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arXiv 2023年

作者： Lekeufack, Jordan Angelopoulos, Anastasios N. Bajcsy, Andrea Jordan, Michael I. Malik, Jitendra Department of Statistics UC Berkeley United States Department of Electrical Engineering and Computer Science UC Berkeley United States Robotics Institute Carnegie Mellon University United States

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever;the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans, automated stock trading, and robot manufacturing. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Explainable Symbolic Regression Model for Tool Wear Diagnosis 9

Explainable Symbolic Regression Model for Tool Wear Diagnosi...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Mishra, Debasish Han, Seulki Pattipati, Krishna R. Bollas, George M. University of Connecticut Department of Electrical & Computer Engineering 371 Fairfield Way StorrsCT06269 United States Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut 159 Discovery Dr. StorrsCT06269 United States University of Connecticut Department of Chemical & Biomolecular Engineering 159 Discovery Dr. StorrsCT06269 United States

ISBN: (纸本)9798350311402

In precision machining, predicting the tool health can improve productivity, job quality, and reduce machine downtime and energy consumption. While deep learning (DL) algorithms have garnered recent interest, they lack the physics understanding associated with machining processes. To address this limitation, we present a symbolic regression framework for tool wear diagnostics. The method explores analytical symbolic expressions using health indicators and cutting settings. Tool health indicators are computed from wavelet subspaces of vibration signals by applying a distance metric to the wavelet coefficients. These indicators are strongly correlated with tool wear measurements, making them suitable for tool wear diagnostics. We applied the developed framework to the IEEE PHM 2010 data, which comprises three sets of run-to-failure machining tests conducted with three tools. The framework predicted tool wear with an R2 of 0.947 and a mean absolute error (MAE) of 0.006 mm across test sets. The results demonstrate the effectiveness of the symbolic regression approach for tool wear diagnostics, showcasing the richness of information in the indicators and the quality of the developed model for tool wear estimation. © 2023 IEEE.

关键词： Regression analysis

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Application of LabVIEW to Determine Characteristics of Two-Terminal Passive Components 14

Application of LabVIEW to Determine Characteristics of Two-T...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Otomanski, Przemyslaw Pawlowski, Eligiusz Szlachta, Anna Poznan University of Technology Institute of Electrical Engineering and Electronics Piotrowo street 3A Poznań60-965 Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Nadbystrzycka street 38A Lublin20-618 Poland Rzeszow University of Technology Department of Metrology and Measurement Systems W. Pola street 2 Rzeszow35-959 Poland

ISBN: (纸本)9788097262976

The paper discusses the topic of determining the current-voltage characteristics of two-terminal passive components. A virtual measurement instrument created in the LabVIEW environment using the NI USB-6211 data acquisition device is presented, as well as the results of tests performed for example elements. The described approach can also be applied to other measurement situations, such as for studying the self-heating effect of RTD. © 2023 institute of Measurement Science, Slovak Academy o.

关键词： Current voltage characteristics

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Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction

arXiv

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arXiv 2023年

作者： Pandya, Ravi Wang, Zhuoyuan Nakahira, Yorie Liu, Changliu Robotics Institute Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot’s behavior, but such models do not account for the influence the robot has on the human’s actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot’s behavior based on a conditional model of the human that depends on the robot’s future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human’s intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and safe trajectory generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users’ interactions with a robot collaborator quantitatively and qualitatively. © 2023, CC BY.

关键词： Microrobots

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Reliable Avionics Power System for Commercial Autonomous Science (CAS) microrovers 73

Reliable Avionics Power System for Commercial Autonomous Sci...

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73rd International Astronautical Congress, IAC 2022

作者： Khurana, Saksham Schweitzer, Lydia Newman, Kyle J. Guevara, Emilio Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA United States School of Computer Science Robotics Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA United States Department of Mechanical Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA United States

The realm of planetary exploration is gravitating towards a smaller class of rover. These microrovers are technically and scientifically ambitious and offer high return on investment. With mission resource constraints and limited mass, size, power, and communications, these rovers must leverage light-weight yet robust power systems which enable autonomous exploration initiatives, environmental survivability, fault detection, isolation, recovery, and ultimately mission success. This paper presents methodology, design, analysis and results pertaining to the power system of MoonRanger, the first lunar microrover for measuring ice at the Moon's south pole on a NASA Commercial Lunar Payload Services (CLPS) mission. Radioisotope Thermoelectric Generator (RTG) power that pertains to large rovers is not viable for microrovers due to mass, budget and availability. Instead, the rover is equipped with a custom designed solar panel with 30.7% cell efficiency able to sustain the rover's battery system for a full lunar day. Coupled with 200 Wh of lithium ion battery power, the rover's power system supports autonomous exploration into darkness for up to 4 hrs of dark survival. This custom power system is a fraction of the mass and cost of a traditional rover. Power generated by the solar panel is distributed to the rest of the rover system via compartmentalized power switching groups. This compartmentalization improves fault isolation for the rover's on-board fault detection and mitigation system as well as for operators on Earth. For missions with lower budget and time, like MoonRanger, rigorous testing and purchasing of space-rated parts is not as feasible. This power system organization can also be used to isolate the various power switching groups allowing for simpler testing, integration, and operational mode configuration. This paper describes how the rover's solar panel attributes, power generation, analysis, distribution, and storage systems facilitate MoonRanger's ambitio

关键词： NASA

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

Carbon-Aware Computing in a Network of Data Centers: A Hiera...

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European Control Conference (ECC)

作者： Enno Breukelman Sophie Hall Giuseppe Belgioioso Florian Dörfler Division of Decision and Control Systems KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zurich Zürich Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach success-fully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling.

关键词： Data centers Processor scheduling Computational modeling Carbon dioxide Games Supervisory control Resource management

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Moving Target Defense Approach for Secure Relay Selection in Vehicular Networks

TechRxiv

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TechRxiv 2023年

作者： Ghourab, Esraa M. Naser, Shimaa Muhaidat, Sami Bariah, Lina Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Damiani, Ernesto The KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University United Arab Emirates The Department of Systems and Computer Engineering Carleton University Canada Technology Innovation Institute 9639 Masdar City Abu Dhabi United Arab Emirates Center Department of Electrical Engineering and Computer Science Khalifa University United Arab Emirates The Department of Electrical Engineering Tampere University Finland

Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task, due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. The moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions, based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of the network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks has several limitations, such as they are not well-suited for highly dynamic environments;they require high synchronization modules that are resource-intensive and difficult to implement;and finally, they rely heavily on the attack-defense models, which may not always be accurate or comprehensive to use. In this paper, we propose an intelligent spatiotemporal diversification MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. Specifically, we design benign random data injection patterns to meet the security and reliability requirements of the vehicular network. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. In order to optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are c

关键词： Iterative methods

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Secured Cluster-Based Electricity Theft Detectors Against Blackbox Evasion Attacks

Secured Cluster-Based Electricity Theft Detectors Against Bl...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Islam Elgarhy Ahmed T. El-Toukhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Maazen Alsabaan Hisham A. Kholidy Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

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Optimal Placement of Voltage Regulator and Capacitor for Distribution System

Optimal Placement of Voltage Regulator and Capacitor for Dis...

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2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Rajalakshmi, M. Addisu, Molla Jeyakkannan, N. Renuka Devi, G. Karthik, C. Jyothi Engineering College Department of Mechatronics Thrissur India Woldia University Department of Electrical and Computer Engineering Ethiopia Manakula Vinayagar Institute of Technology Deprtment of Robotics and Automation Puduchery India

ISBN: (纸本)9781665439862

The radial distribution system (RDS) is the ultimate link between the high-voltage transmission system (HVT) and the user in most electrical systems in Ethiopia. The Distribution System (DS) is divided into different areas for proper operation and supply of electricity demand. The study in this paper applies specifically to the Gondar feeder, which consists of 60 nodes with 3,413 MW of total capacity, in power distribution lines. For the modulation of voltages and DS node power loss indices, the fuzzy membership functions are used. A fluid expert system (FES) with a collection of heuristic legislation will determine the voltage regulator and the capacitor suitability index. The proposed method discusses and compares the effectiveness and results obtained from the voltage control system and the optimal placement of capacitors. © 2021 IEEE.

关键词： Voltage regulators

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