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    Harbin Institute of Technology Researcher Publishes Findings in Robotics (Deep M ARL-Based Resilient Motion Planning for Decentralized Space Manipulator)

    67-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; Research findings on robotics are disc ussed in a new report. According to news reportingoriginating from Harbin, Peop le’s Republic of China, by NewsRx correspondents, research stated, “Spacemanipu lators play an important role in the on-orbit services and planetary surface ope ration.”Our news reporters obtained a quote from the research from Harbin Institute of T echnology: “In theextreme environment of space, space manipulators are suscepti ble to a variety of unknown disturbances.How to have a resilient guarantee in failure or disturbance is the core capability of its future development.Compared with traditional motion planning, learning-based motion planning has gradually become a hotspot in current research. However, no matter what kind of research ideas, the single robotic manipulatoris studied as an independent agent, making it unable to provide sufficient flexibility under conditions suchas external f orce disturbance, observation noise, and mechanical failure. Therefore, this pap er putsforward the idea of “discretization of the traditional single manipulator”. Different discretization formsare given through the analysis of the multi-d egree-of-freedom single-manipulator joint relationship, and asingle-manipulator representation composed of multiple new subagents is obtained.”
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    OFFIS - Institute for Information Technology Reports Findings in Machine Learnin g (Pre-Processing of Categorical Features Within Medical Analysis Systems)

    68-68页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; New research on Machine Learning is th e subject of a report. According to newsoriginating from Oldenburg, Germany, by NewsRx correspondents, research stated, “The complexity ofthe cancer problem d omain presents challenges not only to the medical analysis systems tasked with i tsanalysis, but also to the users of such systems. While it is desirable to ass ist users in operating thesemedical analysis systems, prior groundwork is requi red before this can be achieved, such as recognisingpatterns in the way users c reate certain analyses within these systems.”
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    Investigators from Technical University Munich (TU Munich) Release New Data on R obotics and Automation (Improving Selfsupervised Learning of Transparent Catego ry Poses With Language Guidance and Implicit Physical Constraints)

    69-69页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; Current study results on Robotics - Ro botics and Automation have been published.According to news reporting out of Mu nich, Germany, by NewsRx editors, research stated, “Accurate objectpose estimat ion is crucial for robotic applications and recent trends in category-level pose estimation showgreat potential for applications encountering a large variety o f similar objects, often encountered in homeenvironments. While common in such environments, photometrically challenging objects with transparencysuch as glas ses are poorly handled by current methods.”
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    University of Osnabruck Reports Findings in Machine Learning (AI can see you: Ma chiavellianism and extraversion are reflected in eye-movements)

    70-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews ; New research on Machine Learning is the subject o f a report. According to news originating fromOsnabruck, Germany, by NewsRx cor respondents, research stated, “Recent studies showed an associationbetween pers onality traits and individual patterns of visual behaviour in laboratory and oth er settings.The current study extends previous research by measuring multiple p ersonality traits in natural settings;and by comparing accuracy of prediction o f multiple machine learning algorithms.”
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    Study Data from Harbin Engineering University Update Understanding of Robotics a nd Automation (Enhancing Joint Dynamics Modeling for Underwater Robotics Through Stochastic Extension)

    71-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; A new study on Robotics - Robotics and Automation is now available. According tonews reporting from Harbin, People’s Republic of China, by NewsRx journalists, research stated, “Accuratejoint dynam ics models are essential for the compliance and robustness of robot control, esp ecially forrobots operating in complex underwater environments. To improve the precision of joint dynamics models,much research focuses on refining specific p arameters or incorporating previously overlooked parametersthrough theoretical deductions and simulations.”
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    Data from Future University Hakodate Broaden Understanding of Robotics and Mecha tronics (Buoyancy and Propulsion Mechanisms for Stable Movement in Snow Field)

    72-72页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; A new study on robotics and mechatroni cs is now available. According to newsoriginating from Hokkaido, Japan, by News Rx correspondents, research stated, “For small-sized mobilemachines, moving on snow without sinking is challenging. Snowmobiles are often used to move on snow.”Financial supporters for this research include Japan Society For The Promotion o f Science.
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    New Findings Reported from University Indonesia Describe Advances in Machine Lea rning (Machine learning-based forward and inverse designs for prediction and opt imization of fracture toughness of aluminum alloy)

    73-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; New study results on artificial intell igence have been published. According to newsreporting from the University Indo nesia by NewsRx journalists, research stated, “Utilization of machinelearning f ramework to design aluminum alloy with high fracture toughness is increasing.”The news editors obtained a quote from the research from University Indonesia: “ Nonetheless, beforesuch model can be applied, the generalizability of the model becomes imperative thus it can give a betterperformance not only in the presen t, but also against the future data. In this work, we deployed shallowand deep machine learning techniques represented by support vector regression (SVR), k-ne arest neighbors(KNN), extreme gradient boosting (XGBoost) and artificial neural network (ANN) was deployed to predictthe fracture toughness of various aluminu m alloys in the scheme of MLDS framework. Our study revealsthat the highest pre diction accuracy can be obtained for XGBoost technique with R2 score, RMSE, andMAPE values were found to be 90.6 %, 2.57, and 7.0 %, respectively with robust k-fold validation valueof 90.1 ± 1.5. The superior per formance of Xgboost due to its capability handling non-linear regressionproblem s with a small amount of data. The forward properties to compositions (P2C) and reversecompositions to properties (C2P) models exhibited good accuracies in pre dicting the fracture toughnessas illustrated by the error values lower than the machine learning design system (MLDS) error criteriaof 5 %. The X GBoost model feasibility to predict fracture toughness for various aluminum allo ys ofAlcoa 7055-T7751, AA 7055-T7751, 2024-T852, 5083-O 8090-T8151 was demonstr ated as well as usedto search aluminum alloy compositions with high fracture to ughness.”
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    Study Data from University of Valencia Provide New Insights into Machine Learnin g (Harnessing Machine Learning to Decode the Mediterranean’s Climate Canvas and Forecast Sea Level Changes)

    74-74页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; Investigators discuss new findings in artificial intelligence. According to news reportingoriginating from Valencia, Spain, by NewsRx correspondents, research stated, “Climate change and risingsea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Currentmethods face limitations due to their inability to ful ly capture nonlinear complexities, high computationalcosts, gaps in historical data, and bridging the gap between short-term and long-term forecasting intervals.”
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    Recent Findings in Machine Learning Described by Researchers from Bangladesh University of Professionals (A comprehensive approach to detecting chemical adulter ation in fruits using computer vision, deep learning, and chemical sensors)

    75-75页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; Investigators discuss new findings in artificial intelligence. According to news originatingfrom Dhaka, Bangladesh, by NewsRx correspondents, research stated, “Contamination of harmful additivesin fruits has become a concerning norm these days. Owing to the great popularity o f fruits, dishonestvendors frequently use harmful chemicals to contaminate frui ts to extend their shelf life, which is extremelydangerous for the general publ ic’s health.”
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    King Abdulaziz University Researcher Highlights Research in Machine Learning (Ac curate Forecasting of Global Horizontal Irradiance in Saudi Arabia: A Comparativ e Study of Machine Learning Predictive Models and Feature Selection Techniques)

    76-76页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; Current study results on artificial in telligence have been published. According to newsoriginating from Jeddah, Saudi Arabia, by NewsRx correspondents, research stated, “The growing interestin sol ar energy stems from its potential to reduce greenhouse gas emissions.”Funders for this research include Institutional Fund Projects.The news editors obtained a quote from the research from King Abdulaziz Universi ty: “Global horizontalirradiance (GHI) is a crucial determinant of the producti vity of solar photovoltaic (PV) systems.Consequently, accurate GHI forecasting is essential for efficient planning, integration, and optimizationof solar PV e nergy systems. This study evaluates the performance of six machine learning (ML) regressionmodels-artificial neural network (ANN), decision tree (DT), elastic net (EN), linear regression (LR),Random Forest (RF), and support vector regress ion (SVR)-in predicting GHI for a site in northern SaudiArabia known for its hi gh solar energy potential. Using historical data from the NASA POWER database,c overing the period from 1984 to 2022, we employed advanced feature selection tec hniques to enhance thepredictive models. The models were evaluated based on met rics such as R-squared (R2), Mean SquaredError (MSE), Root Mean Squared Error ( RMSE), Mean Absolute Percentage Error (MAPE), and MeanAbsolute Error (MAE). The DT model demonstrated the highest performance, achieving an R2 of 1.0,MSE of 0 .0, RMSE of 0.0, MAPE of 0.0%, and MAE of 0.0.”
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