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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Optimal Scheduling of Distributed Energy Resources Aggregators in Energy and Flexibility Markets

Optimal Scheduling of Distributed Energy Resources Aggregato...

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Australasian Universities Power engineering Conference, AUPEC

作者： Afshin Najafi-Ghalelou Mohsen Khorasany M. Imran Azim Reza Razzaghi Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

This paper investigates the stochastic scheduling of a Distributed Energy Resources Aggregator participating in wholesale day-ahead energy and flexibility markets. The proposed framework focuses on maximizing the aggregator’s profit while representing entities such as PV-based prosumers, electric vehicles, and large-scale battery within an unbalanced distribution network. It incorporates uncertainties related to PV generation, market prices, and flexibility deployment requests. In addition to profit maximization, the framework manages the distribution network’s voltage and line congestion while meeting the basic energy demand of the players. The aggregator leverages the flexibility of these players to participate in the flexibility electricity market, enabling it to adjust its total power production as requested by the flexibility market operator. The study evaluates two cases: Case 1, where the aggregator participates only in the energy market, and Case 2, where the aggregator joins both energy and flexibility markets. The results show that participating in both markets significantly increases profit, while maintaining compliance with the distribution network’s voltage and thermal limits and meeting participants’ demand requirements.

关键词： Power engineering Uncertainty Stochastic processes Optimal scheduling Distribution networks Voltage Production Electricity supply industry Batteries Distributed power generation

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Gram-Schmidt Methods for Unsupervised Feature Selection 63

Gram-Schmidt Methods for Unsupervised Feature Selection

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Yaghooti, Bahram Raviv, Netanel Sinopoli, Bruno Washington University in St. Louis Department of Electrical and Systems Engineering St. LouisMO United States Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO United States

ISBN: (纸本)9798350316339

Unsupervised feature selection is a critical task in data analysis, particularly when faced with high-dimensional datasets and complex and nonlinear dependencies among features. In this paper, we propose a family of Gram-Schmidt feature selection approaches to unsupervised feature selection that addresses the challenge of identifying non-redundant features in the presence of nonlinear dependencies. Our method leverages probabilistic Gram-Schmidt (GS) orthogonalization to detect and map out redundant features within the data. By applying the GS process to capture nonlinear dependencies through a pre-defined, fixed family of functions, we construct variance vectors that facilitate the identification of high-variance features, or the removal of these dependencies from the feature space. In the first case, we provide information-theoretic guarantees in terms of entropy reduction. In the second case, we demonstrate the efficacy of our approach by proving theoretical guarantees under certain assumptions, showcasing its ability to detect and remove nonlinear dependencies. To support our theoretical findings, we experiment over various synthetic and real-world datasets, showing superior performance in terms of classification accuracy over state-of-the-art methods. Further, our information-theoretic feature selection algorithm strictly generalizes a recently proposed Fourier-based feature selection mechanism at significantly reduced complexity. © 2024 IEEE.

关键词： Feature Selection

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On Comparing Interference Impacts

On Comparing Interference Impacts

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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Sanders, Aric Pirrone, Michelle Forsyth, M. Keith Wunderlich, Adam National Institute of Standards and Technology Communications Technology Laboratory BoulderCO United States University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO United States

ISBN: (纸本)9798350360394

Interference between communication systems is a critical issue that can impact performance and operability of devices. A wide range of methods and testbeds have been developed to study susceptibility to interference, and each of these testbeds and their experimental test schedules can have different factors and responses. Although there is a variety in testbed design and measurement, it is common to choose a response that is assumed to vary with signal-to-noise ratio. A fundamental issue in any interference study of this type is the assignment of the power of the interference signal in a way that represents the desired environmental situation. This can be a challenging task for signals with disparate time, frequency and power dynamics. In particular, whether to assign a maximum (peak power) or a time averaged power becomes ambiguous for inter-interference signal comparisons. To address this problem we propose a quantitative scale based on the impact on the receiver of interest. We demonstrate how to form an interference signal agnostic scale and apply that scale for different interference situations. This scale, impact equivalent power ratio, can be transformed into an impact equivalent power with knowledge of the effective noise figure of the system under test. © 2024 IEEE.

关键词： Testbeds

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Multimodal Human Detection using RGB, Thermal and LiDAR modalities for Robotic Perception

Multimodal Human Detection using RGB, Thermal and LiDAR moda...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Kennedy O. S. Mota Luís Garrote Cristiano Premebida Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

People detection is a relevant research topic in artificial perception with wide range of applications from security, surveillance, robotics to autonomous driving. Overcoming challenges in this field involves advanced algorithms, combination of machine learning approaches, as well as the use of sensory data e.g., from cameras and LiDARs. This work addresses the problem of people detection using YOLO, a state-of-the-art object detection method, trained on three distinct data sources LiDAR, RGB (color) and ‘thermal’ (long-wave infra-red) images. The rationale for combining multiple-sensory representation relies on the assumption that each sensor has its own advantages and disadvantages, but together they normally complement each other - specially in real-world conditions. LiDAR contributes to a physically-interpretable mapping of the environment, providing precise information regarding size/dimension and location of the objects, while RGB and thermal provide relevant textural features. The sensors have been calibrated w.r.t. each other thus, allowing the LiDAR’s point-clouds to be projected into the image plane, followed by an up-sampling step, to create dense-depth maps (DM) that enable direct use of the YOLO framework. To support the experiments, a new multi-sensory dataset has been collected using a mobile robot. Besides single-modality models, this paper also explores early and late-fusion strategies. Finally, the new dataset has been made available in a Github repository 1 .

关键词： YOLO Laser radar Machine learning algorithms Surveillance Soft sensors Thermal engineering Robot vision systems Security Mobile robots Software development management

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Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima

arXiv

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arXiv 2023年

作者： Dixit, Rishabh Gürbüzbalaban, Mert Bajwa, Waheed U. Department of Electrical and Computer Engineering Departments of Electrical & Computer Engineering Management Science and Information Systems and Statistics United States Departments of Electrical & Computer Engineering and Statistics

This paper considers the problem of understanding the behavior of a general class of accelerated gradient methods on smooth nonconvex functions. Motivated by some recent works that have proposed effective algorithms, based on Polyak’s heavy ball method and the Nesterov accelerated gradient method, to achieve convergence to a local minimum of nonconvex functions, this work proposes a broad class of Nesterov-type accelerated methods and puts forth a rigorous study of these methods encompassing the escape from saddle-points and convergence to local minima through a both asymptotic and a nonasymptotic analysis. In the asymptotic regime, this paper answers an open question of whether Nesterov’s accelerated gradient method (NAG) with variable momentum parameter avoids strict saddle points almost surely. This work also develops two metrics of asymptotic rate of convergence and divergence, and evaluates these two metrics for several popular standard accelerated methods such as the NAG, and Nesterov’s accelerated gradient with constant momentum (NCM) near strict saddle points. In the local regime, this work provides an analysis that leads to the "linear" exit time estimates from strict saddle neighborhoods for trajectories of these accelerated methods as well the necessary conditions for the existence of such trajectories. Finally, this work studies a sub-class of accelerated methods that can converge in convex neighborhoods of nonconvex functions with a near optimal rate to a local minima and at the same time this sub-class offers superior saddle-escape behavior compared to that of NAG. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Defective Microstrip Structure Based Phase Shifter with Reduced Size for Phased Antenna Array Applications 12

Defective Microstrip Structure Based Phase Shifter with Redu...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Khan, Hajra Khan, Shahid Nasir, Jamal Ali Shah, Syed Ahson Dalarsson, Mariana Alibakhshikenari, Mohammad Comsats University Islamabad Department of Electrical and Computer Engineering Pakistan Hanyang University Applied Bioelectronics Laboratory Seoul Korea Republic of School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Spain

ISBN: (纸本)9798350358056

In this work, we propose a small-size and low-cost phase shifter based on defective microstrip structure (DMS) technique, with a modified reconfigurable unit cell (MRDMS) for WLAN applications at 5.2 GHz. The phase shifter is made up of an MRDMS unit, created by etching slots on a simple microstrip line with an outline of three bridges. To further increase the phase shift, a T-shaped slot is introduced in the central bridge and PIN diodes on the edges of the outer bridges are used. By switching the diodes on and off, the MRDMS unit cell is capable of operating in a variety of different modes to give a phase shift of 37.71° at 5.2 GHz. Additionally, MRDMS unit cells are cascaded for higher phase shift as required. Cascaded two- and four-cell phase shifters were designed and simulated in HFSS and a parametric study on various parameters was conducted to show the detailed operating properties of the proposed phase shifter. Compared to previously reported research, the present study shows that two cascaded phase shifters introduce 75.08° phase shifts and 151.75°, respectively. To the best of our knowledge, this is the maximum phase shift obtained by a phase shifter based on MRDMS. © 2023 IEEE.

关键词： defected microstrip structure phase shifter reconfigurable microwave system

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Assessing Peak Demand Reduction and Electricity Cost Savings of a Behind-the-Meter Battery for Commercial and Industrial Customers

Assessing Peak Demand Reduction and Electricity Cost Savings...

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Australasian Universities Power engineering Conference, AUPEC

作者： M. Imran Azim Mohsen Khorasany Reza Razzaghi Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

This paper presents an analysis of peak demand reduction and electricity cost savings for commercial and industrial (C&I) customers deploying behind-the-meter battery storage systems (BSSs). To do so, a BSS scheduling strategy is proposed that facilitates battery charging, both from the solar system and the grid, as well as discharging during demand charge-, peak time-of-use (ToU) rate-, and shoulder ToU rate-applicable periods in succession to peak demand and maximize cost savings. The proposed grid charging is conducted during off-peak ToU periods by taking factors like possible solar supply, BSS size and available BSS capacity, and demand during peak and shoulder periods into account. Finally, the performance of the proposed charge and discharge scheduling of the BSS is investigated for three C&I customers with different demand profiles. The simulation results are also compared with the business as usual, in which C&I customers buy and sell energy at ToU and feed-in-tariff (FiT) rates, respectively, without possessing any BSS at their premises. It is observed from the case study that the reductions in peak demand and electricity costs vary between 35.62% and 98.07% and between 4.61% and 11.01%, respectively.

关键词： Power engineering Electric potential Costs Job shop scheduling Electricity Simulation Discharges (electric) Batteries Solar system Business

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Resilient Operation of DC Standalone Microgrids: Experimental Validation of Hybrid RNN-PI Control for Pulse Load Management

Resilient Operation of DC Standalone Microgrids: Experimenta...

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2025 IEEE Green Technologies Conference, GreenTech 2025

作者： Aghmadi, Ahmed Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798331520182

Effective energy storage system (ESS) management is critical for enhancing the performance of standalone DC microgrids, particularly when integrating renewable energy sources such as photovoltaic (PV) or wind systems. This research describes a new hybrid control technique to improve DC microgrid operations' efficiency and resiliency, especially when handling critical loads. This research presents a hybrid closed-loop control technique that uses Recurrent Neural Networks (RNNs) and Proportional-Integral (PI) controllers to address the complex issues of dynamic load situations and transient disturbances. The RNN evaluates voltage errors to generate accurate reference currents, which the PI controllers then adjust to improve the performance of the bidirectional converter. Unlike standard PI-PI controls, our hybrid approach is particularly effective at regulating pulsed power loads (PPLs) and enhancing system response to transitory situations. The experimental results show that the RNN-PI control scheme significantly improves voltage regulation and transient responsiveness, exceeding conventional performance and resilience techniques. This approach provides a convincing solution for enhancing microgrid systems' stability and efficiency in various real-world applications. © 2025 IEEE.

关键词： Electric load distribution

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