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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Tziovani, Lysandros Hadjidemetriou, Lenos Timotheou, Stelios University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9789531842976

Battery energy storage systems (BESSs) are widely used in energy management applications to maximize arbitrage profits. The consideration of the power losses from both battery and inverter components in optimization schemes is vital to ensure the economic BESSs scheduling. Towards this direction, this work develops two optimization models that consider the combined inverter-battery efficiency. The first model represents the non-linear efficiency curve of the inverter using a piecewise linear approximation, resulting in mixed-integer linear programming formulations. The second model approximates the nonlinear efficiency curve using a constant term, resulting in linear programming formulations which can be fast and reliably solved. Moreover, an experimental investigation is performed to derive the efficiency curve of a real inverter-battery system, where the parameters of the two models are identified. Simulation results highlight the necessity to consider the efficiencies from both battery and inverter components to reduce modelling inaccuracies and hence to maximize profits. The results also show the capability of the linear programming model to yield high-quality solutions with an approximation error of less than 1%. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Increasing the Fairness of Photovoltaic Curtailments for Voltage Management in Distribution Grids

Increasing the Fairness of Photovoltaic Curtailments for Vol...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Kotsonias, Andreas Hadjidemetriou, Lenos Asprou, Markos Panayiotou, Christos G. University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9789531842976

The increasing penetration of photovoltaic systems (PV) in distribution grids can create significant over-voltage conditions due to intense reverse power flow. In addition to reactive power control, PV curtailments are often employed for voltage regulation to ensure compliance with the regulatory limits. However, reducing the output power of a PV system results to a revenue drop for the owners, raising concerns about fairness. Relevant works often address this issue through penalty terms in the objective function, but most works neglect the trade-off between fairness and total curtailments, i.e., increasing fairness leads to an increase of total curtailments. This paper proposes a novel methodology to enhance the fairness of PV curtailments for voltage regulation while ensuring that the increase in total curtailments is within a specified acceptable limit. In this direction, the Jain's fairness index is used to quantitatively measure fairness, which is incorporated in the optimization process as a second order cone program constraint. An iterative algorithm based on the bisection method is then developed to increase fairness until the allowable total curtailments are reached. Simulation results demonstrate the capability of the proposed method to improve the fairness of PV curtailments, considering different levels of allowable total curtailments. © 2024 IEEE.

关键词： DC distribution systems

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Efficient Global Neural Architecture Search

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SN computer Science 2025年第3期6卷 1-12页

作者： Siddiqui, Shahid Kyrkou, Christos Theocharides, Theocharis Electrical and Computer Engineering Department University of Cyprus Nicosia Cyprus KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

Neural architecture search (NAS) has shown promise towards automating neural network design for a given task, but it is computationally demanding due to training costs associated with evaluating a large number of architectures to find the optimal one. To speed up NAS, recent works limit the search to network building blocks (modular search) instead of searching the entire architecture (global search), approximate candidates’ performance evaluation in lieu of complete training, and use gradient descent rather than naturally suitable discrete optimization approaches. However, modular search does not determine network’s macro architecture i.e. depth and width, demanding manual trial and error post-search, hence lacking automation. In this work, we revisit NAS and design a navigable, yet architecturally diverse, macro-micro search space. In addition, to determine relative rankings of candidates, existing methods employ consistent approximations across entire search spaces, whereas different networks may not be fairly comparable under one training protocol. Hence, we propose an architecture-aware approximation with variable training schemes for different networks. Moreover, we develop an efficient search strategy by disjoining macro-micro network design that yields competitive architectures in terms of both accuracy and size. Our proposed framework achieves a new state-of-the-art on EMNIST and KMNIST, while being highly competitive on the CIFAR-10, CIFAR-100, and FashionMNIST datasets and being 2-4 times faster than the fastest global search methods. Lastly, we demonstrate the transferability of our framework to real-world computer vision problems by discovering competitive architectures for face recognition applications. © The Author(s) 2025.

关键词： AutoML Convolutional neural networks Deep learning Image classification Neural architecture search

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Near-Field Beam Training with Analog Extremely Large True-Time-Delay Arrays: (Invited Paper) 58

Near-Field Beam Training with Analog Extremely Large True-Ti...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Pehlivan, Ibrahim Ilter, Mehmet C. Valkama, Mikko Cabric, Danijela University of California Electrical and Computer Engineering Department Los Angeles United States Tampere Wireless Research Center Tampere University Department of Electrical Engineering Finland

ISBN: (纸本)9798350354058

As wireless systems have started to use millimeter-wave (mmWave) frequencies to exploit the abundant bandwidth, large antenna arrays with high beamforming gain have become necessary for compensating severe path loss. However, deploying large number of antennas extends the near-field (NF) region where effective beamforming requires the knowledge of not only the user equipment (UE)'s direction, but also distance. In this paper, we present a fast NF beam training method for analog extremely large (XL) true-time-delay (TTD) arrays using only a single orthogonal frequency-division multiplexing (OFDM) pilot symbol. In the proposed virtual sub-array sparse rainbow (VSSR) algorithm, we virtually partition the antenna array into smaller aperture sub-arrays so that a UE falls in the far-field region of each sub-array. By exploiting properties of the rainbow beamforming, we obtain near-orthogonal sub-array measurements such that UE directions with respect to each sub-array can be estimated with a sparse recovery algorithm. Then, these sub-array referenced UE directions are combined to estimate UE's location. Our simulation results demonstrate that the proposed method enables near-optimal beam training using only a single OFDM symbol, achieving an average of at least the 95% of the optimal array gain under various channel conditions. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

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A Survey of Cyber Attacks on Cyber Physical Systems:Recent Advances and Challenges

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 784-800页

作者： Wenli Duo MengChu Zhou Abdullah Abusorrah Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China IEEE Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center and Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz University

A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over *** plays a key role in modern industry since it connects physical and cyber *** order to meet ever-changing industrial requirements,its structures and functions are constantly ***,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and *** paper presents a survey of state-of-the-art results of cyber attacks on cyber physical ***,as typical system models are employed to study these systems,time-driven and event-driven systems are ***,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are *** particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and ***,both attack and defense strategies are discussed based on different system *** challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

关键词： Attack detection attack strategy cyber attack cyber physical system(CPS) secure control

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Dynamic Task Offloading in Connected Vehicles: Leveraging a Graph Neural Networks Approach for Multi-hop Search

Dynamic Task Offloading in Connected Vehicles: Leveraging a ...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Malik, Asad Waqar Khan, Samee U. Mississippi State University National Strategic Planning and Analysis Research Center Department of Electrical and Computer Engineering United States Mississippi State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350367942

The vehicular edge computing model provides computational support to nearby vehicles requiring low-latency computation. However, existing algorithms typically only consider immediate neighbor nodes as potential vehicles for computation offloading. In this study, we address this limitation by modeling the task offloading problem using a Graph Neural Network approach. This approach incorporates dynamic properties of vehicles and edges, facilitating a more effective selection of vehicles for task offloading. By leveraging message passing and aggregate feature techniques, our proposed approach expands the search space for source vehicles, thereby distributing the workload across a wider spectrum, particularly benefiting vehicles that frequently generate computational tasks. Evaluation of our proposed system demonstrates significant improvements in the utilization factor of available vehicles, a reduction in queuing states, and an overall system efficiency of 92%. © 2024 IEEE.

关键词： Computation offloading

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Defect Analysis for FeFETs using a Compact Model

Defect Analysis for FeFETs using a Compact Model

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2024 IEEE International Test Conference, ITC 2024

作者： Thapar, Dhruv Chaudhuri, Arjun Ni, Kai Chakrabarty, Krishnendu School of Electrical Computer and Energy Engineering AZ United States Asu Center for Semiconductor Microelectronics United States University of Notre Dame Department of Electrical Engineering Notre DameIN United States

ISBN: (纸本)9798331520137

Ferroelectric field-effect transistors (FeFETs) are a promising emerging non-volatile memory, but the impact of manufacturing imperfections on these devices has yet to be studied comprehensively. We extend a previous FeFET compact model to combine the Preisach ferroelectric capacitor model with the BSIM-SOI MOSFET model. We calibrate this new compact model with data from a technology CAD (TCAD) model that is calibrated against a fabricated metal-ferroelectric-metal capacitor. We analyze polarization defects in the ferroelectric layer using this compact model. We address two classes of defects and map them to stuck-at-fault models, referred to as neutral faults (SAP0), and stuck-at-plus and stuck-at-minus (SAP+ and SAP-) faults. This framework obviates the need for computationally expensive TCAD simulations for each defect scenario. © 2024 IEEE.

关键词： Nonvolatile storage

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A Context-Aware Readout System for Sparse Touch Sensing Array Using Ultra-Low-Power Always-On Event Detection

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IEEE Transactions on Circuits and Systems II: Express Briefs 2023年第9期70卷 3719-3723页

作者： Roh, Hyeri Choi, Woo-Seok Seoul National University Department of Electrical and Computer Engineering Inter-University Semiconductor Research Center Seoul08826 Korea Republic of

Increasing demand for larger touch screen panels (TSPs) places more energy burden to mobile systems with conventional sensing methods. To mitigate this problem, taking advantage of the touch event sparsity, this brief proposes a novel TSP readout system that can obtain huge energy saving by turning off the readout circuits when none of the sensors are activated. To this end, a novel ultra-low-power always-on event and region of interest detection based on lightweight compressed sensing is proposed. Exploiting the proposed event detector, the context-aware TSP readout system, which can improve the energy efficiency by up to 42×, is presented. © 2004-2012 IEEE.

关键词： Tactile sensors

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Perfomance comparison of Machine Learning (ML) based fault detection scheme for a compensated transmission line

Perfomance comparison of Machine Learning (ML) based fault d...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Mazibuko, N. Akindeji, K.T. Moloi, K. University of Technology Smartgrid Research Center Durban Department of Electrical Power Engineering Department of Electronics and Computer Engineering Durban South Africa

ISBN: (纸本)9798350389388

Compensated transmission lines are employed in power system networks to increase energy transmission capacity, improve system stability, decrease transmission losses, and provide more flexibility in power management. However, the effectiveness of protection relays in detecting, classifying, and locating faults in such networks is significantly impacted. This is due to changes in impedance within the fault loop caused by devices used in compensated networks, which alters the apparent impedance seen by the relay. This study introduces three different fault detection and classification schemes for compensated transmission lines. Initially, an IEEE 9-bus system with series compensation at line 4-6 is utilized for data collection. Feature extraction using Principal Component Analysis (PCA) is then conducted, followed by the implementation of fine k-nearest neighbors (KNN), Medium Nuero network (MNN), and SQVM classifiers. A comprehensive performance analysis is carried out, revealing that SQVM achieves the highest overall accuracy of 99.3%, while the fine tree outperforms SQVM in single-line fault detection. © 2024 IEEE.

关键词： Power transmission lines

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