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    Reports on Robotics Findings from Chinese Academy of Sciences Provide New Insigh ts (Intuitive Human-robot-environment Interaction With Emg Signals: a Review)

    102-103页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news originating from Shenyang, People’s Republic of China, by NewsRx correspondents, research stated, “A long history has passed since electromyography (EMG) signals have been explored in human-centered robots for intuitive interaction. However, it still has a gap between scientific resea rch and real-life applications.” Financial support for this research came from National Key Research and Developm ent Program of China. Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “Previous studies mainly focused on EMG decoding algorithms, leavi ng a dynamic relationship between the human, robot, and uncertain environment in real-life scenarios seldomly concerned. To fill this gap, this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interac tion (HREI) systems. The general processing framework is summarized, and three i nteraction paradigms, including direct control, sensory feedback, and partial au tonomous control, are introduced. EMG-based intention decoding is treated as a m odule of the proposed paradigms. Five key issues involving precision, stability, user attention, compliance, and environmental awareness in this field are discu ssed. Several important directions, including EMG decomposition, robust algorith ms, HREI dataset, proprioception feedback, reinforcement learning, and embodied intelligence, are proposed to pave the way for future research. To the best of w hat we know, this is the first time that a review of EMG-based methods in the HR EI system is summarized.”
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    Studies from University of Stuttgart Add New Findings in the Area of Robotics (M odeling and Experimental Validation of High-flow Fluid-driven Membrane Valves fo r Hyperactuated Soft Robots)

    103-103页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news reporting originating from Stuttgart, Germany, by NewsRx correspondents, research stated, “Herein, the design, modeling, and va lidation of high-flow, fluid-driven, membrane valves tailored specifically for a pplications in soft robotic systems are described. Targeting the piping problem in hyper-actuated soft robots, two fluid-driven membrane valve designs that can admit flows of up to 871 mg s-1$871 \text rm{ } \textrm { } \ text{mg} \textrm{ } \ left(\text{s}\ right) <. >{- 1} $ while weighing less than 20 g$false$ are introduced.” Funders for this research include Toyota Research Institute, Deutsche Forschungs gemeinschaft, Carl- Zeiss-Stiftung.
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    Findings from Southeast University Provides New Data on Robotics (Event-triggere d-based Sliding Mode Asymptotic Tracking Control of Robotic Manipulators)

    104-104页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news reporting out of Jiangsu, People’s Republic o f China, by NewsRx editors, research stated, “This brief studies the trajectory tracking problem for robotic manipulators with disturbances. A novel terminal sl iding mode surface is developed to accelerate the convergence process.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Southeast Universit y, “For reducing the burden of information transmission, the event-triggered str ategy is introduced into the sliding mode control (SMC). A novel event-triggered condition with a time-varying threshold is proposed to reduce the times of cont rol execution and the chattering. An event-triggered based practical sliding mod e controller is constructed to render the system states reach the practical slid ing mode surface in a finite time. With the help of Lyapunov stability theory, t he tracking error converges to zero asymptotically.”
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    Central South University Reports Findings in Machine Learning (Do green finance and green innovation affect corporate credit rating performance? Evidence from m achine learning approach)

    104-105页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Changsha, People’s Rep ublic of China, by NewsRx correspondents, research stated, “This study investiga tes the impact of green finance (GF) and green innovation (GI) on corporate cred it rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. Th e least absolute shrinkage and selection operators (LASSOs) machine learning alg orithms are first used to select the critical drivers of corporate credit perfor mance.” Our news journalists obtained a quote from the research from Central South Unive rsity, “Then, we applied partialing-out LASSO linear regression (POLR) and doubl e selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% i ncrease in GF diminishes CR by 0.26%, whereas GI promotes CR perfor mance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting fi rms, non-state-owned enterprises, and firms in the Western region.”
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    Study Findings on Robotics Published by a Researcher at Taif University (Kriging -based Model Predictive Control for Lower-limb Rehabilitation Robots)

    105-106页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on robotics is the subjec t of a new report. According to news reporting out of Taif University by NewsRx editors, research stated, “Model predictive control (MPC) has emerged as a predo minant method in the realm of control systems; yet, it faces distinct challenges .” The news journalists obtained a quote from the research from Taif University: “F irst, MPC often hinges on the availability of a precise and accurate system mode l, where even minor deviations can drastically affect the control performance. S econd, it entails a high computational load due to the need to solve complex opt imization problems in real time. This study introduces an innovative method that harnesses the probabilistic nature of Gaussian processes (GPs), offering a solu tion that is robust, adaptive, and computationally efficient for optimal control . Our methodology commences with the collection of data to learn optimal control policies. We then proceed with offline training of GPs on these data, which ena bles these processes to accurately grasp system dynamics, establish input-output relationships, and, crucially, identify uncertainties, thereby informing the MP C framework. Utilizing the mean and uncertainty estimates derived from GPs, we h ave crafted a controller that is capable of adapting to system deviations and ma intaining consistent performance, even in the face of unforeseen disturbances or model inaccuracies.”
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    Beijing Normal University-Hong Kong Baptist University United International Coll ege Researcher Details Research in Machine Learning (Comparison of Machine Learn ing Models for Stock Prediction)

    106-107页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from Zhuhai, People’s Republic of China, by NewsRx correspondents, research stated, “The sto ck market, a significant component of the financial market, is essential to the functioning of the world economy.” The news journalists obtained a quote from the research from Beijing Normal Univ ersity-Hong Kong Baptist University United International College: “Accurate pred iction of changes in stock prices is of great importance to investors, financial institutions, and the economic system. In order to compare the effects of three methods-linear regression, K-nearest neighbor (KNN), and long short-term memory network (LSTM)- in the context of Tesla stock prediction, the goal of this study is to investigate the use of machine learning in the field of stock prediction. Through empirical analysis and comprehensive evaluation, this paper finds that the LSTM model performs best in Tesla stock prediction, with better prediction a ccuracy and stability. LSTM can better capture the time series characteristics a nd complex nonlinear relationships of stock prices, thus improving the accuracy of prediction. This research investigates the future development direction of ma chine learning techniques in stock forecasting, building upon the discovered ins ights. Subsequent investigations may concentrate on broadening the scope of data attributes, investigating group education techniques, and including attention m echanisms.”
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    New Robotics Research Reported from BioRobotics Institute (Porcospino Flex: A Bi o-Inspired Single-Track Robot with a 3D-Printed, Flexible, Compliant Vertebral C olumn)

    107-107页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in robotic s. According to news originating from Pisa, Italy, by NewsRx correspondents, res earch stated, “This paper is focused on the design and development of the Porcos pino Flex, a single-track robot inspired by nature and featuring a meta-material structure.” Our news journalists obtained a quote from the research from BioRobotics Institu te: “In the earlier version of the Porcospino, the main body was composed of a c hain of vertebrae and two end sections linked by flexible joints, but the excess ive use of materials in 3D printing and the resulting weight of the robot posed challenges, ultimately leading to a decrease in its overall efficiency and perfo rmance. The Porcospino Flex is manufactured through the fused deposition modelin g process using acrylonitrile butadiene styrene and thermoplastic polyurethane, featuring a singular meta-material structure vertebral column. The adoption of a lattice structure in the main body of the Porcospino Flex leads to a substantia l increase in performance, reducing its weight from 4200 g to 3600 g. Furthermor e, the decrease in weight leads to a reduction in material usage and waste, maki ng a substantial contribution to the sustainability of the robot.”
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    Data on Artificial Intelligence Described by a Researcher at University of Marib or (Private Firm Valuation Using Multiples: Can Artificial Intelligence Algorith ms Learn Better Peer Groups?)

    108-109页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on artificial intelligence have bee n presented. According to news reporting from Maribor, Slovenia, by NewsRx journ alists, research stated, “Forming optimal peer groups is a crucial step in multi plier valuation.” Our news correspondents obtained a quote from the research from University of Ma ribor: “Among others, the traditional regression methodology requires the defini tion of the optimal set of peer selection criteria and the optimal size of the p eer group a priori. Since there exists no universally applicable set of closed a nd complementary rules on selection criteria due to the complexity and the diver se nature of firms, this research exclusively examines unlisted companies, rende ring direct comparisons with existing studies impractical. To address this, we d eveloped a bespoke benchmark model through rigorous regression analysis. Our aim was to juxtapose its outcomes with our unique approach, enriching the understan ding of unlisted company transaction dynamics. To stretch the performance of the linear regression method to the maximum, various datasets on selection criteria (full as well as F- and NCA-optimized) were employed. Using a sample of over 20 ,000 private firm transactions, model performance was evaluated employing multip lier prediction error measures (emphasizing bias and accuracy) as well as predic tion superiority directly.”
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    Findings from Norwegian University of Science and Technology (NTNU) Yields New F indings on Robotics (Automatic Alignment of Underwater Snake Robots Operating In Wakes of Bluff Bodies)

    108-108页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics. According to news originating from Trondheim, Norway, by NewsRx corres pondents, research stated, “This paper presents the development of controllers t o automatically align an underwater snake robot (USR) with a wake that forms beh ind a bluff body, while swimming at a desired distance to the object. The low-le vel controllers stabilize the joint motion of the USR to a swimming gait while a chieving a desired orientation and tangential velocity.” Financial supporters for this research include European Research Council (ERC), Research Council of Norway through the Centres of Excellence funding scheme.
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    Studies from Netaji Subhash Engineering College Have Provided New Data on Machin e Learning (Application of Machine Learning- Assisted Global Optimization for Imp rovement in Design and Performance of Open Resonant Cavity Antenna)

    109-110页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Kolkata, India, by NewsRx c orrespondents, research stated, “Open resonant cavity antenna (ORCA) and its rec ent advances promise attractive features and possible applications, although the designs reported so far are solely based on the classical electromagnetic (EM) theory and general perception of EM circuits.” Funders for this research include Scheme of Abdul Kalam Technology Innovation Na tional Fellow of Inae/dst-serb, Government of India; Dst-serb Project, Governmen t of India.
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