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    Data on Robotics Reported by Researchers at University of Washington (Persistent Homology Meets Object Unity: Object Recognition In Clutter)

    1-2页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news originating from Seattle, Washington, by NewsRx correspondents, research stated, “Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. To address this challenge, we propose a new descriptor, Topological features Of Point cloud Slices (TOPS), for point clouds generated from depth images and an accompanying recognition framework, TOPS for Humaninspired Object Recognition (THOR), inspired by human reasoning.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Machine learning models for predicting disability and pain following lumbar disc herniation surgery

    Bjornar Berg
    1-1页
    查看更多>>摘要：About The Study: The findings of this study including 22,000 surgical cases suggest that machine learning models can inform about individual prognosis and aid in surgical decision-making to ultimately reduce ineffective and costly spine care.
      原文链接:
    • NETL
    • NSTL
    • Amer Medical Assoc

    Investigators from Zhejiang University Target Robotics (Singularity Analysis and Solutions for the Origami Transmission Mechanism of Fast-moving Untethered Insect-scale Robot)

    2-3页
    查看更多>>摘要：Fresh data on Robotics are presented in a new report. According to news reporting originating in Hangzhou, People’s Republic of China, by NewsRx journalists, research stated, “Designing insect-scale robots with high mobility is becoming an essential challenge in the field of robotics research. Among the methods for fabricating the transmission mechanism of the insect-scale robot, the smart composite microstructure (SCM) method is getting more and more attention.” Financial support for this research came from National Natural Science Foundation of China (NSFC).
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Machine Learning Study Results from Silesian University of Technology Described (Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases)

    3-4页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from Gliwice, Poland, by NewsRx editors, the research stated, “In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives.” The news correspondents obtained a quote from the research from Silesian University of Technology: “Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies from Institute Mines-Telecom (IMT) Have Provided New Information about Robotics (Implicit Time-integration Simulation of Robots With Rigid Bodies and Cosserat Rods Based On a Newtoneuler Recursive Algorithm)

    4-4页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting originating in Nantes, France, by NewsRx journalists, research stated, “In this article, we propose a new algorithm for solving the forward dynamics of multibody systems consisting of rigid bodies connected in arbitrary topologies by localized joints and/or soft links, possibly actuated or not. The simulation is based on the implicit time integration of the Lagrangian model of these systems, where the soft links are modeled by Cosserat rods parameterized by assumed strain modes.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Researchers from King’s College London Report New Studies and Findings in the Area of Machine Learning (Ascertaining Price Formation In Cryptocurrency Markets With Machine Learning)

    5-5页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating from London, United Kingdom, by NewsRx correspondents, research stated, “The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity.” Our news editors obtained a quote from the research from King’s College London, “This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction.”
      原文链接:
    • NETL
    • NSTL
    • Taylor & Francis

    Research Conducted at Zhejiang Agriculture & Forestry University Has Updated Our Knowledge about Machine Learning (A Singlewavelength Laser Relaxation Spectroscopy-based Machine Learning Solution for Apple Mechanical Damage Detection)

    6-7页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting out of Hangzhou, People’s Republic of China, by NewsRx editors, research stated, “Detecting and mitigating mechanical damage in apples during picking and transportation is a critical concern in the agricultural industry. This paper investigates the optimization of a pattern recognition model using single-wavelength laser relaxation spectroscopy for the purpose of apple mechanical damage detection.” Financial supporters for this research include Scientific Research Project of Zhejiang Province, National College Student Research Programme, College Student Research Programme of Zhejiang Province, Zhejiang AF University.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Investigators from University of Quebec Trois-Rivieres Zero in on Machine Learning (The Use of Machine Learning In Processstructure- property Modeling for Material Extrusion Additive Manufacturing: a State-of-the-art Review)

    7-8页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting from Trois-Rivieres, Canada, by NewsRx journalists, research stated, “Since its first appearance in the 1980s, additive manufacturing has become increasingly popular. Complex parts can be produced with high quality, minimal waste, and a variety of materials.” Financial supporters for this research include CGIAR, Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Research Chairs.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Study Results from University of New South Wales in the Area of Machine Learning Published (Comparative analysis of voice denoising using machine learning and traditional denoising)

    8-8页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from the University of New South Wales by NewsRx correspondents, research stated, “Noise often affects the content of an audio signal, and noise reduction techniques can help retrieve the original speech content.” Our news editors obtained a quote from the research from University of New South Wales: “In recent years, AI-based noise reduction has witnessed rapid development. This article provides a brief introduction to the background and principles of several AI-based noise reduction methods. One of the mentioned methods is an end-to-end time-domain deep learning speech division algorithm, which utilizes a multi-layer CNN network framework. Due to the need for deep network architectures to extract features, it involves a higher computational load. Traditional noise reduction algorithms, on the other hand, are based on researchers’ understanding of noise patterns and modeling. Traditional methods may not perform well on non-stationary noise, but they are relatively simple in terms of algorithmic implementation.”
      原文链接:
    • NETL
    • NSTL

    New Findings from Sandia National Laboratories in the Area of Machine Learning Described (Accelerating Fem-based Corrosion Predictions Using Machine Learning)

    9-9页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting from Albuquerque, New Mexico, by NewsRx journalists, research stated, “Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service.” Funders for this research include United States Department of Energy (DOE), United States Department of Energy (DOE).
      原文链接:
    • NETL
    • NSTL
    • Electrochemical Soc Inc