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    Studies from Karlsruhe Institute of Technology (KIT) Have Provided New Informati on about Support Vector Machines (Support Vector Machines Within a Bivariate Mix ed-integer Linear Programming Framework)

    10-10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 Support Vector Machines is now available. According to news originating from Karlsruhe, Germany, by New sRx correspondents, research stated, “Support vector machines (SVMs) are a power ful machine learning paradigm, performing supervised learning for classification and regression analysis. A number of SVM models in the literature have made use of advances in mixed-integer linear programming (MILP) techniques in order to p erform this task efficiently.” Our news journalists obtained a quote from the research from the Karlsruhe Insti tute of Technology (KIT), “In this work, we present three new models for SVMs th at make use of piecewise linear (PWL) functions. This allows effective separatio n of data points where a simple linear SVM model may not be sufficient. The mode ls we present make use of binary variables to assign data points to SVM segments , and hence fit within a recently presented framework for machine learning MILP models. Alongside presenting an inbuilt feature selection operator, we show that the models can benefit from robust inbuilt outlier detection.”

    Data on Machine Learning Reported by Researchers at University of Leon (Improve Quality of Service for the Internet of Things Using Blockchain & M achine Learning Algorithms)

    11-11页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Machine Learning have been prese nted. According to news reporting originating from Leon, Spain, by NewsRx corres pondents, research stated, “The quality of service (QoS) parameters in IoT appli cations plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially sh ortly.” Financial support for this research came from Recovery, Transformation, and Resi lience Plan - European Union (Next Generation).

    Research Results from Liverpool Hope University Update Understanding of Robotics (Design and Integration of a Robotic Welding Parameterized Procedure for Indust rial Applications)

    12-12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 Liverpool Hope University by NewsRx corres pondents, research stated, “This paper explores the development of an effective motion planning strategy for robotics welding in tube to tubesheet joints, a cri tical process in heat exchanger manufacturing. The research methodology follows an experimental paradigm, investigating two distinct approaches to tackle the in tricacies of this task.” The news correspondents obtained a quote from the research from Liverpool Hope U niversity: “The initial approach, involving a welding torch affixed to the robot ic arm’s flange, proved ineffective due to the complexity of continuous 360° orb ital welding. This led to the adoption of a custom end effector in the second ap proach, designed to enhance adaptability and precision. Key tools and materials employed in this research include the Robot Operating System (ROS), Rviz for 3D visualization, MoveIt for motion planning, SolidWorks for CAD modelling, and the xArm7 Robotic Arm. These tools facilitated the creation of a comprehensive plan ning environment. The motion planning process relies on three essential paramete rs: tube diameter, type of tube to tubesheet joint (flush or protruding), and th e 3D coordinates of tube centers. A Python scripts control the robot’s movements , with specific joint state and pose goals for precise positioning.”

    Investigators from Zhejiang University of Technology Zero in on Machine Learning (Continuous Flow Synthesis of n,o-dimethyl-n‘- nitroisourea Monitored By Inline Fourier Transform Infrared Spectroscopy: Bayesian Optimization ...)

    13-13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 Hangzhou, People’s Republic o f China, by NewsRx correspondents, research stated, “The synthesis of N,O-dimeth yl-N ‘-nitroisourea, crucial intermediates in pesticide manufacturing, was explo red through a substitution reaction between O-methyl-N-nitroisourea and methylam ine within a novel continuous flow microreactor system, featuring Fourier transf orm infrared (FTIR) in-line analysis for real-time monitoring. In this paper, th e reaction is investigated using two optimization methods: the contemporary mach ine learning-based Bayesian optimization and the traditional kinetic modeling.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Zhejiang Province, Zhejiang Pr ovince Science and Technology Plan Project, National Natural Science Foundation of China (NSFC).

    Study Data from University of South Carolina Update Knowledge of Robotics (Plann ing To Chronicle: Optimal Policies for Narrative Observation of Unpredictable Ev ents)

    14-15页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on Robotics is now available. Accordi ng to news reporting from Columbia, South Carolina, by NewsRx journalists, resea rch stated, “One important class of applications entails a robot scrutinizing, m onitoring, or recording the evolution of an uncertain time-extended process. Thi s sort of situation leads to an interesting family of active perception problems that can be cast as planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to.” Financial support for this research came from National Science Foundation (NSF).

    Research Data from Tianjin University Update Understanding of Robotics and Autom ation (You Only Plan Once: a Learning-based One-stage Planner With Guidance Lear ning)

    15-15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 - Robotics and Automation. According to news reporting from Tianjin, People’s Republic of China, by NewsRx journalists, research stated, “In this work, we pro pose a learning-based one-stage planner for trajectory generation of quadrotor i n obstaclecluttered environment without relying on explicit map. We integrate p erception and mapping, front-end path searching, and back-end optimization into a single network.” Financial support for this research came from National Key R&D Prog ram of China.

    State Key Laboratory Reports Findings in Androids (A HUG taxonomy of humans with potential in human-robot hugs)

    16-16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 - Androids is the subject of a report. According to news reporting from Shanghai, People’s Re public of China, by NewsRx journalists, research stated, “Humans can easily perf orm various types of hugs in human contact and affection experience. With the pr evalence of robots in social applications, they would be expected to possess the capability of hugs as humans do.” Financial supporters for this research include Science and Technology Commission of Shanghai Municipality, National Natural Science Foundation of China, Nationa l Key Research and Development Program of China.

    Data from Eastern Institute of Technology Advance Knowledge in Machine Learning (Forward Prediction and Surrogate Modeling for Subsurface Hydrology: a Review of Theory-guided Machine-learning Approaches)

    17-17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 Machine Learning is now available. According to news reporting originating from Zhejiang, People’s Repu blic of China, by NewsRx correspondents, research stated, “Forward prediction of subsurface hydrological processes, as well as tasks that require repetitive eva luation of forward models (e.g., uncertainty quantification, inverse modeling, o ptimization problems, etc.) can be computationally-intensive. Deep learning-base d approaches are increasingly applied to facilitate the forward modeling process (e.g., learning partial differential equations) and to build surrogate models a s efficient replacements of physics-based forward models.” Our news editors obtained a quote from the research from the Eastern Institute o f Technology, “However, for practical problems, the available training data are usually of limited quantity and quality, which significantly affects the models’ learning capability. Theory-guided machine-learning (TGML) emerged in the past decades to cope with such conditions. Through introducing theoretical guidance t o data preprocessing, neural network design, or model-training processes, the ac curacy, robustness, and generalizability of the trained model can be dramaticall y improved. Herein, a review is provided on the latest advances of TGML applied to subsurface hydrology problems. In particular, three ways of incorporating the oretical guidance into the model-training process are summarized, all of which a re based on the principle of adding additional physical regularizations to the l oss function. TGML-based surrogate models for forward prediction, uncertainty qu antification, inverse modeling, and optimization problems in the areas of single -/two-phase flow and contaminant transport are reviewed in detail.”

    Findings from Shanxi University Yields New Data on Artificial Intelligence (Eval uating Edge Artificial Intelligence-driven Supply Chain Management Platforms Usi ng Collaborative Large-scale Fuzzy Information Fusion)

    18-19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 reporting out of Shanxi, Peopl e’s Republic of China, by NewsRx editors, research stated, “The rapid developmen t of edge artificial intelligence (AI) has brought about revolutionary changes i n supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision -making process, injecting more i nnovative elements into SCM.” Funders for this research include National Natural Science Foundation of China ( NSFC), Special Fund for Science and Technology Innovation Teams of Shanxi, China Postdoctoral Science Foundation, Natural Science Foundation of Chongqing, Scien ce and Technology Research Program of Chongqing Education Commission, Training P rogram for Young Scientific Researchers of Higher Education Institutions in Shan xi, Graduate Education Innovation Programs of Shanxi Province, Cultivate Scienti fic Research Excellence Programs of Higher Education Institutions in Shanxi (CSR EP), The 1331 Engineering Project of Shanxi Province, China.

    New Findings from Northeastern University in the Area of Robotics Reported (Robu st Adaptive Safety Control of Uncertain Nonlinear Systems and Its Application To Differential Robots)

    18-18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 Shenyang, People’s R epublic of China, by NewsRx correspondents, research stated, “This article focus es on the problem of robust adaptive safety control of a class of nonlinear syst ems with parametric uncertainties. Two novel definitions, that is, robust adapti ve control Lyapunov function (RACLF) and robust adaptive control barrier functio n (RACBF) are introduced in this article.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).