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Sensitivity-based Vulnerability Assessment of State Estimation

Journal of Modern Power systems and Clean Energy 2021年第4期9卷 886-896页

作者： Gonzalo E.Constante-Flores Antonio J.Conejo Jiankang Wang Department of Electrical and Computer Engineering The Ohio State UniversityColumbusUSA Department of Integrated Systems Engineering and the Department of Electrical and Computer Engineering The Ohio State UniversityColumbusUSA

We propose a technique to assess the vulnerability of the power system state estimation. We aim at identifying the measurements that have a high potential of being the target of false data injection attacks. From the perspective of the adversary, such measurements have the following characteristics: ① being influential on the variable estimates;② corrupting their measured values is likely to be undetected. Additionally, such characteristics should not change significantly with the system operation condition. The proposed technique provides a systematic way of identifying the measurements with such characteristics. We illustrate our methodology on a 4-bus system, the New England 39-bus system, and the IEEE 118-bus test system, respectively.

关键词： False data injection attack state estimation sensitivity analysis singular value decomposition

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Cooperative nonlinear distributed model predictive control with dissimilar control horizons 63

Cooperative nonlinear distributed model predictive control w...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Chanfreut, Paula Maestre, Jose M. Zhu, Quanyan Heemels, W.P.M.H. Maurice Eindhoven University of Technology Department of Mechanical Engineering Netherlands University of Seville Department of Systems and Automation Engineering Spain New York University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

In this paper, we introduce a nonlinear distributed model predictive control (DMPC) algorithm, which allows for dissimilar and time-varying control horizons among agents, thereby addressing a common limitation in current DMPC schemes. We consider cooperative agents with varying computational capabilities and operational objectives, each willing to manage varying numbers of optimization variables at each time step. Recursive feasibility and a non-increasing evolution of the optimal cost are proven for the proposed algorithm. Through numerical simulations on systems with three agents, we show that our approach effectively approximates the performance of traditional DMPC, while reducing the number of variables to be optimized. This advancement paves the way for a more decentralized yet coordinated control strategy in various applications, including power systems and traffic management. © 2024 IEEE.

关键词： Predictive control systems

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Wheat Field Fire Smoke Detection from UAV Images using CNN-CBAM 2

Wheat Field Fire Smoke Detection from UAV Images using CNN-C...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alsalem, Ahmed Zohdy, Mohamed Oakland University Rochester Department of Industrial and Systems Engineering MI United States Oakland University Rochester Department of Electrical and Computer Engineering MI United States

ISBN: (纸本)9798331529529

Wheat field fires pose a significant threat to wildlife habitats, agricultural resources, and especially farmer incomes. The frequency of these fires has increased noticeably in recent years, likely due to human activity in agricultural areas and the exacerbating effects of climate change. Rapid action is crucial once smoke is detected, as it marks the first step in early fire detection. To address this challenge, we propose an enhanced CNN model specifically designed to identify smoke in wheat field fires. As part of this approach, a dataset of 3900 UAV images was compiled to depict smoke from wheat field fires. The proposed work also integrates the CBAM into the CNN to improve the model's ability to extract features for smoke patch characteristics of wheat field fires. Furthermore, the CNN architecture includes decoupled heads to extract relevant data from various sources. Testing on our wheat field fire smoke dataset has demonstrated the effectiveness of this proposed method, achieving an accuracy of up to 0.91 and outperforming the previous model. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Machine Learning-Based Channel State Estimators for 5G Wireless Communication systems

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computer Modeling in engineering & Sciences 2023年第4期135卷 755-778页

作者： Mohamed Hassan Essai Ali Fahad Alraddady Mo’ath Y.Al-Thunaibat Shaima Elnazer Department of Electrical Engineering Faculty of EngineeringAl-Azhar UniversityQena83513Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Management Information Systems Taif UniversityTaif21944Saudi Arabia

For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state ***,it utilizes pilots to offer more helpful information about the communication *** proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based *** CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based *** three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based *** BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)*** addition,the computational and learning time complexities for DNN-CSEs are *** estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.

关键词： DLNNs channel state estimator 5G and beyond communication systems robust loss functions

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An IoMT-Based Incremental Learning Framework with a Novel Feature Selection Algorithm for Intelligent Diagnosis in Smart Healthcare

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 370-383页

作者： Sai, Siva Bhandari, Kartikey Singh Nawal, Aditya Chamola, Vinay Sikdar, Biplab Department of Computer Science and Information Systems Pilani Campus Pilani333031 India Microsoft Campus Gachibowli Telangana Hyderabad500032 India Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India Pilani Campus Pilani333031 India National University of Singapore Department of Electrical and Computer Engineering Cluny Road Singapore117583 Singapore

Several recent research papers in the Internet of Medical Things (IoMT) domain employ machine learning techniques to detect data patterns and trends, identify anomalies, predict and prevent adverse events, and develop personalized patient treatment plans. Despite the potential of machine learning techniques in IoMT to revolutionize healthcare, several challenges *** conventional machine learning models in the IoMT domain are static in that they were trained on some datasets and are being used for real-time inferencing data. This approach does not consider the patient's recent health-related data. In the conventional machine learning models paradigm, the models must be re-trained again, even to incorporate a few sets of additional samples. Also, since the training of the conventional machine learning models generally happens on cloud platforms, there are also risks to security and privacy. Addressing these several issues, we propose an edge-based incremental learning framework with a novel feature selection algorithm for intelligent diagnosis of patients. The approach aims to improve the accuracy and efficiency of medical diagnosis by continuously learning from new patient data and adapting to patient conditions over time, along with reducing privacy and security issues. Addressing the issue of excessive features, which might increase the computational burden on incremental models, we propose a novel feature selection algorithm based on bijective soft sets, Shannon entropy, and TOPSIS(Technique for Order Preference by Similarity to Ideal Solution). We propose two incremental algorithms inspired by Aggregated Mondrian Forests and Half-Space Trees for classification and anomaly detection. The proposed model for classification gives an accuracy of 87.63%, which is better by 13.61% than the best-performing batch learning-based model. Similarly, the proposed model for anomaly detection gives an accuracy of 97.22%, which is better by 1.76% than the best-performing b

关键词： Patient treatment

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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC systems

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 34-48页

作者： Hammadi, Ahmed Al Bariah, Lina Muhaidat, Sami Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Debbah, Merouane Technology Innovation Institute Abu Dhabi United Arab Emirates Khalifa University Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaK1S 5B6 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere33014 Finland Khalifa University KU 6G Center Abu Dhabi United Arab Emirates

Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain's Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance. © 2023 The Authors.

关键词： Spectrum efficiency

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Deep Learning-Based Strategies for Integrated Autonomous Navigation: A Review

Deep Learning-Based Strategies for Integrated Autonomous Nav...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Fekry, Alaa Kamel, Ahmed M. Elhalwagy, Yehia Abosekeen, Ashraf Military Technical College Department of Electrical and Computer Engineering Cairo Egypt October University for Modern Sciences and Arts Department of Mechatronics Systems Engineering Giza Egypt

ISBN: (纸本)9798350351408

Multi-sensor integrated navigation/positioning systems (MSINPS) play a crucial role in achieving precise positioning and navigation in challenging environments. By combining data from various sensors such as Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cameras, and Light Detection and Ranging (LiDar), MSINPS enhance accuracy and robustness, ensuring uninterrupted operation even in the event of sensor malfunction. This paper explores autonomous vehicle guidance for safe and efficient navigation, focusing on two paths of data fusion: sensor-based fusion and learning-based fusion. The effectiveness of MSINPS hinges on meticulous sensor selection and optimization to meet the requirements of autonomous navigation applications. Addressing noise, biases, and inconsistencies through data pre-processing is essential for robust data fusion. Advanced fusion algorithms leveraging machine learning and deep learning techniques exhibit superior accuracy and adaptability. Furthermore, integrating MSINPS with artificial intelligence (AI) opens new avenues for navigation and positioning enhancement. This integration can lead to improvements in sensor technology and solution outcomes. Striking a balance between computational complexity and available resources promotes efficient operation while implementing fault tolerance strategies ensures system robustness across diverse environments. © 2024 IEEE.

关键词： Global positioning system

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Tiny-RFNet: Enabling Modulation Classification of Radio Signals on Edge systems 5

Tiny-RFNet: Enabling Modulation Classification of Radio Sign...

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5th CPSSI International Symposium on Cyber-Physical systems (Applications and Theory), CPSAT 2024

作者： Chegini, Mohammad Abdollahi, Meisam Baniasadi, Amirali Patooghy, Ahmad University of Victoria Department of Electrical & Computer Engineering Victoria Canada North Carolina A&T State University Department of Computer Systems Technology NC United States

ISBN: (纸本)9798331529284

Automatic Modulation Classification (AMC) is a key task to identify the type of modulation used to Radio Frequency (RF) communications. Conventional AMC methods are often computationally expensive and inaccurate making them not applicable for Edge systems. In this paper, we propose Tiny-RFNet, a novel deep learning model that achieves high performance and low resource consumption for AMC. Tiny-RFNet takes advantage of a novel multi-scale convolutional layer to extract robust features at different resolutions. The proposed network features the concept of Separable Convolution Blocks (SCB) to adjust the network complexity as needed. For further optimization for Edge, we implement different variants of Tiny-RFNet on Jetson Orin Nano as a representative of high performance edge devices. Obtained hardware results show that Tiny-RFNet fits resources available on Jetson Orin Nano i.e., enabling AMC at the Edge. Moreover, we investigate Tiny-RFNet's data efficiency and the impact of network depth on its accuracy, confirming that the different variants of Tiny-RFNet are data-efficient. © 2024 IEEE.

关键词： Signal modulation

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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics

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Microsystems & Nanoengineering 2025年第1期11卷 465-476页

作者： Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Center for Cyber-Physical Systems-System on Chip Lab Khalifa UniversityAbu Dhabi 127788UAE Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering Khalifa UniversityAbu Dhabi 127788UAE Division of Engineering New York UniversityAbu Dhabi 129188UAE Department of Mechanical and Nuclear Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Chemistry Khalifa UniversityAbu Dhabi 127788UAE

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing *** this study,we present an approach that integrates an iontronic fluidic memristive(IFM)device with low input impedance and a triboelectric nanogenerator(TENG)based on ferrofluid(FF),which has high input *** incorporating contact separation electromagnetic(EMG)signals with low input impedance into our FF TENG device,we enhance the FF TENG’s performance by increasing energy harvesting,thereby enabling the autonomous powering of IFM devices for self-powered ***,replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic *** fluidic devices,composed of soft-matter materials,dynamically adjust their conductance by altering the solution *** developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane(PDMS)structures,utilising a fluidic interface of FF and polyacrylic acid partial sodium salt(PAA Na^(+)).The confined ion interactions in this system induce hysteresis in ion transport across various frequencies,resulting in significant ion memory *** IFM successfully replicates diverse electric pulse patterns,making it highly suitable for neuromorphic ***,our system demonstrates synapse-like learning functions,storing and retrieving short-term(STM)and long-term memory(LTM).The fluidic memristor exhibits dynamic synapse-like features,making it a promising candidate for the hardware implementation of neural *** TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials,further enhanced by intricate chemical designs for self-powered electronics.

关键词： iontronic memristive ionic fluidic devices memcapacitive ff teng s ff teng fluidic triboelectric nanogenerator teng based

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NoCSNet: Network-on-Chip Security Assessment Under Thermal Attacks Using Deep Neural Network 17

NoCSNet: Network-on-Chip Security Assessment Under Thermal A...

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17th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2024

作者： Abdollahi, Meisam Chegini, Mohammad Hesar, Mahdi Hasanzadeh Javadinia, Samaneh Patooghy, Ahmad Baniasadi, Amirali University of Victoria Department of Electrical & Computer Engineering Victoria Canada North Carolina a&t State University Department of Computer Systems Technology NC United States

ISBN: (纸本)9798331506421

As the demand for high-performance computing continues to rise, Network-on-Chip (NoC) architectures play a crucial role in enabling efficient data transmission within complex systems. However, the sensitivity of NoCs to intentional thermal fluctuations opens doors to conducting Denial of Service (DoS) attacks that can alter the system's reliability and security. In this paper, for the first time, we introduce NoCSNet as a novel database of NoC traffic collected under various network configurations and thermal attack scenarios. We also use Deep Neural Networks (DNNs) to analyze the collected traffic to enhance data transmission security in the presence of thermal DoS attacks. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of NoCSNet in capturing the security profile of NoC architectures, which can be actively used in protecting NoCs' data integrity and stability against thermal DoS attacks. The experimental results indicate that among the MLP, LSTM, and RNN deep neural networks, the RNN approach provides the highest attack detection accuracy of 93.8%. We anticipate that the collected dataset will help the community develop a deeper understanding of the susceptibility of NoCs against thermal DoS attacks. © 2024 IEEE.

关键词： Denial-of-service attack

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