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Data-driven electrical resistance tomography for robotic large-area tactile sensing

Science China(Information Sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

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Enhancing Solar Energy Production Forecasting with Ensemble-based Learning Techniques 6

Enhancing Solar Energy Production Forecasting with Ensemble-...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Abdelsalam, Hany Souri, Alireza İnanç, Nihat Department of Software Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey

ISBN: (纸本)9798331539603

The global trend in renewable energy solutions has emphasized the urgent need for accurate forecasting of solar energy production. This study examines the potential of ensemble-based learning techniques in predicting solar energy production by leveraging historical data collected from 11 solar photovoltaic installations in Calgary, Canada, spanning from September 2015 to March 2023. The dataset was carefully preprocessed to handle missing values, duplicates, and to extract meaningful features that align with the nature of time series data. A range of ML models were implemented and assessed by several forecasting evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). The experimental results demonstrate the superior performance of ensemble methods, particularly XGBoost and Light GBM, in solar energy forecasting. XGBoost achieved the best performance, with an MAE of 25.36, an MSE of 3599.66, and R2 of 0.957, outperforming models such as Random Forest and Neural Networks. The practical implications of these findings suggest that accurate solar energy forecasting can significantly enhance operational planning and optimize energy management in solar power plants, contributing to more reliable and efficient renewable energy systems. © 2024 IEEE.

关键词： ensemble methods machine learning renewable energy solar energy forecasting time series analysis XGBoost

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Graph-Segmenter:graph transformer with boundary-aware attention for semantic segmentation

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Frontiers of computer Science 2024年第5期18卷 97-108页

作者： Zizhang WU Yuanzhu GAN Tianhao XU Fan WANG Computer Vision Perception Department of ZongMu Technology Shanghai 201203China Faculty of Electrical Engineering Information TechnologyPhysicsTechnical University of BraunschweigBraunschweig 38106Germany

Thetransformer-based semantic segmentation approaches,which divide the image into different regions by sliding windows and model the relation inside each window,have achieved outstanding ***,since the relation modeling between windows was not the primary emphasis of previous work,it was not fully *** address this issue,we propose a Graph-Segmenter,including a graph transformer and a boundary-aware attention module,which is an effective network for simultaneously modeling the more profound relation between windows in a global view and various pixels inside each window as a local one,and for substantial low-cost boundary ***,we treat every window and pixel inside the window as nodes to construct graphs for both views and devise the graph *** introduced boundary-awareattentionmoduleoptimizes theedge information of the target objects by modeling the relationship between the pixel on the object's *** experiments on three widely used semantic segmentation datasets(Cityscapes,ADE-20k and PASCAL Context)demonstrate that our proposed network,a Graph Transformer with Boundary-aware Attention,can achieve state-of-the-art segmentation performance.

关键词： graph transformer graph relation network boundary-aware attention semantic segmentation

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Fractal Dimension Analysis Demonstrates Overestimation and Underestimation of Time in EEG Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Azari, Maryam Mollazadeh Sarbaz, Yashar Koohestani, Behrooz Farzamnia, Ali Modeling Biological System’s Laboratory Department of Biomedical Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Keeping track of time is regarded as an essential human behavior. The question of how the brain deals with temporal information remains a subject of scholarly debate. The current investigation aims to explore the mechanism underlying time perception by extracting fractal dimension from an electroencephalogram (EEG) signal and its frequency bands. To accomplish this, Higuchi’s fractal dimension was calculated for 42 healthy subjects’ electroencephalogram (EEG) signal and its sub-bands during the time perception task. The EEG signal was recorded from 19 channels. Subsequently, a statistical analysis was conducted to compare participants who underestimated versus those who overestimated the elapsed time and significantly different channels were presented. The findings suggest an elevated level of fractal dimensionality in persons who displayed a tendency to overestimate time. The EEG signal and Gamma rhythm emerged as the most distinguishing signals between the two cohorts. The contrast in fractal dimension between the two groups was predominantly apparent in the parietal and central channels. To summarize, an increased level of complexity is discernible in the EEG signal and high-frequency rhythms when there is an overestimation of temporal duration. It can be asserted that the employment of FD yields presents an exceptional approach to comprehending cerebral functionalities, notably temporal perception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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A Survey on Wind Speed Forecasting in Smart Grids Using Deep Learning Algorithm Applications 28

A Survey on Wind Speed Forecasting in Smart Grids Using Deep...

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28th International electrical Power Distribution Conference, EPDC 2024

作者： Majidi-Gharehnaz, Hassan Hayati, Mohammad Mohsen Khalilzadeh, Reza Shahinzadeh, Hossein Abapour, Mehdi Gharehpetian, Gevork B. University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Tarbiat Modares University Department of Electrical Engineering Tehran Iran Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350385502

The crisis resulting from the use of fossil fuels has heightened the push towards increasing the share of renewable energy production, particularly from sources like wind and solar power. While wind energy holds special significance due to its potential availability round the clock, relying on wind power comes with various challenges. One such challenge is the uncertainty surrounding the production levels of wind farms. In response to this challenge, researchers have consistently sought ways to predict wind speed, with one of the most crucial methods being the application of machine learning techniques. These data-driven methods exhibit a commendable ability to forecast wind speed by leveraging historical data and other relevant inputs. Recent years have witnessed substantial advancements in deep learning methods, further enhancing the appeal of these approaches. To achieve this objective, this article endeavors to explore the role of applying deep learning methods in wind speed forecasting. A comprehensive survey of the application of these algorithms is conducted, and ultimately, a comparative analysis of these methods is performed, taking into account the specific types of wind speed forecasting problems. To achieve this, the article delves into the examination of nine machine learning methods, namely convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), deep belief network (DBN), physics-informed neural networks (PINN), Autoencoder, generative adversarial network (GAN), deep reinforcement learning (DRL), and hybrid models. The goal is to provide insights into the effectiveness of these approaches and to facilitate a comprehensive understanding of their application in addressing wind speed forecasting challenges. © 2024 IEEE.

关键词： Wind power

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Multi-Input Multi-Output High Step-Up DC-DC Converter with Low Voltage Stress on Semiconductors 4

Multi-Input Multi-Output High Step-Up DC-DC Converter with L...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Ndermohammadi, Ali Abolhassani, Pouya Hashemzadeh, Seyed Majid Ansari, Md Fahim Iqbal, Atif Hosseini, Seyed Hossein Babaei, Ebrahim University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Graphic Era Deemed to be University Department of Electrical Engineering Dehradun India Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350383997

This article introduces a Multi-Input Multi-Output (MIMO) high step-up transformerless DC-DC circuit. Designed for a broad spectrum of applications, the proffered configuration facilitates the integration of multiple hybrid voltage sources to power diverse loads with varying voltage requirements. Simultaneous regulation of all output voltages across the output ports is achieved by tuning individual controlling parameters. To enhance the analysis, a careful investigation is conducted into the single-input, dual-output (SIDO) operation of the proffered circuit. To validate the theoretical findings, a mock-up demonstrating the SIDO operation of the circuit is implemented for a 20V input voltage, producing output voltages of 100V and 195V, with a total power output of 600W. © 2024 IEEE.

关键词： Voltage-source converters

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Semi-global weighted output average tracking of discrete-time heterogeneous multi-agent systems subject to input saturation and external disturbances

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Control Theory and Technology 2023年第3期21卷 315-333页

作者： Qilin Song Yuanlong Li Yijing Xie Zongli Lin Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Department of Electrical Engineering University of Texas at ArlingtonArlington 76019TXUSA Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesville 22904-4743VAUSA

In this paper,we revisit the semi-global weighted output average tracking problem for a discrete-time multi-agent system subject to input saturation and external *** multi-agent system consists of multiple heterogeneous linear systems as leader agents and multiple heterogeneous linear systems as follower *** design both the state feedback and output feedback control protocols for each follower *** particular,a distributed state observer is designed for each follower agent that estimates the state of each leader *** the output feedback case,state observer is also designed for each follower agent to estimate its own *** these estimates,we design low gain-based distributed control protocols,parameterized in a scalar low gain *** is shown that,for any bounded set of the initial conditions,these control protocols cause the follower agents to track the weighted average of the outputs of the leader agents as long as the value of the low gain parameter is tuned sufficiently *** results illustrate the validity of the theoretical results.

关键词： Distributed average tracking Input saturation Low gain feedback Heterogeneous multi-agent systems Output regulation

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Design and Implementation of an Underactuated Biped Robot Prototype on Compliant Ground 8

Design and Implementation of an Underactuated Biped Robot Pr...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhou, Qian Yin, Xiong Guo, Zhilong Pei, Yu Feng, Zhao Yao, Daojin East China Jiaotong University Department of Electrical and Automation Engineering China Wuhan University Faculty of the School of Power and Mechanical Engineering China East China Jiaotong University Faculty of the Department of Electrical and Automation Engineering China

ISBN: (纸本)9798350300178

Considering the influence of the actual compliant grounds on the stable walking of the underactuated robot, a prototype of the planar underactuated biped walking robot is designed in this paper, and the simulation and experiment are carried out. Firstly, a biped robot model is established, and the compliant ground is equivalent to a spring-damping system. An adaptive feedforward control strategy is adopted to realize stable underactuated walking by controlling the robot's centroid velocity. Then, according to the control strategy, a 600mm high, 5.62kg weight planar underactuated robot prototype was produced, including the mechanical structure and software system. Finally, the underactuated walking with the step size of 0.1839m is realized through simulation and robot prototype experiment. The experimental results show that the robot prototype designed in this paper can carry out underactuated walking on different compliant grounds. © 2023 IEEE.

关键词： Machine design

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Solving dynamic optimization problems using parent–child multi-swarm clustered memory (PCSCM) algorithm

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Neural Computing and Applications 2024年第31期36卷 19549-19583页

作者： Mohammadpour, Majid Mostafavi, Seyedakbar Mirjalili, Seyedali Department of Computer Engineering Yazd University Yazd Iran Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm’s performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity;while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This n

关键词： Chaos theory

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Fetal health classification by support vector machine based on feature vector optimization 10

Fetal health classification by support vector machine based ...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Fotouhi sefidi, Mahdiyeh Pouramirarsalani, Shahrzad Rajebi, Saman Department of Electrical Engineering Seraj Higher Education Institute Tabriz Iran Faculty of Electrical and Computer Engineering university of Tabriz Tabriz Iran

ISBN: (纸本)9798350348873

In recent years, diagnosis of fetal health has prevented fetal hypoxia at birth and reduced infant mortality. The acquired data from Cardiotocography (CTG) signals are used to monitor and evaluate the status of the fetus during pregnancy and delivery. Visual analysis of CTG signals using common guidelines is difficult and time-consuming due to the physiological complexity of the fetus. Artificial intelligence-based computer-aided diagnosis (CAD) systems can be useful in objective medical decision-making and digital health. In this paper, an optimized SVM classification method is proposed, which was used to classify UCI fetal health data. The optimization process was based on the genetic algorithm and the FDR relationship, in which the importance of all the features was obtained in the form of coefficients. By applying these coefficients to the feature vector of the data set, the separation of data into different classes is improved. First, the support vector machine (SVM) algorithm was used without optimization for data classification, and the correct classification rate (CCR) was 88.28. Then, by normalizing, whitening and removing outlier data in the pre-processing stage, the accuracy increased to 90.30. By applying the feature value coefficients to the input of the class band model with the aim of optimizing the feature vector, the classification accuracy increased to 93.98. ©2024 IEEE.

关键词： Genetic algorithms

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