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arXiv 2024年

作者： Cinà, Antonio Emanuele Villani, Francesco Pintor, Maura Schönherr, Lea Biggio, Battista Pelillo, Marcello Department of Computer Science Bioengineering Robotics and Systems University of Genoa Italy Department of Electrical and Electronic Engineering University of Cagliari Italy CISPA Helmholtz Center for Information Security Germany Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy

Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider 2- and ∞-norm constraints to craft input perturbations, only a few investigate sparse 1- and 0-norm attacks. In particular, 0-norm attacks remain the least studied due to the inherent complexity of optimizing over a non-convex and non-differentiable constraint. However, evaluating adversarial robustness under these attacks could reveal weaknesses otherwise left untested with more conventional 2- and ∞-norm attacks. In this work, we propose a novel 0-norm attack, called σ-zero, which leverages a differentiable approximation of the 0 norm to facilitate gradient-based optimization, and an adaptive projection operator to dynamically adjust the trade-off between loss minimization and perturbation sparsity. Extensive evaluations using MNIST, CIFAR10, and ImageNet datasets, involving robust and non-robust models, show that σ-zero finds minimum 0-norm adversarial examples without requiring any time-consuming hyperparameter tuning, and that it outperforms all competing sparse attacks in terms of success rate, perturbation size, and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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A Multi-Objective Memetic Algorithm with Adaptive Local Search for Airspace Complexity Mitigation ⋆

SSRN

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SSRN 2023年

作者： Li, Biyue Guo, Tong Mei, Yi Li, Yumeng Chen, Jun Zhang, Yu Tang, Ke Du, Wenbo School of Electronic and Information Engineering Beihang University Beijing100191 China School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Civil and Environmental Engineering University of South Florida TampaFL33620 United States Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Province Shenzhen518055 China

Airspace complexity is a paramount safety metric to measure the difficulty and effort required to safely manage air traffic. The continuing growth in air traffic demand results in increasing airspace complexity and unprecedented safety concerns. Most existing methods treat the minimization of airspace complexity as the sole objective, ignoring the path deviation cost induced by the re-scheduled aircraft. In this paper, regarding reduction of airspace complexity and path deviation cost as two conflicting objectives, a multi-objective airspace complexity mitigation model is proposed to simultaneously ensure the safety and efficiency of air transport by optimizing flight trajectories. To effectively solve this multi-objective and non-linear optimization problem, a novel memetic algorithm called MA-ALS is developed. Specifically, we design a new crossover and three new local search operators under the flight trajectory representation. MA-ALS conducts exploration by crossover, and exploitation by a hill-climbing local search process. Moreover, we proposed an adaptive local search selection mechanism which facilitates the dynamic collaboration of different local search operators during evolution. A comprehensive comparison with the most recently developed algorithms on Chinese air traffic dataset is conducted. The Pareto front generated by the proposed algorithm dominates that of the compared baselines. Moreover, compared with a real flight schedule, the flight plan obtained by the proposed algorithm can significantly reduce the airspace complexity. © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Mobile Application for Power Substation Monitoring for Maintenance Operations 8

Mobile Application for Power Substation Monitoring for Maint...

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8th IEEE International Conference on Adaptive Science and Technology, ICAST 2021

作者： Adjei, Derrick Arthur, Ezekiel Asamoah, Henrietta Blankson-Afful, Gilbert Fletcher, Peter Mensah, Godfred Hodey, Dzifa Mills, Godfrey University of Ghana Department of Computer Engineering Legon Ghana Electricity Company of Ghana Subtransmission Region Accra Ghana Electricity Company of Ghana Power System Planning Division Accra Ghana Ashesi University Department of Electrical and Electronic Engineering Berekuso Ghana

ISBN: (纸本)9781665427173

Power substations are the most critical units of any electricity supply system. These substations either step down or step up voltages from one level to another, or maintain the same level of voltage for interconnections. Substations that operate at medium to high voltage levels have critical components such as transformers, circuit breakers, protection relays, DC batteries among others, that require regular monitoring and analysis for maintenance operations. These components also detect potential anomalies that could lead to unplanned outages and system failure. In this paper, we present an automated monitoring system that enables authorized substation operators to remotely access and receive critical substation information on trips and alarms necessary for rapid maintenance response and intervention of faults. At the crux of the monitoring system is a hardware controller system which has a microcontroller, relays, and shift registers, for acquiring and processing signals from the transformer control panel at the substation. The controller system, which also functions as a data logger, communicates the substation data to a cloud server using the GSM/GPRS (Global Service Mobile/General Packet Radio Service) communication protocol. A user interface system on a mobile device interacts and access prevailing conditions at the substation from the controller system through the server system. A prototype of the substation monitoring system design was implemented and tested through numerical simulation and field tests at a 33/11kV substation of the ECG (Electricity Company of Ghana). The results show that authorized substation operators are able to remotely monitor and receive in real-time, critical substation information such as alarms, trips, warnings, and the winding loads on transformers necessary to facilitate rapid maintenance to avoid system failures and shut-down. © 2021 IEEE.

关键词： Controllers

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Network Slicing with Flexible VNF Order: A Branch-and-Bound Approach

arXiv

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arXiv 2024年

作者： Luu, Quang-Trung Nguyen, Minh-Thanh Do, Tuan-Anh Kieffer, Michel Nguyen, Van-Dinh Nguyen, Tai-Hung Nguyen, Huu-Thanh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Université Paris-Saclay CNRS CentraleSupélec – L2S Gif-sur-YvetteF-91192 France College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Vinhomes Ocean Park Hanoi100000 Viet Nam

Network slicing is a critical feature in 5G and beyond communication systems, enabling the creation of multiple virtual networks (i.e., slices) on a shared physical network infrastructure. This involves efficiently mapping each slice component, including virtual network functions (VNFs) and their interconnections (virtual links), onto the physical network. This paper considers slice embedding problem in which the order of VNFs can be adjusted, providing increased flexibility for service deployment on the infrastructure. This also complicates embedding, as the best order has to be selected. We propose an innovative optimization framework to tackle the challenges of jointly optimizing slice admission control and embedding with flexible VNF ordering. Additionally, we introduce a near-optimal branch-and-bound (BnB) algorithm, combined with the A* search algorithm, to generate embedding solutions efficiently. Extensive simulations on both small and large-scale scenarios demonstrate that flexible VNF ordering significantly increases the number of deployable slices within the network infrastructure, thereby improving resource utilization and meeting diverse demands across varied network topologies. © 2024, CC BY.

关键词： 5G mobile communication systems

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Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models

Comparative Analysis of Epileptic Seizure Prediction: Explor...

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IEEE UP Section Conference on Electrical computer and electronics (UPCON)

作者： Md. Simul Hasan Talukder Rejwan Bin Sulaiman Gunjan Chhabra Suman Avdhesh Yadav Rezaul Haque Md Babul Islam Keshav Kaushik Nuclear Safety Security and Safegurad Division Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh School of Computer Science and Technology Northumbria University Newcastle UK Dept. of CSE Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Amity School of Engineering & Technology Noida India Dept of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering (DIMES) University of Calabria Calabria Italy School of Computer Science University of Petroleum and Energy Studies Dehradun India

Precise prediction and detection of epilepsy, a common neurological condition marked by sporadic and repeated seizures, is necessary for efficient management and patient care. To do accurate and reliable seizure detection, machine learning (ML) on electroencephalogram (EEG) recordings is a good choice because of its capacity to provide insightful information about brain activity during seizures. In this work, we do a thorough comparative analysis of five machine learning methods for the prediction of epileptic seizures using EEG data: Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB). Firstly, to assure the quality of the data and enable precise model training, the dataset underwent extensive preprocessing, which included cleaning, normalization, treatment of outliers, and oversampling. These preprocessing techniques significantly enhanced the models' performance. In our study, we evaluate the accuracy of each model; the LR classifier obtains 56.95% accuracy, while both GB and DT achieve 97. 17% accuracy. Outperforming the findings of previous investigations, the ET model showed the best performance with an accuracy of 99.29%. Achieving 98.99% accuracy was another accomplishment for the RF model. Our results imply that the ET model not only performed better than the other models in the comparison analysis but also more effectively than the most advanced outcomes from earlier research. Regarding the accurate and dependable prediction of epileptic seizures using EEG data, the ET model is a solid option due to its remarkable performance.

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Design of Anode Saturable Reactor Core Based on Electromagnetic-Thermal Simulation and Neural Network Modeling

Design of Anode Saturable Reactor Core Based on Electromagne...

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IEEE Conference on Electromagnetic Field Computation

作者： Jiaxin Yuan Xuzhe Li Hang Zhou Yifan Wang Zuoquan Mo Yanli Zhang Yanhui Gao Kazuhiro Muramatsu State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and Automation Wuhan University Wuhan China Guangzhou Power Supply Section China Railway Guangzhou Group Co. Ltd. Guangzhou China School of Electrical Engineering Shenyang University of Technology Shenyang China Division of Mechatronics Oita University Oita Japan Department of Electrical and Electronic Engineering Saga University Saga Japan

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

The separated structure of the anode saturable reactor (ASR) iron core makes it possible to change the arrangement of the iron core to affect heat exchange and reduce the risk of local thermal aging of insulation materials. Based on the electromagnetic thermal simulation results of the ASR, this study aims to improve the design of iron core thickness by using a neural network model with the same average temperature of 9 pairs of iron cores.

关键词： Heating systems Insulation Simulation Neural networks Optimization methods Aging Iron

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A Generalized Pointing Error Model for FSO Links with Fixed-Wing UAVs for 6G: Analysis and Trajectory Optimization

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IEEE Transactions on Wireless Communications 2025年

作者： Moon, Hyung-Joo Chae, Chan-Byoung Wong, Kai-Kit Alouini, Mohamed-Slim Yonsei University School of Integrated Technology Seoul03722 Korea Republic of University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul03722 Korea Republic of King Abdullah University of Science and Technology Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia

Free-space optical (FSO) communication is a promising solution to support wireless backhaul links in emerging 6G non-terrestrial networks. At the link level, pointing errors in FSO links can significantly impact capacity, making accurate modeling of these errors essential for both assessing and enhancing communication performance. In this paper, we introduce a novel model for FSO pointing errors in unmanned aerial vehicles (UAVs) that incorporates three-dimensional (3D) jitter, including roll, pitch, and yaw angle jittering. We derive a probability density function for the pointing error angle based on the relative position and posture of the UAV to the ground station. This model is then integrated into a trajectory optimization problem designed to maximize energy efficiency while meeting constraints on speed, acceleration, and elevation angle. Our proposed optimization method significantly improves energy efficiency by adjusting the UAV's flight trajectory to minimize exposure to directions highly affected by jitter. The simulation results emphasize the importance of using UAV-specific 3D jitter models in achieving accurate performance measurements and effective system optimization in FSO communication networks. Using our generalized model, the optimized trajectories achieve up to 11.8% higher energy efficiency compared to those derived from conventional Gaussian pointing error models. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Beyond Radio: Het-Medium Networks and Its Cross-Medium Communication Paradigms for 6G

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IEEE Network 2025年

作者： Wu, Mingming Han, Yufeng Xiao, Yue Gao, Yulan Li, Shiying Niyato, Dusit Karagiannidis, George K. University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China QianYuan National Laboratory Hangzhou310024 China KTH Royal Institute of Technology Division of Information Science and Engineering Stockholm100 44 Sweden Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Group Electrical and Computer Engineering Dept Thessaloniki54 124 Greece

In the evolving landscape of communication technologies, the integration of Heterogeneous Medium Networks (HetMNets) has emerged as a pivotal progression, signaling a departure from traditional, radio-centric paradigms. As we edge closer to the realization of 6G networks, understanding the intricate dynamics of cross-medium communication (CMC) becomes imperative. This article indicates the multifaceted nature of HetMNets and explores the transformative CMC paradigms shaping the roadmap toward 6G. First, we outline the key performance indicators (KPIs), potential scenarios, and the emerging technologies and applications of 6G wireless systems, which serve as the driving forces behind the development of HetMNets. Subsequently, various communication media are introduced, contrasting their propagation characteristics to underscore the indispensability of HetMNets for the 6G landscape. We then enumerate the general issues involving challenges/problems and potential technical solutions in HetMNets. Moreover, we present an air-sea coordinated CMC system model, serving as an example of HetMNets built upon CMC paradigms. Specifically, we tackle a power allocation challenge, optimizing the transmit power of both underwater acoustic and radio frequency (RF) communication media at the cross-medium bidirectional relay to curtail the outage probability. The corresponding solution highlights the efficacy of the proposed CMC paradigm in supporting the reliability and resource efficiency of HetMNets. © 1986-2012 IEEE.

关键词： Radio communication

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Study of Matrix Effects in Laser-Induced Breakdown Spectroscopy by Different Sample Preparation

Study of Matrix Effects in Laser-Induced Breakdown Spectrosc...

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IEEE International Conference on Plasma Science (ICOPS)

作者： Y. Huang F. Keserwan L. Droog S. Mohajan J. Achtymichuk M. Dyck A. Bais A. Hussein Electronic Systems Engineering University of Regina Regina Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Civil and Environmental Engineering University of Alberta Edmonton Canada Department of Renewable Resources University of Alberta Edmonton Canada

ISBN: (纸本)9781665479264

Laser-induced breakdown spectroscopy (LIBS) has become a promising technology for real-time characterization of soil properties. However, due to the inherent microscale heterogeneity of granular materials like soil, spectral emission lines can be highly variable. To investigate the effects of moisture and heterogeneity, we performed qualitative and quantitative analysis on several field moist, air-dried, and ground, air-dried soil samples with differing elemental concentrations. Principal component analysis was carried out to extract features from raw spectra. Matrix effects on Al, Ca, Fe, Mg, Na and K, were investigated. We found that even if the elemental concentration is different, samples prepared in the same way, have spectra with similar features. We were able to discriminate spectra from moist and dried, ground samples with 100% accuracy using a support vector machine, while the unground dry samples, which did not have outstanding features, were differentiated with 50% accuracy. When samples are ground, elemental prediction was improved for all elements except for Fe, indicating that microscale heterogeneity can be reduced by grinding samples. Unlike the other elements, Fe estimation was better on moist samples, which may be due to the interaction of iron minerals with soil moisture.

关键词： Support vector machines Spectroscopy Electric breakdown Soil properties Soil moisture Feature extraction Iron

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Deep-Learning–Based Screening and Ancillary Testing for Thyroid Cytopathology

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American Journal of Pathology 2023年第9期193卷 1185-1194页

作者： Dov, David Elliott Range, Danielle Cohen, Jonathan Bell, Jonathan Rocke, Daniel J. Kahmke, Russel R. Weiss-Meilik, Ahuva Lee, Walter T. Henao, Ricardo Carin, Lawrence Kovalsky, Shahar Z. I-Medata AI Center Tel Aviv Sourasky Medical Center Tel Aviv-Yafo Israel Department of Pathology Duke University Medical Center Durham NC United States Department of Head and Neck Surgery & Communication Sciences Duke University Medical Center Durham NC United States Department of Head and Neck Surgery Kaplan Medical Center Rehovot Israel Biological Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biostatistics and Bioinformatics Duke University Durham NC United States Computer Science Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics University of North Carolina at Chapel Hill Chapel Hill NC United States

Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification. © 2023 American Society for Investigative Pathology

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