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2023 International Conference on Decision Aid sciences and Applications, DASA 2023

作者： Kahina, Rekkal Sara, Rekkal Salhi Ahmed University Center Naama Department of Electrical Engineering Naama Algeria University of Badji Mokhtar Annaba Department of Computer Science Annaba Algeria

ISBN: (纸本)9798350342055

In our research, we employ the multicriteria decision-making (MCD) method, which incorporates Euclidean distance, the creation of ideal and anti-ideal profiles, and the weighting of criteria to express preferences. This approach makes it easier to make well-informed choices among several, frequently competing alternatives. Our goal is to choose the best storage battery among four different varieties, including AGM lead-acid, GEL lead-acid, lithium, and nickel, taking into account aspects like cost, performance, and depth of discharge. We may objectively compare the benefits and drawbacks of each kind of battery by using the well-known TOPSIS approach. Instead of pursuing an ideal solution, which may be elusive, our strategy tries to help decision-makers reach an agreeable compromise. Our research helps to improve the integration of renewable energy sources into storage systems, where storage batteries are essential in stabilising intermittent solar and wind energy production. © 2023 IEEE.

关键词： Decision making

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UAV-mounted Intelligent Reflecting Surface (IRS) MISO Communications

UAV-mounted Intelligent Reflecting Surface (IRS) MISO Commun...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Li, Kaixin Zhao, Ke Khan, Muhammad Fawad Ho, Pin-Han Peng, Limei School of Computer Science and Engineering Kyungpook National University Deagu Korea Republic of University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

This paper proposes using the unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) as a flying relay to enable the communication between the base station (BS) and suburb access points (APs). Since the UAV flight endurance is mainly affected by the weight of the IRS mounted on it, it is significant to reduce the IRS weight by minimizing the number of its reflecting elements. Regarding this, we propose an Optimized Phase Shift (OPS) algorithm to adjust the phase shift of the IRS reflecting elements, aiming to minimize the number of IRS reflecting elements required to successfully serve the APs while satisfying the signal-to-noise ratio (SNR) requirement. © 2022 IEEE.

关键词： Signal to noise ratio

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Relation Between electrical Grid Congestion and Bus Characteristics

Relation Between Electrical Grid Congestion and Bus Characte...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Flin Verdaasdonk Maria Vlasiou Gerwin Hoogsteen Johann Hurink Department of Electrical Engineering Mathematics Computer Science University of Twente

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

electrical grids are increasingly congested, which causes stability and safety risks. To get more insights in how congestion can be managed, this paper analyses whether bus characteristics exist that have a consistent influence on grid loading. To study this, a metric for quantifying bus influence on grid loading is introduced and applied in two case studies. The first case study investigates if there are characteristics that correlate with congestion consistently, regardless of grid topology. The results from this case study suggest that none of the evaluated characteristics consistently correlate with grid loading. These results imply that topology should be explicitly considered in congestion management. The second case study investigates the variance in the correlation between bus characteristics and influence, within a single topology, over a variety of conditions. The results of the second case study suggest that this correlation is robust to reasonable changes in bus loads.

关键词： Correlation Loading Noise Europe Power system stability Stability analysis Robustness Topology Smart grids Safety

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Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management

Multi-Objective Reinforcement Learning for Cognitive Radar R...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ziyang Lu Subodh Kalia M. Cenk Gursoy Chilukuri K. Mohan Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-objective optimization problem and employ deep reinforcement learning to find Pareto-optimal solutions and compare deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms. Our results demonstrate the effectiveness of both algorithms in adapting to various scenarios, with SAC showing improved stability and sample efficiency compared to DDPG. We further employ the NSGA-II algorithm to estimate an upper bound on the Pareto front of the considered problem. This work contributes to the development of more efficient and adaptive cognitive radar systems capable of balancing multiple competing objectives in dynamic environments.

关键词： Upper bound Target tracking Signal processing algorithms Cognitive radar Dynamic scheduling Deep reinforcement learning Resource management Time factors Speech processing Optimization

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Radio Frequency Fingerprinting With Siamese Network

Radio Frequency Fingerprinting With Siamese Network

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International Conference on Computing, Networking and Communications (ICNC)

作者： Raju Dhakal Bhawana Poudel Devkota Laxima Niure Kandel Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Cryptographic security measures are typically unsuitable for IoT and UAV networks because of their inherent computational and energy limitations. As a result, lightweight alternatives like radio frequency fingerprinting (RFF) have recently gained significant attention. RFF utilizes deep learning (DL) techniques, such as Convolutional Neural Networks (CNNs), to identify and detect unique fingerprints. However, CNNs present several challenges: they require large training sets, struggle to detect malicious devices not included in the training process, and require retraining the entire model when new devices are added to the network. To overcome these limitations, in this research we propose the use of a Siamese network for device identification. A Siamese network can measure the similarity or difference between the outputs of two input data points. It requires a smaller training set, can easily detect outliers, and does not need retraining when new devices are added to the network. The transmission of wireless signals from 25 different devices is simulated, collecting 600 MB of I/Q samples using MATLAB. The simulation introduces three physical layer imperfections in the transmitter devices: phase noise, DC offset, and frequency offset. Also, the Similarity-Based Device Identification and Outlier Detection Algorithm (SDIODA) is developed to identify known devices, detect outliers, and utilize the collected dataset and the designed Siamese network to verify the performance of the algorithm. The results demonstrate that the Siamese network achieves nearly 100% accuracy in identifying known devices and detecting outliers with the simulated datasets.

关键词： Training Wireless communication Accuracy Scalability Fingerprint recognition Mathematical models Object recognition Reliability MATLAB Radiofrequency identification

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Exploring the concept of multi-modality using grounding dino with evaluation of its performance in the context of different types and complexity of input text prompts

Exploring the concept of multi-modality using grounding dino...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Rahim, Abdul Singh, Amanjot Sharma, Ankur Garg, Rachit Ravisankar, Ch Yamsani, Nagendar Malla Reddy Engineering College for Women Department of ECE Hyderabad India Lovely Professional University School of Electronics and Electrical Engineering Punjab Phagwara India Institute of Technology and Science Mohan Nagar Ghaziabad India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India SR University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350394412

We have known artificial intelligence, deep learning models can be trained with a certain type of input format to perform a task e.g., OCR models takes input in the form of image to read the text characters from the image, a weather prediction model can take weather data like wind speed, moisture, etc. as an input which is in text format to predict future weather data. But the multi-modality concept which is comparatively newer in the market paves the way for models to be built which can take more than one type of inputs e.g., text and audio, text and image, etc. In the same concept one of the popular architectures is specially designed for detection of any hindrance, model grounding dino about which we will be discussing in this setting. It is a zero-shot object detection model which can perform inference just by taking a text prompt input from the user describing what they want the model to detect in the provided image. In this study, we will be discussing about its capabilities, architecture and also its performance. We will be testing and quantifying its performance using standard self-created input text prompts with variations including description of the object, complexity of the object, etc. on a standard image. © 2024 IEEE.

关键词： Zero-shot learning

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RF Sensing-enabled Interference Mitigation for Wi-Fi-based IoT Systems: A Deep Learning Approach

RF Sensing-enabled Interference Mitigation for Wi-Fi-based I...

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IEEE Conference on Communications and Network Security (CNS)

作者： ChunChih Lin Gen Li Xiaonan Zhang Linke Guo The Holcombe Department of Electrical and Computer Engineering Clemson University Department of Computer Science Florida State University

ISBN: (数字)9798350375961

ISBN: (纸本)9798350375978

The proliferation of the Internet of Things (IoT) over the 2.4GHz has transformed the way people interact with the world in their daily lives. For emerging applications requiring diverse types of data, using IoT devices from different protocols, such as Wi-Fi, Bluetooth, and ZigBee, for data collection will help fulfill designated objectives. Hence, the increasing use of these IoT devices will form a heterogeneous environment with inevitable wireless coexistence. Due to the dynamic IoT environment, the wireless transmission among heterogeneous IoT devices becomes unknown and notoriously hard to manage, where the network performance is always compromised. This paper leverages the RF sensing information to develop a deep learning-based framework for mitigating the cross-technology interference of the Wi-Fi-based IoT system. Each IoT device can provide detailed Channel State Information (CSI) of the transmission link, by which the gateway uses Generative Adversarial Networks (GAN) for building a detailed environmental RF map. Then, we propose a CSI-inspired gateway topology management strategy to find the optimal gateway location, anticipating reaching the highest network throughput. To enhance the efficiency of the proposed scheme, we also adopt the advanced differentiable augmentation approach to reduce the training data size for gateway deployment. Extensive experimental results have demonstrated both the feasibility and efficiency of our design.

关键词： Wireless communication Radio frequency Training Wireless sensor networks Prevention and mitigation Zigbee Logic gates Throughput Sensors Internet of Things

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Enhancing 5G Enabled Solar Power Plant Performance through Reinforcement Learning

Enhancing 5G Enabled Solar Power Plant Performance through R...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Kanwalinderjit Kaur Jarl Christian Ramos Department of Computer & Electrical Engineering & Computer Science California State University Bakersfield California United States

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The utilization of fifth-generation wireless technology (5G) and artificial intelligence (AI) has opened many paths toward making solar power utility systems run more efficiently. 5G and AI have emerged within the last couple of years as competent technologies that can be used in various settings, including within the field of utility delivery. The main concern about this is how these technologies should be used in renewable energy systems and to what extent they improve operations. This research work is about 5G-enabled solar power systems. One of the models used AI-based implementation, while the other does not. Data for both models pertained to parameters such as power, state of charge, harmonic distortion, and voltage are collected. The results are then compared to determine the effectiveness of using AI with the system. Analysis has shown that implementing AI technology with 5G in a solar power system greatly improves power efficiency by stabilizing the voltage of the model. Furthermore, integrating AI with this model has proven to show unparalleled accuracy compared to a version of the model that does not use AI. It can therefore be inferred that usage of 5G with AI technologies will generally be an improvement over solar power implementations that do not use them.

关键词： Wireless communication Renewable energy sources 5G mobile communication SCADA systems Reinforcement learning Power system stability Harmonic distortion State of charge Artificial intelligence Power generation

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Quantized nonlinear Hall effect from chiral monopole

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Physical Review B 2024年第15期110卷 155143页

作者： Nikolai Peshcherenko Claudia Felser Yang Zhang 01187 Dresden Germany Department of Physics and Astronomy Min H. Kao Department of Electrical Engineering and Computer Science

The nonlinear Hall effect arises in materials without inversion symmetry, and the intrinsic contribution is typically from the Berry curvature dipole of nonuniversal Fermi pockets. Here we propose that the nonlinear Hall effect can reach quantization in chiral Weyl semimetals without mirror symmetries. The energy shift between a pair of Weyl nodes leads to chirally asymmetric intranode relaxation, and the net trace of nonlinear Hall conductivity is thus quantized in units of e3/ℏ2 and determined by the sum of monopole charge weighted by the transport relaxation time. Our theory also applies to mirror symmetric Weyl and Dirac semimetals with chiral anomalies. Additionally, besides dc transport probes, we anticipate that nonlinear circular dichroism measurements can detect chiral asymmetry-induced currents.

关键词： Hall effect Topological materials Weyl semimetal

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PINMAP: A Cost-Efficient Algorithm for Glass Detection and Mapping Using Low-Cost 2-D LiDAR

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chae, Jiyeong Seo, Hyunkyo Lee, Sanghoon Park, Yujin Park, Hyung-Seok Park, Kyung-Joon Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of

Autonomous mobile robots (AMRs) have seen rapid adoption due to their ability to autonomously navigate, avoid obstacles, and collaborate efficiently in complex environments. AMRs equipped with light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) are effective in obstacle-rich settings. However, SLAM approaches, particularly those using low-cost 2-D LiDAR, face challenges in accurately detecting and mapping glass surfaces. AMRs may interpret glass as open space, potentially leading to collisions. In this article, we propose a novel framework, the probabilistic incremental navigation-based mapping with accumulative point cloud data (PINMAP), which enables glass detection and mapping without additional sensor hardware or high-cost LiDAR systems. The proposed PINMAP framework offers three key advantages. First, PINMAP achieves accurate detection and mapping of transparent obstacles, such as glass, using only low-cost 2-D LiDAR. Second, PINMAP distinguishes between static and temporary obstacles, effectively adapting to dynamic environments. Finally, PINMAP significantly reduces mapping costs by eliminating the need for manual labeling of glass and temporary obstacles. We empirically validate the performance of PINMAP through extensive experiments, including highly dynamic real-world scenarios. © 1963-2012 IEEE.

关键词： Mobile robots

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