enhancing both accuracy and robustness in SOC estimation. Compared to the conventional extended Kalman filter, which assumes constant noise covariance matrices, the adaptive version demonstrates superior SOC estimation performance. Additionally, we introduce a self-correcting composite neural network prediction module, a data-driven technique that improves SOC estimation accuracy and robustness. It leverages historical data and model-based estimations to train a neural network capable of correcting estimation errors. Our proposed method combines the strengths of both model-based and data-driven approaches, and experimental results confirm its effectiveness and superiority. © 2023 IEEE.

关键词： Lithium-ion batteries

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High performance of Smart Refrigerator System Based on IoT Technique 16

High Performance of Smart Refrigerator System Based on IoT T...

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16th International conference on Developments in esystems Engineering, DeSE 2023

作者： Alsanad, Hamid R. Al-Askari, Mohanad A. Omar, Khaldoon A. Almashhadany, Yousif Algburi, Sameer Yaseen, Taisir A. University of Anbar Department of Electrical Engineering Anbar Iraq University of Anbar Information Systems Department Anbar Iraq Al-Kitab University College of Engineering Kirkuk Iraq University of Anbar Computer Network Department Anbar Iraq

ISBN: (纸本)9798350381344

The internet of Things (IoT) has made a significant impact on improving human life in the modern era, offering convenience and ease. Smart appliances, which are connected to the internet, have become increasingly popular due to their improved efficiency, robustness, power consumption, and connectivity. The main objective of this paper is to convert a conventional refrigerator into an intelligent appliance by integrating computational intelligence using a microcontroller and a range of sensors. The proposed approach aims to improve food management capabilities by introducing automated techniques and cost-effective intelligence to the refrigerator. The resulting smart refrigerator is designed to be user-friendly and improve the overall quality of life. The system includes a mobile or web application that acts as a bridge between the refrigerator and the user, allowing for remote control and monitoring. The smart refrigerator employs sensors to detect shortages of items and can automatically place orders online or send messages to grocery stores for item delivery. Additionally, the smart module continuously monitors temperature and humidity levels, uploading real-time updates to a cloud platform for user access. The integration of IoT technology into refrigerators offers numerous features and benefits, ultimately enhancing convenience and efficiency in managing food storage and consumption. © 2023 IEEE.

关键词： internet of things

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A New Dataset for Intrusion Detection in Industrial control System: A Gas Pipeline Testbed Study 21

A New Dataset for Intrusion Detection in Industrial Control ...

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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： Deng, Longmin Zhang, Xuemin Zheng, Qianrong Zhao, Dongdong Zhou, Junwei Xiang, Jianwen Wuhan University of Technology Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9798350329223

The security of Industrial control systems (ICS) is a critical issue that has gained increasing attention in recent years. Machine learning based intrusion detection systems (IDS) have shown promise in detecting previously unknown attacks, but their performance depends heavily on the quality of the dataset. However, most existing datasets in the field are either outdated or do not reflect the specific characteristics of ICS. We construct a dataset of ICS network traffic collected from a gas pipeline testbed, which covers a range of normal and abnormal events specific to the domain. We detail our methodology for constructing the dataset based on the gas pipeline testbed. Moreover, the systematic and fundamental comparative experiments are performed, based on the collected dataset. Specifically, we evaluate the performance of five supervised machine learning models on it while comparing their results. Our experimental results show that the dataset can serve as a valuable resource for data-driven IDS research and that Random Forest is an effective model for detecting anomalies in ICS data. © 2023 IEEE.

关键词： Intrusion detection

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A Highly Nonlinear Survival network for Hospital Readmission Prediction of Cardiac Patients 10

A Highly Nonlinear Survival Network for Hospital Readmission...

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10th International conference on internet of Things, Big Data and Security, IoTBDS 2025

作者： Zhai, Yuejing Li, Yiping He, Lihua Luo, Wuman Macao Polytechnic University China Macau University of Science and Technology China

ISBN: (纸本)9789897587504

Hospital readmission prediction of cardiac patients is an increasingly important survival analysis problem these days. So far, three groups of methods for cardiac readmission have been proposed: statistical-based, machine learning-based and deep learning-based. However, the assumptions of the statistical-based methods limit their practicality in real-world applications. The traditional machine learning-based methods suffer from the problem of over-reliance on feature engineering. Deep learning-based methods can be further classified into two groups in terms of how they deal with first hitting times: discrete strategy-based and continuous strategy-based. It is nontrivial for the discrete strategy-based methods to find the optimal granularity of output time intervals. The continuous strategy-based methods assume nonlinear proportional hazards condition, which often limits the model performance in practical applications. Besides, existing deep learning-based methods still have room for improvement in calculating the mean value of fitted dropout models. To address these issues, in this paper, we propose a highly nonlinear survival network called Environment-Aware Maxout Deep Survival Neural network (EMaxSurv) to predict the risk value of hospital readmission of cardiac patients. EMaxSurv is based on a key observation that environmental conditions have a significant impact on the health of cardiac patients. The basic idea of EMaxSurv is to adopt maxout deep networks combined with environmental information to better capture the relationship between covariates and the distribution of the first-hitting times. To evaluate the proposed model, we conduct extensive experiments on three real world datasets. The experimental results show that EMaxSurv outperforms the other baselines in all three datasets. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Deep neural networks

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Development of a Remote Experiment for Practical Work in Physics at the University: The Case of Elastic Pendulum 20th

Development of a Remote Experiment for Practical Work in Phy...

引用

20th International conference on Remote Engineering and Virtual Instrumentation: Open Science in Engineering, REV 2023 co-organized with the International Edunet World conference, IEWC 2023

作者： Laouina, Zineb Ouchaouka, Lynda Mordane, Soumia Moussetad, Mohamed Radid, Mohamed Physics Department LIMAT Laboratory Hassan II University Casablanca Morocco Languages and Sciences of Education Department LMSEIF Laboratory Hassan II University Casablanca Morocco Physics Department LPPSMM Laboratory Hassan II University Casablanca Morocco Chemistry Department Hassan II University Casablanca Morocco

ISBN: (纸本)9783031424663

Laboratory experiments play a significant role in science and engineering education within university settings. Traditional hands-on labs provide a natural environment for enhancing practical skills. However, with the advent of Information and Communication Technology (ICT), virtual and remote labs have emerged as pivotal components in the fields of Science, Technology, Engineering, and Mathematics (STEM). In particular, this white paper focuses on enabling remote access to authentic lab equipment, leveraging cutting-edge computing and networking technologies to establish an environment where remote users can conduct the necessary hands-on experiments in the field of physics. The principal objective of this endeavor is to develop an interactive system that empowers first-year university students in the Physical Chemistry department to control and conduct remote hands-on physics experiments. This paper concentrates on the conception and implementation of a prototype, enabling students to access the experimental facilities of the laboratory via an internet network. This newfound flexibility eradicates temporal and spatial limitations, enhances student safety during hazardous experiments, and concurrently diminishes the costs associated with lab equipment procurement and maintenance. The primary focus of our research is centered on the development of an elastic pendulum, employing both static and dynamic methodologies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： elastic pendulum practical experiments Remote lab

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Advanced Battery Energy Storage System Employing Cloud-based Platform 8

Advanced Battery Energy Storage System Employing Cloud-based...

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8th Southern Power Electronics conference and the 17th Brazilian Power Electronics conference, SPEC / COBEP 2023

作者： Gonçalves, Diuary Soares Callegari, João Marcus De Barros, Rodrigo Cássio Cupertino, Allan Fagner Pereira, Heverton Augusto CEFET-MG Department of Electrical Engineering Belo Horizonte Brazil Universidde Federal de Minas Gerais Department of Electrical Engineering Belo Horizonte Brazil Universidade Federal Do Recôncavo da Bahia Department of Electrical Engineering Cruz das Almas Brazil Universidade Federal de Juiz de Fora Department of Electrical Energy Juiz de Fora Brazil Universidade Federal de Viçosa Department of Electrical Engineering Viçosa Brazil

ISBN: (纸本)9798350321128

This work proposes an upgrade from a conventional Battery Energy Storage System (BESS) to an advanced BESS. The employed hierarchical three-level control architecture improves the system capabilities, allowing the advanced BESS to execute different ancillary services. The internet of Things (IoT)-based network also allows remote commands by external agents. The connection between the BESS and the cloud ensures operational reliability and enables advanced cloud-based features. Experimental tests showed suitable communication with the broker platform under the BESS charging procedure. Additionally, a second experiment was conducted over long periods of time to demonstrate satisfactory performance of the IoT network. © 2023 IEEE.

关键词： Auxiliary systems Battery Energy Storage System internet of Things Low-Cost Smart systems

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Artificial Neural network controller for DC-DC Boost Converter: A Design and performance Analysis

Artificial Neural Network Controller for DC-DC Boost Convert...

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Smart Power & internet Energy systems (SPIES), International conference on

作者： Amulya Viswambharan Rachid Errouissi KSP Kiranmai Mahdi Debouza Dept. of Electrical Engineering United Arab Emirates University Al Ain UAE

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

This study introduces an artificial neural network (ANN)-based machine learning controller for the DC-DC boost converter. The primary controller is a Disturbance Observer based Feedback Linearization controller, which serves as an expert to provide training data for the proposed ANN. After fine-tuning the ANN is seamlessly integrated into the feedback loop, directly facilitating boost converter control. The key advantage is in the ANN ability to enhance system identification, reduce model errors, and accommodate uncertain parameters. MATLAB/Simulink simulations validate the high performance of the ANN controller, showcasing its capability to follow dynamic reference commands fast, maintain output stability amidst input voltage variations, and effectively handle constraints on maximum duty-ratio and current.

关键词： Training systems operation Training data Artificial neural networks Mathematical models Stability analysis Feedback linearization System identification Voltage control Adaptive control

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Resource Allocation of Large-Scale Communication network based on Machine Learning

Resource Allocation of Large-Scale Communication Network bas...

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Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： Zhuxin Zhang Ruifeng Mo Xiaoqi Hou Xin Wei Suzhen Guo North Automatic Control Technology Research Institute Taiyuan China

With the continuous development of mobile internet technology, its demand for network performance is also increasing. Although the traditional multi-level structure of network resources enhances the scalability and independence of the network, it cannot be used effectively, nor can it intelligently configure network resources. To this end, this paper explores a new network resource allocation method, namely, a machine learning-based large-scale communication network resource allocation method. This paper first explains the concepts of communication network resource allocation and machine learning, and then proposes the principle of communication network resource allocation based on machine learning. Finally, through experiments, this paper analyzes that the bandwidth utilization rate, data delay and packet loss rate of the communication network resource allocation based on the method in this paper are 78%, 5ms and 0.05%, respectively, and the comprehensive performance is better than other communication network resource allocation methods.

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