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    Study Findings from Barcelona Supercomputing Center Advance Knowledge in Machine Learning (A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California)

    74-75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 reporting out of the Barcelona Supercomputin g Center by NewsRx editors, research stated, “After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region.” Our news editors obtained a quote from the research from Barcelona Supercomputin g Center: “To satisfy real-time constraints, intensity measures are traditionall y evaluated with empirical Ground Motion Models that can drastically limit the a ccuracy of the estimated values. As an alternative, here we present Machine Lear ning strategies trained on physics-based simulations that require similar evalua tion times. We trained and validated the proposed Machine Learning-based Estimat or for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided th at the events considered are compatible with the training data.” According to the news editors, the research concluded: “Using the proposed strat egy we show significant error reductions not only for synthetic, but also for fi ve real historical earthquakes, relative to empirical Ground Motion Models.”
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    Shanghai Municipal Center for Disease Control and Prevention Reports Findings in Artificial Intelligence (Telephone follow-up based on artificial intelligence t echnology among hypertension patients: Reliability study)

    75-76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 Artificial Intelligenc e is the subject of a report. According to news reporting out of Shanghai, Peopl e’s Republic of China, by NewsRx editors, research stated, “Artificial intellige nce (AI) telephone is reliable for the follow-up and management of hypertensives . It takes less time and is equivalent to manual follow-up to a high degree.” Our news journalists obtained a quote from the research from Shanghai Municipal Center for Disease Control and Prevention, “We conducted a reliability study to evaluate the efficiency of AI telephone followup in the management of hypertens ion. During May 18 and June 30, 2020, 350 hypertensives managed by the Pengpu Co mmunity Health Service Center in Shanghai were recruited for follow-up, once by AI and once by a human. The second follow-up was conducted within 3-7 days (mean 5.5 days). The mean length time of two calls were compared by paired t-test, an d Cohen’s Kappa coefficient was used to evaluate the reliability of the results between the two follow-up visits. The mean length time of AI calls was shorter ( 4.15 min) than that of manual calls (5.24 min, P<.001). Th e answers related to the symptoms showed moderate to substantial consistency (k: .465-.624, P<.001), and those related to the complications showed fair consistency (k:.349, P<.001). In terms of lif estyle, the answer related to smoking showed a very high consistency (k:.915, P<.001), while those addressing salt consumption, alcohol consumption, and exerci se showed moderate to substantial consistency (k:.402-.645, P<.001).”
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    Researchers at Stanford University Report New Data on Artificial Intelligence (F ull-colour 3d Holographic Augmented-reality Displays With Metasurface Waveguides )

    76-77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 Artificial In telligence have been published. According to news originating from Stanford, Cal ifornia, by NewsRx correspondents, research stated, “Emerging spatial computing systems seamlessly superimpose digital information on the physical environment o bserved by a user, enabling transformative experiences across various domains, s uch as entertainment, education, communication and training 1-3. However, the wi despread adoption of augmented-reality (AR) displays has been limited due to the bulky projection optics of their light engines and their inability to accuratel y portray three-dimensional (3D) depth cues for virtual content, among other fac tors 4,5.” Funders for this research include Stanford Graduate Fellowship in Science and En gineering, National Research Council for Economics, Humanities & S ocial Sciences, Republic of Korea, Kwanjeong Scholarship, Meta Research PhD Fell owship, National Science Foundation (NSF), ARO, Samsung, Sony Research Award Pro gram, Stanford Nanofabrication Facility (SNF), National Science Foundation (NSF) , National Nanotechnology Coordinated Infrastructure.
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    Findings from Vrije Universiteit Brussel (VUB) Yields New Data on Robotics (Real -time Constraint-based Planning and Control of Robotic Manipulators for Safe Hum an-robot Collaboration)

    77-78页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now availab le. According to news reporting out of Brussels, Belgium, by NewsRx editors, res earch stated, “A recent trend in industrial robotics is to have robotic manipula tors working side-by-side with human operators. A challenging aspect of this coe xistence is that the robot is required to reliably solve complex path-planning p roblems in a dynamically changing environment.” Our news journalists obtained a quote from the research from Vrije Universiteit Brussel (VUB), “To ensure the safety of the human operator while simultaneously achieving efficient task realization, this paper introduces a computationally ef ficient planning and control architecture that combines a Rapidly-exploring Rand om Tree (RRT) path planner with a trajectory-based Explicit Reference Governor ( ERG) by means of a reference selector. The resulting scheme can steer the robot arm to the desired end-effector pose in the presence of actuator saturation, lim ited joint ranges, speed limits, a cluttered static obstacle environment, and mo ving human collaborators. The effectiveness of the proposed framework is experim entally validated on the Franka Emika Panda robot arm and fed with feedback info rmation from state-of-the-art depth perception systems. Our method outperforms b oth the standalone RRT and ERG algorithms in cluttered static environments where it overcomes: i) the RRT’s inability to handle dynamic constraints which result in constraint violations and ii) the ERG’s undesirable property of getting trap ped in local minima.”
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    Findings from Georgia Technical Research Institute Provides New Data about Machi ne Learning (Identifying Cislunar Orbital Families Via Machine Learning On Light Curves)

    78-79页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating in Atlanta, Georgia, by NewsRx journalists, research stated, “Current methods of performing Initial Orbi t Determination (IOD) in near-earth orbital regions cannot be directly extended to cislunar space due to changes in gravitational models that must be utilized. For the case of cislunar orbits, the Moon’s gravitational influence necessitates that orbital motions be described by three-body dynamics.” Funders for this research include Research Institute, Georgia Institute of Techn ology, GTRI’s Independent Research and Development (IRAD) funds.
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    Studies in the Area of Robotics Reported from University of Shanghai for Science and Technology (Research on multi-dimensional intelligent quantitative assessme nt of upper limb function based on kinematic parameters)

    79-80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 Shanghai, People’s Republic of Chin a, by NewsRx correspondents, research stated, “Rehabilitation assessment is a cr itical component of rehabilitation treatment. This study focuses on a comprehens ive analysis of patients’ movement performance using the upper limb rehabilitati on robot.” Our news reporters obtained a quote from the research from University of Shangha i for Science and Technology: “It quantitatively assessed patients’ motor contro l ability and constructed an intelligent grading model of functional impairments . These findings contribute to a deeper understanding of patients’ motor ability and provide valuable insights for personalized rehabilitation interventions. Pa tients at different Brunnstrom stages underwent rehabilitation training using th e upper limb rehabilitation robot, and data on the distal movement positions of the patients’ upper limbs were collected. A total of 22 assessment metrics relat ed to movement efficiency, smoothness, and accuracy were extracted. The performa nce of these assessment metrics was measured using the Mann-Whitney U test and P earson correlation analysis. Due to the issue of imbalanced sample categories, d ata augmentation was performed using the Synthetic Minority Over-sampling Techni que (SMOTE) algorithm based on weighted sampling, and an intelligent grading mod el of functional impairment based on the Extreme Gradient Boosting Tree (XGBoost ) algorithm was constructed. Sixteen assessment metrics were screened. These met rics were effectively normalized to their maximum values, enabling the derivatio n of quantitative assessment scores for motor control ability across the three d imensions through a weighted fusion approach. Notably, when applied to the dataenhanced dataset, the intelligent grading model exhibited remarkable improvement , achieving an accuracy rate exceeding 0.98. Moreover, significant enhancements were observed in terms of precision, recall, and f1-score.”
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    Study Results from Jiangxi University of Finance and Economics Update Understand ing of Computational Intelligence (Population Aging, Housing Price, and Househol d Consumption)

    80-81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 computational intelligence have been published. According to news originating from Jiangxi, P eople’s Republic of China, by NewsRx correspondents, research stated, “Populatio n aging and housing price can affect household consumption, and population aging can indirectly affect household consumption through housing price.” Financial supporters for this research include National Social Science Fund of C hina; Jiangxi Provincial Department of Education Science And Technology.
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    Researchers from Czech Technical University Describe Findings in Robotics and Au tomation (Fast Swarming of Uavs In Gnss-denied Feature-poor Environments Without Explicit Communication)

    81-82页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Robotics - Robotics a nd Automation have been presented. According to news originating from Prague, Cz ech Republic, by NewsRx correspondents, research stated, “A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in f eature-poor environments without any external localization and communication is introduced in this letter. A novel model of a UAV neighborhood is proposed to ac hieve robust onboard mutual perception and flocking state feedback control, whic h is designed to decrease the inter-agent oscillations common in standard reacti ve swarm models employed in fast collective motion.” Financial support for this research came from CTU. Our news journalists obtained a quote from the research from Czech Technical Uni versity, “The novel swarming methodology is supplemented with an enhanced Multi- Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MR SE and the neighborhood model may rely on information exchange between agents, w e introduce a communication-less version of the swarming framework based on esti mating communicated states to decrease dependence on the often unreliable commun ication networks of large swarms.”
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    Investigators from University of Minnesota Zero in on Machine Learning (Computat ionally Efficient Solution of Mixed Integer Model Predictive Control Problems Vi a Machine Learning Aided Benders Decomposition)

    82-83页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from Minneapolis, Minnesota, by Ne wsRx correspondents, research stated, “Mixed integer Model Predictive Control (M PC) problems arise in the operation of systems where discrete and continuous dec isions must be taken simultaneously to compensate for disturbances. The efficien t solution of mixed integer MPC problems requires the computationally efficient online solution of mixed integer optimization problems, which are generally diff icult to solve.” Financial support for this research came from National Science Foundation (NSF).
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    Study Results from Dhirubhai Ambani Institute of Information and Communication T echnology Provide New Insights into Robotics (Collaborative Dispersion By Silent Robots)

    83-84页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Robotics are disc ussed in a new report. According to news reporting out of Gujarat, India, by New sRx editors, research stated, “In the dispersion problem, a set of k co -located mobile robots must relocate themselves in distinct nodes of an unknown network. The network is modeled as an anonymous graph G = (V, E), where the graph’s node s are not labeled.” Funders for this research include Science and Engineering Research Board (SERB) , Department of Science and Technology, Govt. of India, Science Engineering Rese arch Board (SERB), India, Research Initiation Grant - IIT Bhilai, India.
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