查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news originating from Dalian, People's Republic o f China, by NewsRx correspondents, research stated, "Pavement performance evalua tion based on modulus is crucial for controlling the overall performance of pave ments and decisions making throughout the pavement's life cycle. Falling weight deflectometer (FWD) tests are commonly employed to collect deflection data, whic h is subsequently back-calculated to get each layer's modulus." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Urumqi Transportation Research Project, Shanxi Province Tran sportation Research Project. Our news journalists obtained a quote from the research from the Dalian Universi ty of Technology, "However, existing studies lack a complete framework for incor porating the bedrock condition in the backcalculation process. Here, an integra ted process of pavement performance evaluation utilizing FWD tests is proposed, and the focus is on the classification of bedrock condition by modern classifica tion algorithms (BPNN, MLP, SVM, and RF) to determine the presence or absence of bedrock and its depth range. The implementation of classification process allow s for the inclusion of bedrock influence in the back-calculation process, thereb y improving the accuracy of modulus results. Results from the four classificatio n algorithms reveals that RF is the most suitable for classifying bedrock depth, exhibiting superior overall performance."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news originating from Baja California, Mexico, by NewsRx c orrespondents, research stated, "Optical Sensors Fusion is intended to enrich th e data obtained from Robotic Vision systems, which play a crucial role in applic ations such as machine guidance and monitoring. This paper presents a data augme ntation method that uniquely combines cameras with the rotational wide-based Las er Scanner Technical Vision System (LSTVS), an innovative system not previously explored in conjunction with other 3D data acquisition systems." Our news journalists obtained a quote from the research from the Autonomous Univ ersity of Baja California, "This combined work aims to address common issues in 3D reconstruction by utilizing a deterministic position estimation from the lase r scanner complementary to probabilistic estimations from stereo vision. This ap proach aims to reduce informational entropy in regions where data is lacking or difficult to interpret, primarily due to the inherent limitations of Robotic Vis ion systems. An LSTVS with stereo cameras prototype was calibrated using intrins ic and extrinsic parameters of the cameras and laser scanner components, enablin g laser positioning over selected interest point and areas from the stereo 3D da ta. By fusing 3D data from both systems, data quality is improved on challenging surfaces often problematic for stereo vision, like low texture or nonLambertian surfaces. Experiments aim to test the stereo system limits in order to fuse the obtained data. Multiple experiments with variable parameters (angle of view, st riking distance, most indicative kinds of the obstacle's refractive surface amon g them) are described in order to prove new abilities of the proposed combined R V."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Robotics. According to news reporting from Rio de Janeiro, Brazil, by NewsRx jou rnalists, research stated, "In this paper, we study mechanical optimal control p roblems on a given Riemannian manifold (Q, g) in which the cost is defined by a general cometric g. This investigation is motivated by our studies in robotics, in which we observed that the mathematically natural choice of cometric g = g* - the dual of g - does not always capture the true cost of the motion." Financial support for this research came from National Science Foundation The news correspondents obtained a quote from the research from Federal Universi ty Rio de Janeiro, "We then, first, discuss how to encode the system's torque-ba sed actuators configuration into a cometric g. Second, we provide and prove our main theorem, which characterizes the optimal solutions of the problem associate d to general triples (Q, g, g) in terms of a 4th order differential equation. We also identify a tensor appearing in this equation as the geometric source of ‘b iasing' of the solutions away from ordinary Riemannian splines and geodesics for (Q, g)."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are disc ussed in a new report. According to news originating from Beijing, People's Repu blic of China, by NewsRx correspondents, research stated, "Coalgangue sorting r obots have been established as a ground-breaking technology in mineral processin g,particularly for mitigating labor intensity and increasing production efficie ncy. Existing coal-gangue sorting robots tend to employ object detection algorit hms as the framework, but the crude bounding boxes provided by this approach is insufficient to support the robot in all sorting scenarios." Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news reporting out of Barcelona, Spain, by NewsRx editors, re search stated, "The roots of the closure polynomial associated with a given mech anism determine its assembly modes. In the case of 6R closed-loop mechanisms, th ese polynomials are usually expressed in the half-angle tangent of one of its jo ints."Financial support for this research came from Spanish Government. Our news journalists obtained a quote from the research from Spanish National Re search Council (CSIC), "In this paper, we derive closure polynomials of 6R robot s in terms of distances, not angles. The use of a distance-based formulation pro vides, in general, a fundamental advantage since it leads to closure conditions without requiring neither variable eliminations nor variable substitutions. We r estrict our attention, though, to robots with coplanar consecutive joint axes, i .e. , robots whose consecutive axes intersect at either proper or improper point s. We show that this particular arrangement of joints does not result in a reduc tion in the maximum number of the inverse kinematic solutions with respect to th e general case. Moreover, this family of robots include broadly used offset-wris t arms."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Victo ria, Australia, by NewsRx correspondents, research stated, "This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived co mputed tomography (CT) slices from the lumber (L3) region in colorectal cancer ( CRC) patients. A total of 541 axial CT slices at the L3 vertebra were retrospect ively collected from 319 patients with CRC diagnosed during 2012-2019 at a singl e Australian tertiary institution, Western Health in Melbourne." Our news editors obtained a quote from the research from the University of Melbo urne, "A twodimensional U-Net convolutional network was trained on 338 slices t o segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices ) and a test dataset (203 slices). The measurement of cross-sectional area and H ounsfield unit (HU) density of muscle, VAT and SAT were compared between two met hods. The segmentation for muscle, VAT and SAT demonstrated excellent performanc e for both the validation (Dice similarity coefficients > 0.98, respectively) and test (Dice similarity coefficients > 0.97, respectively) datasets. There was a strong positive correlation between ma nual and AI segmentation measurements of body composition for both datasets (Spe arman's correlation coefficients: 0.944-0.999, P<0.001)."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Blood Diseases and Con ditions - Sepsis is the subject of a report. According to news reporting origina ting from Nuremberg, Germany, by NewsRx correspondents, research stated, "Proact ive analysis of patient pathways helps healthcare providers anticipate treatment -related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions abo ut future events." Financial support for this research came from Friedrich-Alexander-Universitat Er langen-Nurnberg. Our news editors obtained a quote from the research from Technische Hochschule N urnberg Georg Simon Ohm, "However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for cl inicians to apply such models. Our work introduces PatWay-Net, an ML framework d esigned for interpretable predictions of admission to the intensive care unit (I CU) for patients with symptoms of sepsis. We propose a novel type of recurrent n eural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its u tility through a comprehensive dashboard that visualizes patient health trajecto ries, predictive outcomes, and associated risks. Our evaluation includes both pr edictive performance - where PatWay-Net outperforms standard models such as deci sion trees, random forests, and gradient-boosted decision trees - and clinical u tility, validated through structured interviews with clinicians."
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 reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "In this work, we proposed a deeply- integrated explainable pre-trained deep learning framework with stacked denoisin g autoencoders in the assessment of slope stability. The deep learning model con sists of a deep neural network as a trunk net for prediction and autoencoders as branch nets for denoising." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the Beijing Univers ity of Technology, "A comprehensive review of machine learning algorithms in slo pe stability evaluation is first given in the introduction section. A series of 530 data is then collected from real slope records, which are visualized and inv estigated in feature engineering and further preprocessed for model training. To ensure reliable and trustworthy model interpretability, a unified model from bo th local and global perspectives is integrated into the deep learning model, whi ch incorporated the ad hoc back-propagation based Deep SHAP, perturbation based Kernel SHAP and PDPs, and distillation based LIME and Anchors. For a fair evalua tion, repeated stratified 10-fold cross-validation is adopted in model evaluatio n. The obtained results manifest that the constructed model outperforms commonly used machine learning methods in terms of accuracy and stability on the real-wo rld slope data."
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 originating from Baton Rouge, Louisiana, by NewsRx correspondents, research stated, "Subsidence in southeastern Louisiana is a sig nificant geological issue caused by natural and human-induced factors like low-l ying topography and groundwater pumping. Human activities also led to coastal la nd loss and reduced sediment supply." Financial support for this research came from United States Geological Survey. Our news journalists obtained a quote from the research from Louisiana State Uni versity, "Satellitebased technologies such as Global Navigation Satellite Syste ms (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are used to monit or subsidence. Louisiana has about 130 continuously operating reference stations (CORS) monitoring subsidence statewide. GNSS provides accurate point measuremen ts but limited spatial coverage. InSAR, however, detects ground deformation over large areas using satellitebased radar imagery. In response to this advantage, we employed Sentinel-1 SAR images from 2017 to 2021 to estimate the vertical di splacement in East Baton Rouge (EBR) Parish. Significant displacement is found i n urban and industrial areas, particularly in high- and medium-density residenti al areas. The significant subsidence area is between Denham Spring and Baton Rou ge faults, where residential areas experience displacement of -0.7 to -1 cm/year . The displacement variation in land use indicates significant annual subsidence in some buildings and infrastructure. Three strategic facilities in Baton Rouge Downtown experienced displacement, with -6.1 mm/yr in Downtown, -2.99 mm/yr at Horace Wilkinson Bridge, and -4.94 mm/yr at central railway station. In addition , machine learning is employed to estimate the vertical displacement in the stud y area. The K-Nearest Neighbors (KNN) model provides a comprehensive understandi ng of subsidence estimation among the GBR (Gradient Boosting Regression), RFR (R andom Forest Regression), and KNN models."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Esophageal Cancer is the subject of a report. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Esophage al cancer is one of the leading causes of cancer-related deaths worldwide. The i dentification of residual tumor tissues in the surgical margin of esophageal can cer is essential for the treatment and prognosis of cancer patients." Our news journalists obtained a quote from the research from the Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, "But the current diagnostic methods, either pathological frozen section or paraffin section exam ination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular infor mation with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tum or tissue in surgically resected specimens. We tested five machine learning-base d classification methods, including k-Nearest Neighbors, Adaptive Boosting, Rand om Forest, Principal Component Analysis-Linear Discriminant Analysis, and Suppor t Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 % ) in classifying the esophageal tumor and normal tissues. The portable Raman sys tem demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on th is, we finally achieve successful Raman visualization of tumor boundaries on sur gical margin specimens, and the Raman measurement time is less than 5 min."
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