查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery-Thyroidectomy is the subject of a report. According to news originating from Fuzhou, People's Republic of China, by NewsRx correspondents, research stated, "Robotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This study investigates the impact of the da Vinci Surgical System on operative efficiency, learning curve, and postoperative outcomes in thyroid surgery." Our news journalists obtained a quote from the research from Fujian Medical University Union Hospital, "We conducted a retrospective cohort study of 104 patients who underwent robotic thyroidectomy between March 2018 and January 2022. We evaluated the learning curve using the Cumulative Sum (CUSUM) analysis and analyzed operative times, complication rates, and postoperative recovery metrics. The cohort had a mean age of 36 years, predominantly female (68.3%). The average body mass index (BMI) was within the normal range. A significant reduction in operative times was observed as the series progressed, with no permanent hypoparathyroidism or recurrent laryngeal nerve injuries reported. The learning curve plateaued after the 37th case. Postoperative recovery was consistent, with no significant difference in hospital stay duration. Complications were minimal, with a noted decrease in transient vocal cord palsy as experience with the robotic system increased. Robotic thyroidectomy using the da Vinci system has demonstrated a significant improvement in operative efficiency without compromising safety. The learning curve is steep but manageable, and once overcome, it leads to improved surgical outcomes and high patient satisfaction."
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 Support Vector Machines have been published. According to news reporting from Dalian, People's Republic of China, by NewsRx journalists, research stated, "Considering the limited accuracy of classical empirical formulas and traditional Machine Learning (ML) models for predicting the depth-averaged velocity of flow through submerged vegetation, in this article, a novel hybrid ML model named BO-LSSVM is developed that incorporates Bayesian Optimization (BO) into Least Squares Support Vector Machine (LSSVM). Comparing with standalone LSSVM, BO helps LSSVM to find the optimal hyperparameter combination and thus promotes its prediction accuracy." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC).
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 Machine Learning have been published. According to news reporting originating in Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "Co-gasification of biomass and municipal solid waste (MSW) exhibits synergistic effects by improving the quality of syngas while reducing environmental pollution from MSW. In this study, Machine learning (ML) techniques were employed to investigate the co-gasification process of biomass and MSW." The news reporters obtained a quote from the research from the Chinese Academy of Sciences, "A comprehensive dataset was constructed using existing data, including different feedstock types and operating conditions, with 18 input features and 9 output features. Four advanced ML models were utilized to model and analyze the co-gasification process. By leveraging feedstock characteristics and operating parameters, key gasification parameters such as syngas composition, lower heating value (LHV) of syngas, tar yield, and carbon conversion efficiency were predicted. The results showed that all four models exhibited excellent predictive performance, with R2 values greater than 0.9 in both the training and testing stage. Specifically, Histogram-based gradient boosting regression (HGBR) exhibited the lowest root mean square error (RMSE) in predicting CO, while the gradient boosting regressor (GBR) achieved the best performance in H2 prediction with a RMSE of 1.6. The most influential input features for CO concentration were equivalence ratio (ER), oxygen content in biomass and hydrogen content in biomass." According to the news reporters, the research concluded: "The key features affecting H2 concentration were steam/fuel and ER."
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 originating from Keelung, Taiwan, by NewsRx editors, the research stated, "This study used real-time image processing to realize obstacle avoidance and indoor navigation with an omnidirectional wheeled mobile robot (WMR)." Our news correspondents obtained a quote from the research from National Taiwan Ocean University: "The distance between an obstacle and the WMR was obtained using a depth camera. Real-time images were used to control the robot's movements. The WMR can extract obstacle distance data from a depth map and apply fuzzy theory to avoid obstacles in indoor environments. A fuzzy control system was integrated into the control scheme. After detecting a doorknob, the robot could track the target and open the door. We used the speeded up robust features matching algorithm to recognize the WMR's movement direction." According to the news reporters, the research concluded: "The proposed control scheme ensures that the WMR can avoid obstacles, move to a designated location, and open a door. Like humans, the robot performs the described task only using visual sensors."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learning. According to news reporting originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, "Establishing a correlation between the friction coefficient and friction noise of metal-friction polymer interfaces is a challenging task in various environments. To address this issue, our study utilizes machine learning algo-rithms to construct a friction data-based model, elucidating the relationship between noise and friction coeffi-cient." Financial supporters for this research include Major National R & D Projects of China, National Natural Science Foundation of China (NSFC), Independent Research Project of State Key Laboratory of Tribology in Advanced Equipment. Our news editors obtained a quote from the research from Tsinghua University, "We propose the variational mode decomposition (VMD) along with five machine learning algorithms, each capturing unique data characteristics. Algorithm optimization is achieved through the implementation of L1 and L2 regularization methods."
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 Machine Learning have been published. According to news reporting originating in Espoo, Finland, by NewsRx journalists, research stated, "The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20-30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades." Financial support for this research came from European Commission Joint Research Centre. The news reporters obtained a quote from the research from Aalto University, "Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Robotics are presented in a new report. According to news reporting originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, "This article is devoted to addressing the cluster formation-containment tracking problem of networked robotic systems (NRSs) with unknown model uncertainties and disturbances under directed graphs. A novel hierarchical prescribed-time extended state observer (ESO) based control algorithm is developed such that all the robotic systems are divided into multiple subgroups." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from the China University of Geosciences, "For any subgroup, the master nodes form the different desired formation shapes at specific time points and the center of the shape follows the trajectory of the related target. Moreover, the follower nodes converge into the corresponding formation shapes. In the estimator loop, a cluster time-varying formation-containment tracking (TVFCT) algorithm is designed by employing a time-varying function such that the cluster formation shape can be guaranteed. In the local control loop, an extended state observer is employed to estimate the total disturbances (model uncertainties and disturbances) within a prescribed time. Then, a local control algorithm is designed by incorporating a sliding mode strategy such that the cluster TVFCT problem of the NRSs can be addressed within a prescribed time, where the convergence time can be set freely by choosing a tunable constant irrespective of the initial conditions. By constructing the Lyapunov function, several sufficient criteria for stability analysis are derived."
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 presented. According to news reporting originating from Innsbruck, Austria, by NewsRx correspondents, research stated, "Mecanum wheels are commonly used in mobile robotics applications as they enable a platform with three or more individually controlled wheels to move in arbitrary directions without turning. However, compared to the well-studied models for pneumatic and railroad wheels, modeling of Mecanum wheels remains mostly unexplored." Our news editors obtained a quote from the research from the University of Innsbruck, "In this research a novel, efficient Mecanum wheel model, based on orthotropic friction to model roller-ground contact, is presented together with experimental data. The equations are derived and the Mecanum wheel model is implemented in an open source multibody simulation code and shown to run faster than real-time with time steps in the order of milliseconds, enabling the model to be utilized for control tasks. Extensive experiments are performed with a real robot and statistical evaluation is presented. Both different robot velocities and changes in the center of mass, representing different payloads of the robot, are showcased."
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 Machine Learning. According to news reporting from Munich, Germany, by NewsRx journalists, research stated, "The morphology of a robot is typically assumed to be known, and data from external measuring devices are used mainly for its kinematic calibration. In contrast, we take an agent-centric perspective and ponder the vaguely explored question of whether a robot could learn elements of its morphology by itself, relying on minimal prior knowledge and depending only on unorganized proprioceptive signals." Funders for this research include Alfried Krupp von Bohlen und Halbach Foundation, Lighthouse Initiative Geriatronics by StMWi Bayern (Project X), Lighthouse Initiative KI.FABRIK Bayern by StMWi Bayern, Forschungsund Entwicklungsprojekt, Federal Ministry of Education & Research (BMBF).
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 Robotics is now available. According to news reporting out of Mexico City, Mexico, by NewsRx editors, research stated, "This article contributes to the design of a secondorder sliding-mode controller for the trajectory tracking problem in perturbed unicycle mobile robots. The proposed strategy takes into account the design of two particular sliding variables, which ensure the convergence of the tracking error to the origin in a finite time despite the effect of some external perturbations." Financial supporters for this research include SEP-CONACYT-ANUIES-ECOS NORD Project, ECOS NORD Project, Consejo Nacional de Ciencia y Tecnologia (CONACyT), TecNM Projects. Our news journalists obtained a quote from the research from National Polytechnic Institute, "The straightforward structure of the controller is simple to tune and implement. The global, uniform, and finite-time stability of the closed-loop tracking error dynamics is demonstrated by means of Lyapunov functions."
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