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Online Learning in Blockchain-based Energy Trading Systems 24

Online Learning in Blockchain-based Energy Trading Systems

2024 International Symposium on Theory, Algorithmic Foundations, and Protocol design for Mobile Networks and Mobile Computing, MobiHoc 2024

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798400705212

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers’ strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers’ private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers’ private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers’ truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers’ equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by O(3√lnT/T) when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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Camera-LiDAR Inconsistency Analysis for Active Learning in Object Detection

Camera-LiDAR Inconsistency Analysis for Active Learning in O...

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IEEE Symposium on Intelligent Vehicle

作者： Esteban Rivera Ana Clara Serra Do Nascimento Markus Lienkamp Technical University of Munich Germany Department of Mobility Systems Engineering School of Engineering and Design Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Today, deep learning detectors for autonomous driving are delivering impressive results on public datasets and in real-world applications. However, these detectors require large amounts of data, especially labeled data, to achieve the performance needed to ensure safe driving. The process of collecting and tagging data is expensive and cumbersome. Therefore, the recent focus of the industry has been on how to achieve similar performance while limiting the amount of labeled data required to train such models. Within the cross-modal active learning paradigm, we propose and analyze new strategies to exploit the inconsistencies between camera and LiDAR detectors to improve sampling efficiency and label only the samples that promise improvements for model training. For this, we leverage the 2D projection of the bounding boxes to equalize the output quality of camera and LiDAR detections. Finally, we achieve up to 0.6% AP improvement for camera and 2% improvement for LiDAR over random sampling on the KITTI dataset using a sampling strategy based on the number of detected objects.

关键词： Training Industries Laser radar Three-dimensional displays Limiting Detectors Object detection

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A Novel Information-Theoretic Metric for Evaluating LiDAR Setups of Autonomous Vehicles

A Novel Information-Theoretic Metric for Evaluating LiDAR Se...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Philipp Hafemann Xulin Song David Brecht Technical University of Munich Germany Department of Mobility Systems Engineering School of Engineering & Design Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI)

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The sensor configuration of an autonomous vehicle (AV) is determined in the early development phase when specific perception algorithms are not yet available. Therefore, approaches based on synthetic raw data are necessary to evaluate different configurations. One sensor type used in AV is LiDAR, but developers should carefully consider the amount and placement of the sensors due to their high costs. In this contribution, we propose the Omni-Lidar Evaluation Score (OLES), a novel metric to evaluate different LiDAR configurations based on their simulated raw data. Our OLES metric combines information-theoretic quantities with coverage-based metrics, considering both the spatial coverage and the uniformity of a LiDAR point cloud distribution. We show the need for a new metric and provide details on implementing OLES using the open-source simulator CARLA. We demonstrate the effectiveness of our new metric in a simulation study and highlight its usefulness in the early phases of vehicle development. This research provides a means to evaluate the quality of LiDAR configurations and provides a basis for further optimizing sensor setups for AVs.

关键词： Measurement Point cloud compression Laser radar Costs Autonomous vehicles Cybernetics Information theory

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Solving Challenging Tasks with Manipulation Policy with Limited Demonstrations

Solving Challenging Tasks with Manipulation Policy with Limi...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Haofeng Liu Jiayi Tan Yanchun Cheng Yiwen Chen Haiyue Zhu Marcelo H Ang Department of Mechanical Engineering National University of Singapore Advanced Robotics Centre College of Design and Engineering National University of Singapore Agency for Science Technology and Research (A*STAR)

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Deep Reinforcement Learning (DRL), combined with demonstration data, has progressed in developing manipulation policies. However, the practical collection of ample high-quality demonstrations is timeconsuming, and demonstrations generated by humans may not perfectly align with the operational demands of robots. Our study suggests that RL agents in manipulation tasks are acutely sensitive to demonstration quality in both online and offline environments. Utilizing low-quality or limited demonstrations to enhance RL policy development remains a significant challenge. To address these concerns, we propose TD3+Smooth BC, an enhancement of TD3+BC [1] and TD3fG [2]. This algorithm ensures a smooth transition from learning through expert demonstrations to learning from experience, incorporating prior knowledge while mitigating negative demonstration impacts. In the offline setting, TD3+Smooth BC adjusts the balance between imitation and Q-value maximization dynamically, effectively utilizing human demonstrations for efficient policy learning. The algorithm demonstrates marked improvements in the Adroit manipulator and MuJoCo tasks, even with limited demonstrations of varying quality.

关键词： Mechatronics Heuristic algorithms Robot sensing systems Deep reinforcement learning Task analysis Manipulator dynamics

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design and Analysis of a Human-Centric Shoulder Exoskeleton to Improve the Range of Motion

Design and Analysis of a Human-Centric Shoulder Exoskeleton ...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Avinash S Pramod Santhakumar Mohan Asokan Thondiyath Department of Engineering Design Robotics Laboratory Indian Institute of Technology Madras Chennai Tamil Nadu India Department of Mechanical Engineering Indian Institute of Technology Palakkad Palakkad Kerala India

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

The upper limb exoskeletons available in the market are used to assist in carrying out activities of daily living (ADL) but have a limited range of motion (ROM). The limitation in ROM is due to the negligence of the complete shoulder complex joint motions while designing the wearable exoskeleton. This can affect the shoulder joint components due to interaction forces generated between the exoskeleton and the human shoulder in the presence of joint misalignment. This paper proposes a novel design of a human-centric shoulder exoskeleton mechanism. It is capable of exhibiting the same reachability of the human shoulder without a self-collision or collision with the human body throughout the ROM by introducing kinematic redundancy. The design procedure of the exoskeleton, which considers human joint motions, has been presented along with the static stress analysis. The actuator selection for the glenohumeral joint has also been specified along with the initial 3D printed prototype of the exoskeleton mechanism, which is further used to test and compare its maximum ROM.

关键词： Actuators Translation Exoskeletons Redundancy Shoulder Prototypes Kinematics Three-dimensional printing Motion control Read only memory

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Inline Photometrically Calibrated Hybrid Visual SLAM

Inline Photometrically Calibrated Hybrid Visual SLAM

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Nicolas Abboud Malak Sayour Imad H. Elhajj John Zelek Daniel Asmar Vision and Robotics Lab Maroun Semaan Faculty of Engineering and Architecture American University of Beirut Beirut Lebanon Department of System Design Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents an integrated approach to Visual SLAM, merging online sequential photometric calibration within a Hybrid direct-indirect visual SLAM (H-SLAM). Photometric calibration helps normalize pixel intensity values under different lighting conditions, and thereby improves the direct component of our H-SLAM. A tangential benefit also results to the indirect component of H-SLAM given that the detected features are more stable across variable lighting conditions. Our proposed photometrically calibrated H-SLAM is tested on several datasets, including the TUM monoVO as well as on a dataset we created. Calibrated H-SLAM outperforms other state of the art direct, indirect, and hybrid Visual SLAM systems in all the experiments. Furthermore, in online SLAM tested at our site, it also significantly outperformed the other SLAM Systems.

关键词： Visualization Simultaneous localization and mapping Measurement units Merging Lighting Inertial navigation Feature extraction Calibration Intelligent robots

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Trajectory Tracking Using Linear State Feedback Controller for a Mecanum Wheel Omnidirectional 6th

Trajectory Tracking Using Linear State Feedback Controller f...

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6th IFToMM Asian Mechanisms and Machine Science Conference, Asian MMS 2021

作者： Thai, Nguyen Hong Ly, Trinh Thi Khanh Long, Nguyen Thanh Dzung, Le Quoc Department of Mechanical Design and Robotics School of Mechanical Engineering HanoiUniversity of Science and Technology Hanoi Viet Nam Faculty of Automation Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9783030918910

This paper presents a method to design a linear state feedback controller for the mecanum wheel omnidirectional to track a NURBS curve trajectory. Firstly, the robot's kinematic model and kinematic error were established to design a linear state feedback controller. Then, determine the parameters of the controller according to the varying angular velocity for the desired trajectory. An omnidirectional robot platform has been experimental designed and manufactured for experimental verification of the proposed controller. The simulation and experimental results show that the kinematic controller devel-oped by this study has high-performance trajectory tracking with minor tracking errors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Controllers

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Forearm-Driven Soft Prosthetic Hand designed for Children with the Ability to Execute Intricate and Skillful Motions

Forearm-Driven Soft Prosthetic Hand Designed for Children wi...

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Mechatronics and robotics engineering (ICMRE), International Conference on

作者： Hironari Taniguchi Ren Yakami Kosuke Morinaga Shuichi Wakimoto Department of Robotics and Design Osaka Institute of Technology Osaka Japan Department of Rehabilitaion Hiroshima International University Higashi-Hiroshima Japan Department of Systems Engineering Okayama University Okayama Japan

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

The prosthetic hand we have developed is powered by air pressure, and its functionality is not impaired even when the finger joints are flexibly deformed. This makes it possible to grasp objects of various shapes, and it has the advantages of durability and high safety for the user and others. We have evaluated the performance of the developed prosthetic hand and conducting user tests using only the prosthetic hand. Of the 19 tasks performed, 14 were successfully completed. The user was able to achieve movements that were comparable to those of normal subjects in the task using grip strength grasping. The results of the user test confirmed several issues regarding the functionality of the prosthetic hand, but it was thought that it could sufficiently fulfill the purpose of assisting bimanual motion in an environment where children actually use the prosthetic hand.

关键词： Wiring Pediatrics Actuators Shape Grasping Soft robotics Valves Safety Solenoids Prosthetic hand

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Tele-rehabilitation of Upper Extremity with GARMI Robot: Concept and Preliminary Results

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IFAC-PapersOnLine 2023年第2期56卷 3546-3551页

作者： Xiao Chen Hamid Sadeghian Yanan Li Sami Haddadin Munich Institute of Robotics and Machine Intelligence Technical University of Munich 80992 Munich Germany Department of Engineering and Design University of Sussex Brighton United Kingdom

The discrepancy between the growing need for rehabilitation and the inadequate availability of facilities and skilled therapists is becoming increasingly conspicuous. To address this problem, a tele-rehabilitation system of the upper extremity with the service robot GARMI is proposed. The system utilizes optimal control and game theory, considering the patient, GARMI and possibly the therapist as the agents who share a common cost function. By adapting the roles of these agents, the proposed system can provide passive, active, and assist-as-needed rehabilitation.

关键词： Active Rehabilitation Optimal control Impedance Control

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A Machine Learning-Based Predictive Framework for Head Impact Injuries

SSRN

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SSRN 2024年

作者： Heng, Jing Yi Chai, Rifai Mouloodi, Saeed Tse, Kwong Ming Department of Mechanical and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Telecommunications Electrical Robotics and Biomedical Engineering Swinburne University of Technology Melbourne Australia Department of Mechanical Engineering University of Melbourne Melbourne Australia

Modern technologies have rapidly developed in recent years, particular in the field of artificial intelligence (AI). Nascent technique has been adapted by researchers in their respective research fields to leverage the research outcome. Several research fields have achieved significance breakthroughs by embracing AI to address challenges. Machine learning (ML), a prominent subset of AI, is a data-driven method that enables the discovery and establishment of intricate relationship within datasets. Training ML models require large amounts of high-quality data to effectively capture and comprehend the underlying data relationships. Once adequately trained, these AI models can be deployed in real-life applications without the need for extensive computational power or resources. This research paper introduces the utilization of artificial neural networks (ANNs) to predict intracranial pressures using kinematic linear accelerations as inputs only. The proposed model incorporates three kinematic inputs (ax, ay, az) and time as inputs to the ANN. The developed ANN model exhibits the ability to predict intracranial pressure values for five distinct brain regions, representing the front, rear, left, right and rear sections of the brain. Both feedforward and time series ANNs were trained using 80 samples with 177 time-steps data. The ANN model successfully predicted intracranial pressure from head kinematics, with a correlation coefficient (R = 0.98) and root mean square error (RMSE = 0.008MPa). These findings highlight the efficacy of ANNs in accurately predicting intracranial pressure based on linear kinematic measurements. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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