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5th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2023

作者： Oya, Hidetoshi Nakagawa, Takuya Nagai, Shunya Nakano, Kazushi Tokyo City University Department of Computer Science Tokyo Japan Tokyo City University Graduate School of Integrative Science and Engineering Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350325041

This paper proposes a non-iterative LMI-based design strategy for guaranteed cost static output feedback controllers for linear systems. The proposed design approach is based on the well-known necessary and sufficient condition for the existence of the static output feedback stabilizing control for linear systems. In this paper, for linear systems we show the non-iterative LMI-based design procedure for static output feedback controllers which minimize the upper bound of a given quadratic cost function, and the effectiveness of the proposed static output feedback controller with guaranteed cost is presented through a simple numerical example. © 2023 IEEE.

关键词： Linear systems

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Consistent Submodular Maximization

arXiv

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arXiv 2024年

作者： Dütting, Paul Fusco, Federico Lattanzi, Silvio Norouzi-Fard, Ashkan Zadimoghaddam, Morteza Google Research. Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i.e., the number of changes between two consecutive solutions is bounded). We provide algorithms in this setting with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances. © 2024, CC BY.

关键词： Data mining

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SL-CycleGAN: Blind Motion Deblurring in Cycles Using Sparse Learning 7

SL-CycleGAN: Blind Motion Deblurring in Cycles Using Sparse ...

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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Saqlain, Ali Syed Wang, Li Yun Liu, Zhiyong North China Electric Power University School of Control and Computer Engineering Beijing China Portland State University Department of Computer Science Portland United States

ISBN: (纸本)9781665487115

In this paper, we introduce an end-To-end generative adversarial network (GAN) based on sparse learning for single image motion deblurring, which we called SL-CycleGAN. For the first time in image motion deblurring, we propose a sparse ResNet-block as a combination of sparse convolution layers and a trainable spatial pooler k-winner based on HTM (Hierarchical Temporal Memory) to replace non-linearity such as ReLU in the ResNet-block of SL-CycleGAN generators. Furthermore, we take our inspiration from the domain-To-domain translation ability of the CycleGAN, and we show that image deblurring can be cycle-consistent while achieving the best qualitative results. Finally, we perform extensive experiments on popular image benchmarks both qualitatively and quantitatively and achieve the highest PSNR of 38.087 dB on GoPro dataset, which is 5.377 dB better than the most recent deblurring method. © 2022 IEEE.

关键词： Representation learning Cloud computing Convolution Benchmark testing Big Data Generative adversarial networks Generators

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Can Robots "Taste" Grapes? Estimating SSC with Simple RGB Sensors

arXiv

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arXiv 2024年

作者： Ciarfuglia, Thomas Alessandro Motoi, Ionut Marian Saraceni, Leonardo Nardi, Daniele Sapienza University of Rome Department of Computer Control and Management Engineering RM Rome00185 Italy

In table grape cultivation, harvesting depends on accurately assessing fruit quality. While some characteristics, like color, are visible, others, such as Soluble Solid Content (SSC), or sugar content measured in degrees Brix (°Brix), require specific tools. SSC is a key quality factor that correlates with ripeness, but lacks a direct causal relationship with color. Hyperspectral cameras can estimate SSC with high accuracy under controlled laboratory conditions, but their practicality in field environments is limited. This study investigates the potential of simple RGB sensors under uncontrolled lighting to estimate SSC and color, enabling cost-effective, robot-assisted harvesting. Over the 2021 and 2022 summer seasons, we collected grape images with corresponding SSC and color labels to evaluate algorithmic solutions for SSC estimation on embedded devices commonly used in robotics and smartphones. Our results demonstrate that SSC can be estimated from visual appearance with human-like performance. We propose computationally efficient histogram-based methods for resource-constrained robots and deep learning approaches for more complex applications. © 2024, CC BY-NC-ND.

关键词： Fruits

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Model-Driven Feature engineering for Data-Driven Battery SOH Model

Model-Driven Feature Engineering for Data-Driven Battery SOH...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Khaled Alamin Daniele Jahier Pagliari Yukai Chen Enrico Macii Sara Vinco Massimo Poncino Department of Control and Computer Engineering Politecnico di Torino Turin Italy IMEC Leuven Belgium

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Accurate State of Health (SoH) estimation is indispensable for ensuring battery system safety, reliability, and run-time monitoring. However, as instantaneous runtime measurement of SoH remains impractical when not unfeasible, appropriate models are required for its estimation. Recently, various data-driven models have been proposed, which solve various weaknesses of traditional models. However, the accuracy of data-driven models heavily depends on the quality of the training datasets, which usually contain data that are easy to measure but that are only partially or weakly related to the physical/chemical mechanisms that determine battery aging. In this study, we propose a novel feature engineering approach, which involves augmenting the original dataset with purpose-designed features that better represent the aging phenomena. Our contribution does not consist of a new machine-learning model but rather in the addition of selected features to an existing model. This methodology consistently demonstrates enhanced accuracy across various machine-learning models and battery chemistries, yielding an approximate 25% SoH estimation accuracy improve-ment. Our work bridges a critical gap in battery research, offering a promising strategy to significantly enhance SoH estimation by optimizing feature selection.

关键词： Training Technological innovation Estimation Machine learning Aging Feature extraction Data models

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X-OAR: Orchestration of Access Resources for eXtended Reality Educational Applications

X-OAR: Orchestration of Access Resources for eXtended Realit...

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IFIP Networking Conference

作者： Alessandro Priviero Luca Mastrandrea Ioannis Chatzigiannakis Stefania Colonnese Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Department of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (数字)9783903176638

ISBN: (纸本)9798350390605

Extended reality offers unprecedented learning and training occasions, and unique challenges related not only to throughput and delay, but also to the characteristic spatial concentration of trainees. We have developed an algorithm for eXtended reality oriented Orchestration of Access Resources (X-OAR) grounding on next generation network technologies. X-OAR is designed to efficiently allocate edge computing facilities and cooperatively scheduled radio access network resources for extended reality applications. Building on the 3GPP guidelines on quality of experience in XR services, X-OAR meets the stringent XR delay requirements by leveraging edge and radio resources and employing cooperative scheduling within the radio access network. We introduce a graph model of the X-OAR optimization problem, and we present the X-OAR greedy algorithm, that reduces the orchestration complexity and the dependency on user subscription information. Experimental results show that X-OAR, with its cooperative scheduling technique, outperforms state-of-the-art competitors in terms of XR quality of experience. X-OAR paves the way for further studies extending the system orchestration to the application layer and the related resource charging policy.

关键词： Training Extended reality Processor scheduling Throughput Delays Quality of experience Optimization

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Parameter Estimation of Transient Voltage Signals with an Unscented Kalman Filter

Parameter Estimation of Transient Voltage Signals with an Un...

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2022 North American Power Symposium, NAPS 2022

作者： Sharifi, Fatemeh Mehrizi-Sani, Ali Tehrani, Kambiz The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States Normandy University-ESIGELEC-IRSEEM The Department of Energy and Control Rouen France

ISBN: (纸本)9781665499217

Online parameter estimation of ringdown signals is crucial for analyzing the stability of the power system. This paper aims to estimate the frequency and damping ratio of a transient voltage signal using an unscented Kalman filter (UKF). Traditionally, transient signals in the power system are modeled as the sum of exponentially damped sinusoidal signals, and previous work has been performed to estimate parameters by linearizing the model. However, the proposed algorithm in this paper can approximate parameters without assuming linearity. The performance of this method is evaluated on the IEEE 39-bus system simulated on the PSCAD software connected to the UKF algorithm written in the Python programming language. Simulation results indicate the proposed method detects any fluctuation in parameters accurately. © 2022 IEEE.

关键词： Parameter estimation

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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A network-based bioinformatic analysis for identifying potential repurposable active molecules in different types of human cancers

A network-based bioinformatic analysis for identifying poten...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Brunetti, Martina Paci, Paola Fiscon, Giulia Sapienza University of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Karolinska Institutet Stockholm Sweden National Research Council of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' Rome Italy

ISBN: (纸本)9798350337488

Drug repurposing, also known as drug repositioning, is the process of identifying novel therapeutic indications for existing drugs, offering a cost-effective and time-efficient strategy to drug discovery. In this context, we developed a network-based algorithm, named SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by accounting for the interaction between the drug targets and disease-associated genes in the human interactome, implementing a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Following its successful applications to different disorders (such as viral infections and neurological diseases), in this study, we applied SAveRUNNER on a panel of 13 types of cancers using both disease-associated genes downloaded from widely-used databases and from gene expression data. © 2023 IEEE.

关键词： Drug Repurposing Network Medicine Network Theory

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Deep Reinforcement Learning control of Type-1 Diabetes with Cross-Patient Generalization

Deep Reinforcement Learning Control of Type-1 Diabetes with ...

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Mediterranean Conference on control and Automation (MED)

作者： Mohab M. H. Atanasious Valentina Becchetti Federico Baldisseri Danilo Menegatti Andrea Wrona Department of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Rome Italy

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Type 1 diabetes is one of the major concerns in current medical studies, as the World Health Organisation plans to reduce mortality due to such disease by one third by 2030. Standard clinical practice involves self-administered injections of insulin, while current research in the field of automatic control of blood glucose concentration mostly focuses on model-based control techniques. This work presents an application of a Deep Reinforcement Learning-based controller for autonomous treatment of type 1 diabetes, building on the Deep Determin-istic Policy Gradient algorithm. Such control framework is applied for the first time on the Python implementation of the UVA/Padova simulator, named Simglucose. The proposed methodology is validated through in-vitro simulations on an inter-cluster cross-generalization group of virtual adult patients, showing that normoglycemia is successfully preserved while assuring cross-patient generalization and outperforming clinical practice, without the direct knowledge of the amount of ingested carbohydrates.

关键词： Training Pediatrics Robustness Stability analysis Diabetes Safety Glucose

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