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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

In real-world scenarios, the impacts of decisions may not manifest immediately. Taking these delays into account facilitates accurate assessment and management of risk in real-world environments, thereby ensuring the efficacy of strategies. In this paper, we investigate risk-averse learning using Conditional Value at Risk (CVaR) as risk measure, while incorporating delayed feedback with unknown but bounded delays. We develop two risk-averse learning algorithms that rely on one-point and two-point zeroth-order optimization approaches, respectively. The regret achieved by the algorithms is analyzed in terms of the cumulative delay and the number of total samplings. The results suggest that the two-point risk-averse learning achieves a smaller regret bound than the one-point algorithm. Furthermore, the one-point risk-averse learning algorithm attains sublinear regret under certain delay conditions, and the two-point risk-averse learning algorithm achieves sublinear regret with minimal restrictions on the delay. We provide numerical experiments on a dynamic pricing problem to demonstrate the performance of the proposed algorithms. © 2024, CC0.

关键词： Adversarial machine learning

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A Voxel-Enabled Robotic Assistant for Omnidirectional Conveyance

A Voxel-Enabled Robotic Assistant for Omnidirectional Convey...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Michael Angelo Carvajal Katiso Mabulu Muneer Lalji James Flanagan Rui Luo Samuel Hibbard Tanav Chinthapatla Rohan Bettadpur Salah Bazzi Mark Zolotas Kristian Kloeckl Taşkın Padır Institute for Experiential Robotics Northeastern University Boston MA USA Department of Art + Design School of Architecture Northeastern University Electrical and Computer Engineering Northeastern University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Conventional bidirectional conveyance platforms use a flat translating belt or a series of spinning wheels or rollers to apply a shear force to payloads to move them. Wheel/roller-based conveyors in particular cannot double as a worktop when idle, do not support collision-free multi-object manipulation by default, and are not optimized to move objects that are either slippery or pliable—let alone both. This paper introduces a Voxel-Enabled Robotic Assistant (VERA), a network of intelligent table "partitions" whose topologically dynamic worktops enable omnidirectional conveyance; each partition is composed of a 2D array of "quadrants," axisymmetric modules that can be hot-swapped for maintenance or repairs; each quadrant contains a 2D array of "cells," unitary robotic submodules; each cell houses an independently controllable "voxel," the motorized rotary element that conveys an overhead object. The efficacy of a VERA prototype was determined by evaluating waypoint error as a range of payloads were maneuvered between trajectory waypoints. By conveying both pliable and rigid payloads having slippery textures, the faceted voxels outperformed those augmented to mimic the circular-profiled wheels/rollers of competitor systems. VERA also successfully performed collision-free multi-object planar manipulations planned by its pathfinding algorithm. In light of these results, VERA emerges as a promising material handling platform for use in "Future of Work" settings as the need for multi-purpose collaborative industrial robots continues to grow.

关键词： Visualization Translation Service robots Prototypes Wheels Trajectory Mobile robots Collision avoidance Robots Payloads

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Portable Automatic Sensing System for Sustainable Precision Farm

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 222-227页

作者： Galvão, J. Fernandes, J. Pereira, D. Galvão, M. Neves, F. Department of Electrical Engineering Polytechnic of Leiria Leiria2411-901 Portugal R&D Unit Institute for Systems Engineering and Computers INESC Coimbra Portugal ISTécnico/Lisbon Technical University Lisboa1049-001 Portugal Department of Computer Science Polytechnic of Leiria Leiria2411-901 Portugal

Taking into account the world´s growing population and climate change, there is a need to decarbonize production processes and, in particular, intensive cultures carried out in greenhouses. There is a need to boost the energy and water efficiency of these cyber-physical systems, taking into account human consumption or floriculture, as well as European Community (EEC) and United Nations agreements and pledges, such as the European Green Deal (EGD) or the REPowerEU Plan [1,2] t is critical to develop systems that use integrated models to control these sorts of intense production, collect physical variable factors more efficiently, and achieve improved and zero greenhouse gas emissions (GHG). Finally, it is crucial for a digitalization in these intensive crops, in precision farm or greenhouses. The main objective of this work focuses on the conception of a portable automatic sensing system (PASS) for pH, electroconductivity and water flow, to control irrigation water quality in precision farm/greenhouses. The data collected by these sensors are made available in a mobile app for consultation by the operator. Another objective will be the local production of electrical and thermal energy from renewable sources (solar and biomass), hybrid systems and the use of cogeneration to supply energy to the greenhouse. A sustainable product policy also has the potential to reduce waste significantly. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Sustainable development

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Multi-Layer Network Formation Through HAPS Base Station and Transmissive RIS-Equipped UAV

Multi-Layer Network Formation Through HAPS Base Station and ...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled El-Oued University Lgeere Lab Department of Electrical Engineering El-Oued Algeria Carleton University Ntn Lab Department of Systems and Computer Engineering Ottawa Canada Ankara Yildirim Beyazit University Electrical and Electronics Engineering Ankara Turkey University of Tripoli Department of Electrical and Electronic Engineering Libya Kth Royal Institute of Technology Division of Communications Systems Sweden Zonguldak Bulent Ecevit University Department of Computer Engineering Zonguldak Turkey Department of Computer Engineering Center for Communication Systems and Sensing Dhahran Saudi Arabia

ISBN: (纸本)9798350368369

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels. © 2025 IEEE.

关键词： Enterprise resource planning

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Generalized Continuous-Time Models for Nesterov’s Accelerated Gradient Methods

arXiv

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arXiv 2024年

作者： Park, Chanwoong Cho, Youngchae Yang, Insoon Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of

Recent research has indicated a substantial rise in interest in understanding Nesterov’s accelerated gradient methods via their continuous-time models. However, most existing studies focus on specific classes of Nesterov’s methods, which hinders the attainment of an in-depth understanding and a unified perspective. To address this deficit, we present generalized continuous-time models that cover a broad range of Nesterov’s methods, including those previously studied under existing continuous-time frameworks. Our key contributions are as follows. First, we identify the convergence rates of the generalized models, eliminating the need to determine the convergence rate for any specific continuous-time model derived from them. Second, we show that six existing continuous-time models are special cases of our generalized models, thereby positioning our framework as a unifying tool for analyzing and understanding these models. Third, we design a restart scheme for Nesterov’s methods based on our generalized models and show that it ensures a monotonic decrease in objective function values. Owing to the broad applicability of our models, this scheme can be used to a broader class of Nesterov’s methods compared to the original restart scheme. Fourth, we uncover a connection between our generalized models and gradient flow in continuous time, showing that the accelerated convergence rates of our generalized models can be attributed to a time reparametrization in gradient flow. Numerical experiment results are provided to support our theoretical analyses and results. © 2024, CC BY-NC-ND.

关键词： Continuous time systems

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Abnormalities Detection in Chest CT Scan Images using Federated and Deep Learning

Abnormalities Detection in Chest CT Scan Images using Federa...

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International Conference on Emerging Technologies, ICET

作者： Mashab Ali Javed Syed Jawad Hussain Aqsa Yasmeen Amaad Khalil Muhammad Bilal Rafaqat Department of Electrical and Computer Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan School of ICT University of Tasmania Hobart Australia

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Lung cancer, which accounts for an estimated 2.20 million reported cases and leading to 1.80 million fatalities in the year 2020, represents a significant global cause of mortality. Early detection during Computed Tomography (CT) chest scans can improve survival rate of patients. To address the growing use of CT scan, a robust automated model is required to eliminate tedious manual procedures and reduce variability in diagnosis. Moreover, there are challenges associated with medical research such as data privacy, collaboration, regulatory compliance, and data diversity. In this study, we propose two machine learning (ML) methods and subsequently conduct a comparative analysis. In the first approach, we presented a deep learning technique to achieve high image classification accuracy by using multi-layer CNN and two pre-trained models, namely ResNet50 and DenseNet201. In the second approach, we presented a Federated Learning (FL) technique, which entails the utilization of all three models for classifying abnormalities in chest CT scans. Our results illustrate that the FL-based approach can assist in the diagnosis of anomalies in chest CT scan, while ensuring the privacy of patient data. By exploring the delicate balance between privacy and accuracy in medical image classification, our study will contribute to the advancing realm of research.

关键词： Deep learning Data privacy Privacy Accuracy Federated learning Computed tomography Data models Medical diagnostic imaging Residual neural networks Image classification

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Differentially Private Online Federated Learning with Correlated Noise

Differentially Private Online Federated Learning with Correl...

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IEEE Conference on Decision and Control

作者： Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Systems Engineering and Engineering Management Chinese University of Hong Kong

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an $(\epsilon, \delta)$ DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm.

关键词： Privacy Federated learning Heuristic algorithms Noise Signal processing algorithms Servers

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Adjustable Current Limiting Function with a Monolithically Integrated SiC Circuit Breaker Device

Adjustable Current Limiting Function with a Monolithically I...

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作者： Takamori, Taro Wada, Keiji Boettcher, Norman Erlbacher, Tobias Saito, Wataru Nishizawa, Shin-Ichi Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo192-0397 Japan Fraunhofer Institute for Integrated Systems and Device Technology IISB Erlangen91058 Germany Kyushu University Research Institute for Applied Mechanics Fukuoka816-8580 Japan

This article proposes a current limiting function for a self-sensing and self-triggering monolithically integrated SiC circuit breaker device. The proposed function provides the device not only with a fast-response current breaking operation but also with the current limiting operation, including supplying a constant current to the load and preventing the detection of inrush currents. This function is suitable for replacing mechanical conductors in initial charging circuits within rectifiers and safety equipment in case of overcurrent phenomena. The proposed method achieves certain constant currents through a simple variation of the gate-drive circuit with the adjustable parameters of an additional MOSFET. The mechanism of the current limiting function is verified using TCAD simulations based on certain conditions. Consequently, experimental results verified that the circuit breaker device with the current limiting function reduces the inrush current by up to 76.9% using a 500 VDC distribution circuit system. © 1972-2012 IEEE.

关键词： Silicon carbide

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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Generative adversarial networks

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