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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Gao, Chengzhi Xie, Ye Zhu, Shiqiang Huang, Guanyu Kong, Lingyu Xie, Anhuan Gu, Jianjun Zhang, Dan Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China York University North YorkM3J 1P3 Canada Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

At present, more and more attention has been paid to the exploration of biped robots. As the precondition of biped stable walking, the state estimation of robot has also received the attention of many experts. Biped robot has special motion characteristics, has a hybrid dynamic system and belongs to a floating base, so it must obtain the contact information of two feet before realizing the motion state estimation of the robot. The contribution of this paper is to introduce soft support vector machine to detect contact phase. This method based on support vector machine (SVM) eliminates the need for force sensors on two feet because they are vulnerable to the impact between the foot and the contact surface. In addition, precise dynamic modeling is not required. Furthermore, this paper uses SVM to obtain the touchdown information, and proposes a state estimation method using extended Kalman filter to combine motion estimation with IMU data. Finally, the touchdown detection algorithm and the state estimation algorithm are verified through experiments, and the accuracy and feasibility of the SVM method are proved through the touchdown detection experiments. Based on this, the full state estimation of the robot is realized through the state estimation experiments. © 2022 IEEE.

关键词： State estimation

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Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

arXiv

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arXiv 2024年

作者： Salazar, Teresa Gama, João Araujo, Helder Abreu, Pedro Henriques The Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal INESC TEC The Faculty of Economy University of Porto Porto4200-465 Portugal The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3004-531 Portugal

In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

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A Review of Artificial Intelligence in Education 2

A Review of Artificial Intelligence in Education

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2nd IEEE International Conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Ubah, Adaeze Eveln Onakpojeruo, Efe Precious Ajamu, Janet Mangai, Teyei Ruth Isa, Adam Muhammad Ayansina, Nurudeen Bode Al-Turjman, Fadi Department of Computer Information Systems Near East University Nicosia Cyprus Department of Biomedical Engineering Near East University Cyprus Operational Research Centre in Healthcare Near East University TRNC Mersin 10 Nicosia Turkey Ai Engineering Department Research Centre for Ai and IoT Ai and Robotics Institute Near East University Mersin10 Turkey Research Centre for Ai and IoT University of Kyrenia Mersin10 Turkey

ISBN: (纸本)9798350334104

Nowadays, cognitive qualities such as learning, adaptation, and decision-making are present in computers, robots, and other artificially modified objects. This was made possible by the development of artificial intelligence (AI)-based technology. The adoption and use of AI in the educational system, particularly by educational institutions, has been widespread and has taken several forms. AI systems have developed from standard Web technologies that have to do with computer systems and online intelligent systems, then with the aid of embedded computer systems and other technologies, to humanoid robots, web-based chatbots, duties/functions instructors either alone or in collaborations, etc. This study aimed to determine how AI systems impact and advance educational systems. Based on a narrative and structure for evaluating AI systems that are identified through analysis, the study primarily focuses on the uses, benefits, impact, and application of AI in teaching, learning, and management of activities in an educational system. The advantages of AI in the educational industry and its future possibilities are discussed in this study. The results of this study would increase teachers' efficiency and effectiveness in managing tasks like assessing and grading student projects and assignments as well as the standard of their instructional activities. © 2022 IEEE.

关键词： Students

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Impact of an electric vehicle, solar PV, and battery energy storage system in a distribution system

Impact of an electric vehicle, solar PV, and battery energy ...

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Power engineering Conference (SAUPEC), Southern African Universities

作者： Saheed Lekan Gbadamosi Chukwuemeka Emmanuel Okafor Senthil Krishnamurthy Mukovhe Ratshitanga Prathaban Moodley Department of Electrical Electronics and Computer Engineering Centre for Intelligent Systems and Emerging Technologies Cape Peninsula University of Technology Bellville South Africa South African National Energy Development Institute (SANEDI) Sandton South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

The modern distribution power system has witnessed a tremendous increase in integrating renewable energy sources (wind and solar photovoltaic), electric vehicles, and battery energy storage systems. This is consequent upon the commitment of many countries (around the world) to reduce global carbon emissions and ensure an acceptable global temperature increase. This research focuses on the impacts of the integration of solar photovoltaic (PV) systems, electric vehicles (EVs), and battery energy storage systems (BESS) on the distribution system while minimizing the voltage deviations and power losses of the considered distribution network. Through a time-based Power System Simulation tool, the study evaluates four configurations to assess the technical impacts on the distribution network. The Simulation results showed that electric vehicle integration leads to increased power losses and a reduction in voltage deviations. Integrating battery energy storage systems into the distribution system increases the power losses (from 0.9222 MW to 1.602 MW) depending on the location of the battery energy storage system in the distribution network. Therefore, the study reveals the need for optimal sizing and placement of battery energy storage systems in the distribution network for optimal system performance.

关键词： Photovoltaic systems Renewable energy sources Battery energy storage system Wind energy System performance Simulation Voltage Distribution networks Electric vehicles Power systems

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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IEEE International Conference on robotics and Biomimetics

作者： Tian Xiang Qiwei Meng Ji Zhang Beibei Zhang Wei Song Anhuan Xie Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong HKSAR China Research Center for Intelligent Computing Software Research Institute of Intelligent Computing Zhejiang Laboratory Hangzhou China Electrical and Computer Engineering Dalhousie University Halifax Canada

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model’s partitioning strategy. It also serves as an important metric influencing the model’s suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future.

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Preliminary Design and Performance Analysis of A Solar-Powered Jumping-Gliding Martian Aircraft

Preliminary Design and Performance Analysis of A Solar-Power...

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IEEE International Conference on robotics and Biomimetics

作者： Bin Lou Anhuan Xie Shiqiang Zhu Yaru Liu Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

A solar-powered jumping-gliding Martian aircraft is proposed as a new tool for Mars exploration, designed especially to perform long-term wide-range detection. A completely new way of continues detection performed by aircraft is created. A preliminary design of the jumping-gliding Martian aircraft is carried out while its flight performance is analyzed afterwards. The conceptual design of a large-aspect ratio foldable wing with a thin fuselage and a jumping device is put forward. The solar energy model, aerodynamic model and weight mode are all built. The energy balance models are established for the periods of detecting mission that the aircraft lands on the ground for harvesting solar energy and flies in the sky. The gliding performance of this aircraft is particularly discussed here.

关键词： Performance evaluation Potential energy Atmospheric modeling Photovoltaic cells Biological system modeling Solar energy Aerodynamics

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FEIF: Feature Excitation and Interactive Fusion for 6D Object Pose Estimation

FEIF: Feature Excitation and Interactive Fusion for 6D Objec...

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IEEE International Conference on robotics and Biomimetics

作者： Qiwei Meng Shiqiang Zhu Jianjun Gu Zheyuan Lin Ling Zhong Wei Song Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

In this paper, we present FEIF, a novel framework for 6D object pose estimation from a single RGBD image. Compared with previous approaches, this model fully leverages the complementary RGB and depth information through lay-erwise feature excitation and interactive fusion, thus improving the effectiveness and robustness of extracted features. The representative RGBD features can be subsequently applied for object keypoints prediction and pose computation. In addition, our FEIF includes a self-evaluation module to make robots aware of the confidence of pose estimation, so they are flexible to take the corresponding strategy for following tasks. Experiment results prove the SOTA performance of our model on LineMOD, Occlusion-LineMOD and YCB-Video datasets, and we also demonstrate its applicability and robustness in real-world robotic manipulation.

关键词： Technological innovation Computational modeling Biomimetics Pose estimation Feature extraction Robustness Data mining

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Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

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IEEE International Conference on robotics and Biomimetics

作者： Jian Zhang Yeheng Chen Shiqiang Zhu Yuehua Li Tian Xie Jason Gu Zhejiang Laboratory Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images.

关键词： Training Image recognition Annotations Biomimetics Pipelines Generators Robots

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Crashworthiness Design Optimization for an eVTOL Aircraft

Crashworthiness Design Optimization for an eVTOL Aircraft

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IEEE International Conference on robotics and Biomimetics

作者： Menglong Ding Anhuan Xie Shiqiang Zhu Wei Song Jiandong Cai Xufei Yan Pengyu Zhao Jason Gu Yanyan Zhang Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

The emergence of electric Vertical Takeoff and Landing (eVTOL) aircraft makes cheap, quiet and reliable short-range flights possible. As a manned aircraft, eVTOL's occupant protection capability is critical and its crashworthiness must be improved via optimization of energy absorption design for structures. This study aims to evaluate the crashworthiness of composite components and the entire eVTOL structure within the design space via the building-block approach. A variety of designs for the skid landing gear, energy absorber, structures in the cockpit and the combination of them were estimated by using the numerical method. The results show a tremendous reduction in occupant injury risk with the optimization of the skid landing gear, energy absorber and the entire eVTOL structure. This work provides a preliminary optimization design and evaluation methodology of crashworthiness for an eVTOL.

关键词： Absorption Gears Force Spine computer crashes Reliability Aircraft

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Comparative Analysis of Different Sliding Mode Control Approaches for Load Frequency Control in Edge of the Grid System

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Aich, Pritom Ahmed, Anis Shah, Rakibuzzaman Islam, Md Rabiul Roy, Naruttam Kumar Nasir, Ali Department of Electrical and Electronic Engineering Daffodil International University Dhaka Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh Federation University Australia Mt HelenVIC Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm-based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind-based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored. The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Robust control

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