期刊,Robotics & Machine Learning Daily News 2024年卷Feb.22期_国家学术搜索

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Robotics & Machine Learning Daily News

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Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News

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Road features that predict crash sites identified in new machine- learning model

1-2页

查看更多>>摘要：AMHERST, Mass. - Issues such as abrupt changes in speed limits and incomplete lane markings are among the most influential factors that can predict road crashes, finds new research by University of Massachusetts Amherst engineers. The study then used machine learning to predict which roads may be the most dangerous based on these features. Published in the journal Transportation Research Record, the study was a collaboration between UMass Amherst civil and environmental engineers Jimi Oke, assistant professor; Eleni Christofa, associate professor; and Simos Gerasimidis, associate professor; and civil engineers from Egnatia Odos, a publicly owned engineering firm in Greece. The most influential features included road design issues (such as changes in speed limits that are too abrupt or guardrail issues), pavement damage (cracks that stretch across the road and webbed cracking referred to as "alligator" cracking) and incomplete signage and road markings.

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Researchers at Guangzhou University Release New Data on Machine Learning [Predicting the International Roughness Index of Jpcp and Crcp Rigid Pavement: a Random Forest (Rf) Model Hybridized With Modified Beetle Antennae Search (Mbas) for Higher ...]

2-3页

查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Guangzhou, People's Republic of China, by NewsRx correspondents, research stated, "To improve the prediction accuracy of the International Roughness Index (IRI) of Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP), a machine learning approach is developed in this study for the modelling, combining an improved Beetle Antennae Search (MBAS) algorithm and Random Forest (RF) model. The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study." Financial supporters for this research include Fundamental Research Funds for the Central Universities, Natural Science Foundation of Hunan Province, Hunan Provincial Transportation Technology Project. Our news editors obtained a quote from the research from Guangzhou University, "The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well. The results by the comparative analysis showed the prediction accuracy of the IRI of the newly developed MBAS and RF hybrid machine learning model (RF-MBAS) in this study is higher, indicated by the RMSE and R values of 0.2732 and 0.9476 for the JPCP as well as the RMSE and R values of 0.1863 and 0.9182 for the CRCP. The accuracy of this obtained result far exceeds that of the IRI prediction model used in the traditional Mechanistic-Empirical Pavement Design Guide (MEPDG), indicating the great potential of this developed model."

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Tech Science Press

University of Tsukuba Researchers Publish New Studies and Find- ings in the Area of Robotics (Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Planta- tions)

3-4页

查看更多>>摘要：Investigators publish new report on robotics. According to news reporting from Tsukuba, Japan, by NewsRx journalists, research stated, "Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles." The news editors obtained a quote from the research from University of Tsukuba: "However, au- tonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intel- ligent vision system would have the potential to achieve the desired outcome by utilizing an autonomous weeder to perform operations in uncut sections. Therefore, the objective of this study is to develop a vision module using a custom-trained dataset on YOLO instance segmentation algorithms to support autonomous robotic weeders in recognizing uncut weeds and obstacles (i.e., fruit tree trunks, fixed poles) within rows. The training dataset was acquired from a pear orchard located at the Tsukuba Plant Innovation Research Center (T-PIRC) at the University of Tsukuba, Japan. In total, 5000 images were preprocessed and labeled for training and testing using YOLO models. Four versions of edge-device-dedicated YOLO instance seg- mentation were utilized in this research-YOLOv5n-seg, YOLOv5s-seg, YOLOv8n-seg, and YOLOv8s-seg-for real-time application with an autonomous weeder. A comparison study was conducted to evaluate all YOLO models in terms of detection accuracy, model complexity, and inference speed. The smaller YOLOv5-based and YOLOv8-based models were found to be more efficient than the larger models, and YOLOv8n-seg was selected as the vision module for the autonomous weeder. In the evaluation process, YOLOv8n-seg had better segmentation accuracy than YOLOv5n-seg, while the latter had the fastest inference time."

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Mdpi

Researchers from Fraunhofer Heinrich Hertz Institute Report on Findings in Machine Learning (Distributed Machine-learning for Early Harq Feedback Prediction In Cloud Rans)

4-5页

查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating in Berlin, Germany, by NewsRx journalists, research stated, "In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose a Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup." Financial support for this research came from Federal Ministry of Education and Research of Germany in the Programme of "Souvern. Digital. Vernetzt." Joint Project 6G-RIC.

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IEEE

New Robotics Study Findings Have Been Reported by Researchers at Shenzhen University (Interaction-driven Active 3d Reconstruction With Object Interiors)

5-6页

查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting from Shenzhen, People's Republic of China, by NewsRx journalists, research stated, "We introduce an active 3D reconstruction method which integrates visual perception, robot-object interaction, and 3D scanning to recover both the exterior and interior, i.e., unexposed, geometries of a target 3D object. Unlike other works in active vision which focus on optimizing camera viewpoints to better investigate the environment, the primary feature of our reconstruction is an analysis of the interactability of various parts of the target object and the ensuing part manipulation by a robot to enable scanning of occluded regions." Funders for this research include National Natural Science Foundation of China (NSFC), DEGP Innova- tion Team, GD Natural Science Foundation, Shenzhen Science and Technology Program, Natural Sciences and Engineering Research Council of Canada (NSERC), Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ).

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Assoc Computing Machinery

Sao Paulo State University (UNESP) Reports Findings in Artificial Intelligence (State of the art and prospects for artificial intelligence in orthognathic surgery: A systematic review with meta-analysis)

6-6页

查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Sao Paulo, Brazil, by NewsRx journalists, research stated, "To present a systematic review of the state of the art regarding clinical applications, main features, and outcomes of artificial intelligence (AI) in orthognathic surgery. The PICOS strategy was performed on a systematic review (SR) to answer the following question: 'What are the state of the art, characteristics and outcomes of applications with artificial intelligence for orthognathic surgery?' After registering in PROSPERO (CRD42021270789) a systematic search was performed in the databases: PubMed (including MedLine), Scopus, Embase, LILACS, MEDLINE EBSCOHOST and Cochrane Library. 195 studies were selected, after screening titles and abstracts, of which thirteen manuscripts were included in the qualitative analysis and six in the quantitative analysis."

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University of Oxford Reports Findings in Rheumatoid Arthritis (Dig- ital health technologies and machine learning augment patient re- ported outcomes to remotely characterise rheumatoid arthritis)

7-7页

查看更多>>摘要：New research on Autoimmune Diseases and Conditions - Rheumatoid Arthritis is the subject of a report. According to news reporting originating in Oxford, United Kingdom, by NewsRx journalists, research stated, "Digital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC)." The news reporters obtained a quote from the research from the University of Oxford, "Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients' Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r, 0.692; RMSE, 1.33)."

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Researchers from University of Luxembourg Detail Findings in Robotics (Actor-critic Learning Based Pid Control for Robotic Ma- nipulators)

8页

查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting from Lux- embourg, Luxembourg, by NewsRx journalists, research stated, "In this paper, we propose a reinforcement learning structure for auto-tuning PID gains by solving an optimal tracking control problem for robot ma- nipulators. Capitalizing on the actor-critic framework implemented by neural networks, we achieve optimal tracking performance, estimating unknown system dynamics simultaneously." The news correspondents obtained a quote from the research from the University of Luxembourg, "The critic network is used to approximate the cost function, which serves as an indicator of control performance. With feedback from the critic, the actor network learns time-varying PID gains over time to optimize control input, thereby steering the system toward optimal performance. Furthermore, we utilize Lyapunov's direct method to demonstrate the stability of the closed-loop system. This approach provides an analytical procedure for a stable robot manipulator system to systematically adjust PID gains, bypassing the ad-hoc and painstaking process. The resultant actor-critic PID-like control exhibits stable adaptive and learning capabilities while maintaining a simple structure and inexpensive online computational demands."

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Elsevier

Study Findings from University of Science and Technology Beijing Broaden Understanding of Machine Learning (Knowledge-driven Ex- perimental Discovery of Ce-based Metal Oxide Composites for Se- lective Catalytic Reduction of Nox ...)

9-9页

查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, "Mining the scientific literature, combined with data-driven methods, may assist in the identification of optimized catalysts. In this paper, we employed interpretable machine learning to discover ternary metal oxides capable of selective catalytic reduction of nitrogen oxides with ammonia (NH3-SCR)." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities, University of Science and Technology Beijing, China Scholarship Council.

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Amer Chemical Soc

New Machine Learning Findings from Zhejiang University Dis- cussed (Machine Learning-based Prediction and Generation Model for Creep Rupture Time of Nickel-based Alloys)

10-10页

查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Pre- dicting the creep rupture time of Nickel-based alloys has been one of the central topics in materials science. This work is devoted to develop a comprehensive strategy based on machine learning for data generation and prediction." Financial support for this research came from NSFC key project of joint funds. Our news editors obtained a quote from the research from Zhejiang University, "Our objective is to improve significantly the accuracy and effectiveness of the rupture time prediction. The two different prediction models, residual learning combining liner regression model with nonlinear regression model, ensemble learning combining with genetic algorithm, are employed. Both of them are shown to achieve high accuracy on the training dataset and testing dataset. To overcome the limited availability of experimental data, a novel generation model is designed based on generative adversarial network. The new model is capable of generating a batch of high-quality synthetic dataset for expanding the scale of the training dataset. In particular, the best results obtained by the model for three metrics R2, RMSE, MAE are 0.9971, 0.1259, 0.0395. The proportion of the augmented synthetic dataset is also discussed."

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Elsevier