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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anjarlekar, Ameya Etesami, Rasoul Srikant, R. Uiuc Department of Electrical and Computer Engineering United States Uiuc Faculty of Electrical and Computer Engineering and Industrial and Systems Engineering United States Uiuc Faculty of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

We address a problem involving a buyer seeking to train a logistic regression model by acquiring data from privacy-sensitive sellers. Along with compensating the sellers for their data, the buyer provides differential privacy guarantees to them where the payments depend on the privacy guarantees. In addition, each seller has a different privacy sensitivity associated with their data, which is the cost per unit of loss of privacy. The buyer transacts sequentially with the sellers, wherein the seller will disclose their privacy sensitivity, and the buyer immediately provides a payment and guarantees differential privacy. After receiving the payment, the seller provides their data to the buyer. The buyer's goal is to optimize a weighted combination of test loss and payments, i.e., achieve a tradeoff between getting a good ML model and limiting its payments. Additionally, the buyer must design the payments and differential privacy guarantees in an online fashion. Further, the online problem is historydependent, which adds to the challenge. Consequently, we design a payment mechanism that ensures incentive compatibility and individual rationality and is asymptotically optimal. Additionally, we also provide experimental results to validate our findings. © 2024 IEEE.

关键词： Logistic regression

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Viewport Prediction via Adaptive Edge Offloading

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 21-25页

作者： Aydin, Ahmet Gunhan Vikalo, Haris University of Texas at Austin Electrical and Computer Engineering Department AustinTX78705 United States

The pursuit of enhanced interactive visual experiences has created growing interest in 360-degree video streaming. However, transmitting such content requires significant bandwidth compared to conventional planar video, motivating a search for effective bandwidth optimization strategies. A promising approach involves predicting viewport and prioritizing transmission of the regions of interest at higher quality. The existing methods for viewport prediction rely on sophisticated neural networks hosted on servers and face major bandwidth and latency challenges. This letter proposes a hierarchical approach to viewport prediction that leverages a small model on edge devices and offloads to the server only the most challenging tasks. The offloading algorithm relies on rate control to maximize the performance while meeting resource constraints, presenting a novel solution to bandwidth-efficient viewport prediction for 360-degree video streaming. © 2019 IEEE.

关键词： Video streaming

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Real-time disease detection on bean leaves from a small image dataset using data augmentation and deep learning methods: Real-time disease detection on bean leaves from a small image dataset using data augmentation..: E. Karantoumanis et al.

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Soft Computing 2024年第21期28卷 12929-12941页

作者： Karantoumanis, Emmanouil Balafas, Vasileios Louta, Malamati Ploskas, Nikolaos Department of Electrical and Computer Engineering University of Western Macedonia Campus ZEP Kozani50100 Greece

Disease detection in agricultural crops plays a pivotal role in ensuring food security and sustainable farming practices. Deep learning models, known for their ability in image analysis, often demand extensive image datasets and annotations to achieve high accuracy. However, in the case of bean crops, the absence of a publicly available dataset has posed a significant challenge for applying deep learning algorithms to accurately predict diseases. Additionally, the manual annotation of images demands substantial time and resources. This paper introduces an innovative approach to tackle these issues. We introduce a solution for real-time disease detection on bean leaves, despite the lack of bean-specific image data. Initially, we generate a small dataset from real images and annotate them. Then, we utilize images from the existing dataset PlantDoc (Singh et al. in: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Association for Computing Machinery, pp 249–253, 2020) from leaves of other plant species. Moreover, to compensate for the limitations of a small image dataset, we employ advanced data augmentation techniques, enriching the training data and enhancing the model’s ability to generalize. Our experimental study shows that data augmentation techniques can improve the accuracy of deep learning methods by up to 37%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Adversarial machine learning

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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

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IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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Three-Level Unipolar/Bipolar Buck-Boost AC-AC Converters with Less Active Switches and No Commutation Issues

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IEEE Transactions on Transportation Electrification 2024年第2期10卷 4184-4200页

作者： Ahmed, Hafiz Furqan Zaabi, Omar Al El Moursi, Mohamed Shawky Hosani, Khalifa Al National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Mansoura University Faculty of Engineering Mansoura35516 Egypt

This article proposes three-level (TL) buck-boost direct ac-ac converters based on switching-cell configuration with coupled magnetics. The proposed converters use only six active switches and can produce noninverting buck (NIBu), noninverting boost (NIBo), versatile noninverting buck-boost (VNIBB), and inverting buck-boost (IBB) output voltages. Unlike the conventional ac-ac converters, pulsewidth modulation (PWM) dead and overlap times are allowed in the proposed converters without switch voltage and current overshoots, respectively, making it free of commutation issues without requiring extra circuitry and control. Moreover, high-speed power MOSFETs can be utilized without the conduction of their body diodes and related poor reverse recovery issues. The proposed converters generate TL input and output voltages, increasing the voltage levels of input-output inductors and reducing their voltage stresses. In addition, a phase shift PWM control is utilized, which enhances the actual frequency of input and output voltages by twice the operating frequency of the switching devices. Consequently, input and output inductors can be designed much smaller, while reducing input-output current ripples. The proposed circuits also feature shared-ground point between ac source and load and provide continuous input and output currents. Two circuit topologies are proposed: Type-I is a noninverting (unipolar) buck-boost converter with less magnetic components and diodes and Type-II is a bipolar buck-boost converter. The experimental test results on a 400-W laboratory circuit are given to verify the operation of the proposed converter. © 2015 IEEE.

关键词： Buck-Boost converter

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Identification of Effective Factors on Formation Generator Coherent Groups for Islanding Detection for Renewable Energy Integration 12

Identification of Effective Factors on Formation Generator C...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Karimi, Hamid Sezavar, Hamid Reza Hasanzadeh, Saeed Haghighi, Mehran Heydarian-Forushani, Ehsan Qom University of Technology Department of Electrical and Computer Engineering Qom Iran Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798331525132

Identification of coherent groups and determining the frequency of their oscillations in power system is very important. The coherent groups that are created in the power system and their behavior in the isolation of power system are necessary. In this paper, a number of factors such as error position, Power system structure, constant inertia of generators and operating point of power system in each section are investigated and generator coherent groups are separated. The results show that this factors are affected on the coherent groups. In this paper, for determining the coherent groups, rotor oscillation of the generators is used, which this is a common method for the grouping of generators. These simulations are carried on the network of 9 generators designed at Digsilent environment. © 2025 IEEE.

关键词： Islanding

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A Novel High Step-Up DC-DC Converter with Zero Current Switching and Coupled Inductors for Enhanced Efficiency and Voltage Gain 16

A Novel High Step-Up DC-DC Converter with Zero Current Switc...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Esfahani, Seyed Mohammad Naji Zahiri, Seyed Hamid Delshad, Majid University of Birjand Department of Electrical and Computer Engineering Birjand Iran Branch Department of Electrical Engineering Isfahan Iran

ISBN: (纸本)9798331533946

This study introduces a high step-up DC-DC converter integrated with a novel, lossless snubber circuit. To achieve an elevated voltage gain, the design combines a voltage-lifting mechanism with a voltage-doubling circuit. An auxiliary circuit, comprising two resonant inductors and a resonant capacitor, facilitates soft-switching conditions across all semiconductor components, thereby enhancing efficiency and reducing switching losses. This configuration not only enables high voltage gain but also minimizes voltage stress on the switching element, contributing to the converter's overall high efficiency. Furthermore, the leakage inductance of the transformer is absorbed by the resonant inductor in the auxiliary circuit, effectively mitigating voltage spikes across the switch. A prototype of the proposed converter has been constructed to validate the theoretical analysis, confirming its performance improvements. © 2025 IEEE.

关键词： Capacitor bank

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Self-Learning Multi-Mode Slicing Mechanism for Dynamic Network Architectures

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IEEE/ACM Transactions on Networking 2024年第2期32卷 1048-1063页

作者： Esmat, Haitham H. Lorenzo, Beatriz University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01002 United States

Dynamic network architectures that utilize communication, computing, and storage resources at the wireless edge are key to delivering emerging services in next-generation networks (e.g., AR/VR, 3D video, intelligent cars, etc). Network slicing can be significantly enhanced by including dynamically available resources throughout the fog/edge/cloud continuum and using mmWave/THz bands. However, network slicing of dynamic multi-tier computing networks remains under-explored. In this paper, we present a self-learning end-to-end network slicing mechanism (SELF-E2E-NS) that facilitates collaboration between the Infrastructure Provider (InP) and tenants to slice their subscribers' resources (i.e., radio, computing, and storage) as fog resources. To adapt to the uncertain availability of resources at the edge and minimize the risk of non-satisfying service level agreements (SLAs), our slicing mechanism has two operational modes. Operational mode 1 is for joint network slicing (JNS) in which the InP infrastructure is augmented with fog resources and jointly sliced to meet high throughput and delay tolerant requirements. Operational mode 2 is for independent network slicing (INS) in which the InP infrastructure and fog resources are sliced separately to achieve high throughput, low-latency, and high-reliability requirements. Our schemes leverage mmWave/THz, fog/edge/cloud computing, and caching to achieve new service requirements. We design a DQ-E2E-JNS algorithm that uses Deep Dueling network and a MAAC-E2E-INS algorithm based on multi-agent actor-critic, which incorporate service-aware pricing feedback and fog trading matching, respectively. These algorithms find the optimal slice request admission and collaboration policy that maximizes the long-term revenue of the InP and tenants for each mode. The simulation results show that our novel slicing mechanism can serve up to 4 times more requests and effectively exploits different spectrum bands and fog resources to improve re

关键词： Network architecture

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Analysis and synchronization controller design of dual-port gridforming voltage-source converters for different operation modes

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iEnergy 2024年第1期3卷 46-58页

作者： Shuo Zhang Wei Qiao Liyan Qu Jun Wang Department of Electrical and Computer Engineering University of Nebraska-LincolnLincolnNE 68588-0511USA

Grid-tie voltage source converters(VSCs)can operate in three distinct modes:AC-dominant,DC-dominant,and balanced,depending on the placement of the stiff voltage *** distinct operation modes of the VSCs traditionally demand different synchronization control techniques,leading to heterogeneous *** is challenging for the power system to accommodate and coordinate heterogeneous VSCs.A promising universal synchronization control technique for VSCs is the DC-link voltage synchronization control(DVSC)based on a lead compensator(LC).The LC DVSC stabilizes both the DC and AC voltages of a VSC while achieving synchronization with the AC *** results in a dual-port grid-forming(DGFM)characteristic for the ***,there has been very limited study on the stability and synchronization controller design of the VSCs with the LC DVSC operating in various *** bridge this gap,the paper presents a quantitative analysis on the stability and steady-state performance of the LC DVSC in all three operation modes of the DGFM *** on the analysis,the paper provides step-by-step design guidelines for the LC ***,the paper uncovers an instability issue related to the LC DVSC when the DGFM VSC operates in the balanced *** tackle the instability issue,a virtual resistance control is proposed and integrated with the LC *** results validate the analysis and demonstrate the effectiveness of the DGFM VSC with the LC DVSC designed using the proposed guidelines in all three operation ***,the paper demonstrates the feasibility of employing the DGFM VSC with the LC DVSC for all three possible operation modes,which can help overcome the challenges associated with accommodating and coordinating heterogeneous VSCs in the power system.

关键词： DC-link voltage synchronization control(DVSC) dual-port grid-forming(DGFM)control grid forming(GFM) synchronization stability voltage-source converter(VSC)

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Optimization of Energy Hub Performance Using the Improved Particle Swarm Optimization (IPSO) Algorithm for Integrated Energy Systems 12

Optimization of Energy Hub Performance Using the Improved Pa...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Masoudi, Mohammad Reza Haghighi, Mehran Sezavar, Hamid Reza Department of Electrical Engineering Tehran Iran Qom University of Technology Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9798331525132

Energy hubs are essential infrastructures for integrated energy management, optimizing consumption and distribution while enhancing system stability and efficiency. This study presents a deterministic model aimed at minimizing the operating costs of energy grids, where energy hubs coordinate distributed generation and storage systems. To address uncertainties in load, energy prices, renewable energy production, and mobile storage consumption, an adaptive robust optimization approach is employed. The nonlinear and non-convex problem is solved using the Improved Particle Swarm Optimization (IPSO) algorithm, which achieves superior results compared to other methods, with an objective function value of 4253.7, a convergence iteration of 1130, and a computational time of 313.043 seconds. These outcomes underscore the effectiveness of IPSO in optimizing integrated energy systems. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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