首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Jena University Hospital Reports Findings in Schizophrenia (BrainAGE: Revisited and reframed machine learning workflow)

    10-11页
    查看更多>>摘要：New research on Mental Health Diseases and Conditions - Schizophrenia is the subject of a report. According to news reporting out of Jena, Germany, by NewsRx editors, research stated, "Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health." Financial supporters for this research include Bundesministerium fur Bildung, Wissenschaft und Forschung, Carl-Zeiss-Stiftung, H2020 Marie Sklodowska-Curie Actions. Our news journalists obtained a quote from the research from Jena University Hospital, "We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing diseasespecific patterns for different levels of impairment."
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Study Data from Thiagarajar College of Engineering Update Knowledge of Robotics (Self-adaptive Learning Particle Swarm Optimization-based Path Planning of Mobile Robot Using 2d Lidar Environment)

    11-12页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting from Tamil Nadu, India, by NewsRx journalists, research stated, "The loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system." Financial support for this research came from Centre for Robotics, Department of Mechatronics Engineering, Thiagarajar College of Engineering and Vellore Institute of Technology. The news correspondents obtained a quote from the research from the Thiagarajar College of Engineering, "In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment."
      原文链接:
    • NETL
    • NSTL
    • Cambridge Univ Press

    Reports from Zhejiang University Add New Data to Research in Robotics (Graded in-plane Miura origami as crawling robots and grippers)

    12-13页
    查看更多>>摘要：Researchers detail new data in robotics. According to news reporting originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "In this work, we propose a variation of Miura origami which, different from the existing out-of-plane bending Miura origami, has an in-plane bent configuration due to its graded crease pattern." Funders for this research include National Natural Science Foundation of China; Joint Fund of Science And Technology Department of Liaoning Province And State Key Laboratory of Robotics China; Japan Science And Technology Agency; The Open Foundation of Hubei Key Laboratory of Theory And Application of Advanced Materials Mechanics. Our news reporters obtained a quote from the research from Zhejiang University: "By combining with the one-way shape memory alloy spring, we show that the proposed graded Miura origami can serve as a smart actuator and can be applied to drive crawling robots or grippers. First, we constructed a physical model of the graded Miura origami, from which a curvature-programmable geometric equation is proposed. Then, in addition to providing a mechanical model that can capture the mechanical behavior of the initial force-displacement relationship of the curved beam, we show that the proposed curved origami has a different mechanical behavior compared to the corresponding simple flexible arch, specifically if realized by silicon rubbers. By arranging anisotropic friction to the feet, the origami robot can crawl with an omega-elongation/compression motion like an inchworm."
      原文链接:
    • NETL
    • NSTL

    Study Data from University of Colorado Boulder Update Knowledge of Machine Learning (Predicting Serious Injury and Fatality Exposure Using Machine Learning In Construction Projects)

    13-14页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating from Boulder, Colorado, by NewsRx correspondents, research stated, "Safety academics and practitioners in construction typically use safety prediction models that employ information associated with past incidents to predict the likelihood of future injury or fatality on site. However, most prevailing models utilize only information related to failure (i.e., incident), so they cannot distinguish effectively between success and failure without well-informed comparison." Financial support for this research came from Construction Safety Research Alliance. Our news editors obtained a quote from the research from the University of Colorado Boulder, "Furthermore, recordable incidents on construction sites are extremely rare, which results in data that are too sparse to make predictions with high statistical power. This paper empirically reviews different approaches to safety to increase the understanding of conditions associated with safety success and failure. Empirical data about business-, project-, and crew-related factors were collected to predict serious injury and fatality (SIF) exposure conditions. A variety of modeling techniques were tested in a machine learning pipeline to identify the most accurate and stable predictive models. Results showed that the multilayer perceptron (MLP) approach best distinguished SIF exposure conditions from safety success conditions using nonlinear decision boundaries. The most influential factors in the models included the crew experience working together, supervisor experience with the crew, total number of workers under the supervisor's purview, and the maturity of leadership development programs for frontline supervisors. This study showed that data sets with both success and failure information yield more reliable and meaningful predictions than data sets with failure alone."
      原文链接:
    • NETL
    • NSTL
    • Asce-Amer Soc Civil Engineers

    New Support Vector Machines Data Have Been Reported by Investigators at Department of Computer Sciences (Handling Multiclass Problem By Intuitionistic Fuzzy Twin Support Vector Machines Based On Relative Density Information)

    14-15页
    查看更多>>摘要：Researchers detail new data in Support Vector Machines. According to news reporting out of Toronto, Canada, by NewsRx editors, research stated, "The intuitionistic fuzzy twin support vector machine (IFTSVM) merges the idea of the intuitionistic fuzzy set (IFS) with the twin support vector machine (TSVM), which can reduce the negative impact of noise and outliers. However, this technique is not suitable for multi-class and high-dimensional feature space problems." Our news journalists obtained a quote from the research from the Department of Computer Sciences, "Furthermore, the computational complexity of IFTSVM is high because it uses the membership and nonmembership functions to build a score function. We propose a new version of IFTSVM by using relative density information. This idea approximates the probability density distribution in multi-dimensional continuous space by computing the K-nearest-neighbor distance of each training sample. Then, we evaluate all the training points by a one-versus-one-versus-rest strategy to construct the k-class classification hyperplanes. A coordinate descent system is utilized to reduce the computational complexity of the training. The boot-strap technique with a 95% confidence interval and Friedman test are conducted to quantify the significance of the performance improvements observed in numerical evaluations." According to the news editors, the research concluded: "Experiments on 24 benchmark datasets demonstrate the proposed method produces promising results as compared with other support vector machine models reported in the literature." This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Robotics Findings from Seoul Described (Complete coverage path planning scheme for autonomous navigation ROS-based robots)

    15-16页
    查看更多>>摘要：New research on robotics is the subject of a new report. According to news originating from Seoul, South Korea, by NewsRx correspondents, research stated, "In this paper, a new Complete Coverage Path Planning (CCPP) scheme is proposed which combines path planning and dynamic tracking for robot operating system-based robots." Financial supporters for this research include National Research Foundation of Korea; Ministry of Education. Our news reporters obtained a quote from the research from Division of Electronics and Electrical Engineering: "For the path planning, firstly a sub-area division algorithm is considered to decompose the occupancy map according to the wall or obstacle position after simultaneous localization and mapping process. For each sub-area, an 'S' shape path planning is employed, and then a Bidirectional A-star connects them. Additionally, the dynamic tracking ensures that the robot moves continuously." According to the news editors, the research concluded: "Simulation results show that the coverage ratio of the planning path is improved as 98% by the proposed scheme."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Findings from Emory University Broaden Understanding of Machine Learning (Upscaling Soil Organic Carbon Measurements At the Continental Scale Using Multivariate Clustering Analysis and Machine Learning)

    16-17页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Atlanta, Georgia, by NewsRx journalists, research stated, "Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps." Financial supporters for this research include National Science Foundation (NSF), National Science Foundation (NSF), United States Department of Energy (DOE), United States Department of Energy (DOE), National Science Foundation through the NEON Program, Regional and Global Model Analysis (RGMA) activity of the Earth Environmental Systems Modeling (EESM) Program in the Earth and Environmental Systems Sciences Division (EESSD) of the Office of Biological and Environmental Research (BER) in the US Departme, United States Department of Energy (DOE). The news correspondents obtained a quote from the research from Emory University, "We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 +/- 3.2 Pg for 0-30 cm and 108.3 +/- 8.2 Pg for 0-100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0-30 cm and 90.7 Pg for 0-100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0-30 cm and 133.0 Pg for 0-100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach (R2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database (R2 = 0.23) and SoilGrids 2.0 (R2 = 0.39) for the topsoil (0-30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest."
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Findings from Chinese Academy of Sciences Update Knowledge of Machine Learning (Structure-based Reaction Descriptors for Predicting Rate Constants By Machine Learning: Application To Hydrogen Abstraction From Alkanes By Ch3/h/o …)

    17-18页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, "Accurate determination of the thermal rate constants for combustion reactions is a highly challenging task, both experimentally and theoretically. Machine learning has been proven to be a powerful tool to predict reaction rate constants in recent years." Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "In this work, three supervised machine learning algorithms, including XGB, FNN, and XGB-FNN, are used to develop quantitative structure-property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH3, H, and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the hybrid XGB-FNN algorithm yields average deviations of 65.4%, 12.1%, and 64.5% on the prediction sets of alkanes + CH3, H, and O, respectively, whose performance is comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy () but typically requires cumbersome ab initio calculations. In addition, the XGB-FNN models could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability."
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    New Findings on Machine Learning from Changzhou University Summarized (Rapid Screening B-site Doping Ferroelectric Perovskite With High Curie Temperature for Electronic Applications By a Novel Idbo-rf Approach)

    18-19页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news originating from Jiangsu, People's Republic of China, by NewsRx correspondents, research stated, "Ferroelectric perovskites with high Curie temperatures (Tc) have emerged as a significant focus in the field of electronic materials in recent years. However, accurately predicting Tc remains a longstanding challenge in the discovery of new functional materials." Financial support for this research came from Natural Science Foundation of Jiangsu Provincial Department of Science and Technology. Our news journalists obtained a quote from the research from Changzhou University, "Therefore, it is crucial to explore the relationship between potential materials descriptors and Tc, which can greatly expedite the identification of ferroelectric perovskite materials with high Tc. In this study, a novel intelligence approach was proposed as a promising solution for identifying Pb[B ' xB ' 1_ x]O3-type ferroelectric perovskite materials with B-site doping. To achieve this, a comprehensive set of features was utilized to generate synthetic dataset based on available experimental observations from literature and databases. By applying predictive machine learning (ML) models to analyze the synthetic dataset, four key descriptors were identified that exhibited strong correlations with Tc in these materials. First, considering the application of compressed sensing method to reduce high dimensional features, a novel descriptor cos(BCCvd)/(Ec * BCCe) was created and identified, which combined with the first ionization energy (IE1), the lattice energy difference between atoms and their neighboring coordinated atoms (BCCed), and the spin magnetic moment (GSm). These descriptors were found to be closely correlated with Tc of ferroelectric perovskites. Additionally, the Shapley Additive Explanation (SHAP) method was employed to analyze the relationship between Tc and the selected descriptors. Furthermore, based on the selected Random Forest (RF) model with superior performance among several machine learning models tested, an optimization algorithm was proposed combining Improved Dung Beetle Optimizer (IDBO) and RF regression for predicting Tc. Then the designed hybrid IDBO-RF model achieved a mean absolute error (MAE) of 13.95 K in Tc prediction, surpassing traditional methods based on tolerance factor and ionic displacement, which had a MAE of 30.2 K. From a pool of 2745 candidate materials, two potential ferroelectric perovskites with high Tc were successfully screened and identified."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Maribor Researcher Discusses Findings in Robotics (Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities)

    19-20页
    查看更多>>摘要：Research findings on robotics are discussed in a new report. According to news reporting from Maribor, Slovenia, by NewsRx journalists, research stated, "Workpiece placement plays a crucial role when performing complex surface machining task robotically." Financial supporters for this research include Slovenian Research Agency; Republic of Slovenia. The news reporters obtained a quote from the research from University of Maribor: "If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, especially when performing a complex surface machining task with a collaborative robot, which tend to have lower motion capabilities than conventional industrial robots. Therefore, the kinematic and dynamic capabilities within the robot workspace should be evaluated prior to task execution and optimized considering specific task requirements. In order to estimate maximum directional kinematic capabilities considering the requirements of the surface machining task in a physically consistent and accurate way, the Decomposed Twist Feasibility (DTF) method will be used in this paper. Estimation of the total kinematic performance capabilities can be determined accurately and simply using this method, adjusted specifically for robotic surface machining purposes." According to the news editors, the research concluded: "In this study, we present the numerical results that prove the effectiveness of the DTF method in identifying the optimal placement of predetermined machining tasks within the robot's workspace that requires lowest possible joint velocities for task execution. These findings highlight the practicality of the DTF method in enhancing the feasibility of complex robotic surface machining operations."
      原文链接:
    • NETL
    • NSTL
    • Mdpi