查看更多>>摘要：Research findings on robotics are discussed in a new report. According to news reporting originating from Cluj Napoca, Romania, by NewsRx correspondents, research stated, “Advancements in materials science and 3D printing technologies have opened up new avenues for developing low-cost robotic grippers with high-performance capabilities, making them suitable for various biomechatronic applications.” Financial supporters for this research include Eea Grants/norway Grants. Our news reporters obtained a quote from the research from Technical University: “In this research, it has been explored the utilization of high-performance polymer materials, such as Polyetherketoneketone (PEKK), Polyethylene Terephthalate Glycol (PET-G) and MED 857 (DraftWhite), in the designing and developing of customized robotic grippers. The primary focus of made analyses was oriented on materials characterization, both experimentally and analytically. Computer-Aided Engineering (CAE) methods were employed to simulate bending experiments, allowing for a comprehensive analysis of the mechanical behavior of the selected materials. These simulations were validated through physical bending experiments using samples fabricated via 3D printing technologies, including Fused Filament Fabrication (FFF) for PET-G and PEKK, as well as Jetted Photopolymer (PolyJet) technology employing UV Resin for MED 857. The findings of this research provided advantages of utilizing advanced materials like PEKK in low-cost robotic grippers for biomechatronic applications.”
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, “The control of the velocity of a high-speed laser- induced microjet is crucial in applications such as needle-free injection. Previous studies have indicated that the jet velocity is heavily influenced by the volumes of secondary cavitation bubbles generated through laser absorption.” Financial supporters for this research include Japan Society For The Promotion of Science; Japan Science And Technology Agency. Our news editors obtained a quote from the research from Tokyo University of Agriculture and Technology: “However, there has been a lack of investigation of the relationship between the positions of secondary cavitation bubbles and the jet velocity. In this study, we investigate the effects of secondary cavitation on the jet velocity of laser-induced microjets extracted using explainable artificial intelligence (XAI). An XAI is used to classify the jet velocity from images of secondary cavitation and to extract features from the images through visualization of the classification process. For this purpose, we run 1000 experiments and collect the corresponding images. The XAI model, which is a feedforward neural network (FNN), is trained to classify the jet velocity from the images of secondary cavitation bubbles. After achieving a high classification accuracy, we analyze the classification process of the FNN.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Levanger, Norway, by NewsRx journalists, research stated, “Medical histories of patients can predict a patient's immediate future. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carried out selective feature engineering from longitudinal medical records in this study to develop a dataset with derived features.” The news reporters obtained a quote from the research from Nord University, “We thereafter trained multiple machine learning models for the binary prediction of whether an episode of care will culminate in death among patients suspected of bloodstream infections. The machine learning classifier performance is evaluated and compared and the feature importance impacting the model output is explored. The extreme gradient boosting model achieved the best performance for predicting death in the next hospital episode with an accuracy of 92%.” According to the news reporters, the research concluded: “Age at the time of the first visit, length of history, and information related to recent episodes were the most critical features.” This research has been peer-reviewed.
查看更多>>摘要：New study results on robotics have been published. According to news originating from Hubei, People's Republic of China, by NewsRx correspondents, research stated, “IntroductionIn the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues.” The news correspondents obtained a quote from the research from Hanjiang Normal University: “Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has not yet received sufficient attention. MethodsTo fill this research gap, we propose a YOLOv5-PPO model based on A3C optimization. This model cleverly combines the target detection capabilities of YOLOv5 and the PPO reinforcement learning algorithm, aiming to improve the efficiency and accuracy of collaborative operations among logistics and warehousing robot groups. ResultsThrough extensive experimental evaluation on multiple datasets and tasks, the results show that in different scenarios, our model can successfully achieve multi-robot collaborative operation, significantly improve task completion efficiency, and maintain target detection and environment High accuracy of understanding.
查看更多>>摘要：Current study results on Robotics have been published. According to news reporting out of Hangzhou, People's Republic of China, by NewsRx editors, research stated, “Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation. In this letter, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Zhejiang University, “For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form. This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state. Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints. We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance.”
查看更多>>摘要：Data detailed on robotics have been presented. According to news reporting originating from Southern University of Science and Technology (SUSTech) by NewsRx correspondents, research stated, “Robotic metamaterials represent an innovative approach to creating synthetic structures that combine desired material characteristics with embodied intelligence, blurring the boundaries between materials and machinery.” Funders for this research include National Natural Science Foundation of China; Ministry of Science And Technology of The People&apos,S Republic of China; Science, Technology And Innovation Commission of Shenzhen Municipality. Our news reporters obtained a quote from the research from Southern University of Science and Technology (SUSTech): “Inspired by the functional qualities of biological skin, integrating tactile intelligence into these materials has gained significant interest for research and practical applications. This study introduces a Soft Robotic Metamaterial (SRM) design featuring omnidirectional adaptability and superior tactile sensing, combining vision-based motion tracking and machine learning. The study compares two sensory integration methods to a state-of-the-art motion tracking system and force/torque sensor baseline: an internal-vision design with high frame rates and an external-vision design offering cost-effectiveness. The results demonstrate the internal-vision SRM design achieving an impressive tactile accuracy of 98.96%, enabling soft and adaptive tactile interactions, especially beneficial for dexterous robotic grasping. The external-vision design offers similar performance at a reduced cost and can be adapted for portability, enhancing material science education and robotic learning.”
查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating from Richland, Washington, by NewsRx correspondents, research stated, “Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate.” Funders for this research include United States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE). Our news editors obtained a quote from the research from Pacific Northwest National Laboratory, “To reduce or even eliminate the dependency on high-fidelity data, we propose a novel multi-fidelity architecture which is based on a feature space shared by the low-and high-fidelity solutions. In the feature space, the representations of the low-fidelity and high-fidelity solutions are adjacent by constraining their relative distance. The feature space is represented with an encoder and its mapping to the original solution space is effected through a decoder.”
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from Elazig, Turkey, by NewsRx journalists, research stated, “Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place.” Financial supporters for this research include Scientific And Technological Research Council of Turkiyetubitak; Ecomai Pentaeuripides; Scientific Research Projects Coordination Unit of Firat University. Our news editors obtained a quote from the research from Firat University: “Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed.”
查看更多>>摘要：Data detailed on Robotics have been presented. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, “Chatter is one of the main barriers that significantly limits the efficiency and quality of robotic drilling processes. In this work, we present an approach based on synchroextracting chirplet transform (SECT) for early chatter recognition in a robotic drilling system.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Shanghai, Shanghai Municipal Science and Technology Major Project. Our news editors obtained a quote from the research from Shanghai Jiao Tong University, “It mainly comprises four steps: signal preprocessing, time-frequency analysis (TFA), signal reconstruction, and indicator calculation. To remove the disturbances of the rotation-related frequencies, matrix notch filters are designed to preprocess the acquired vibration signal. The nonstationary and nonlinear properties of chatter acceleration signal are characterized by the SECT, which extracts the time-frequency (TF) points satisfying the instantaneous frequency (IF) equation to acquire an energy-concentrated TF representation. The whole signal is decomposed into several subsignals, and reconstruction for the SECT is then employed to reconstruct each subsignal for different frequency bands. To characterize signal energy and frequency distribution change, the energy entropy is selected as an indicator for chatter monitoring. The effectiveness and superiority of the presented recognition approach were verified by robotic drilling tests under various cutting parameters and part materials. The results demonstrated that the presented chatter recognition approach could identify the onset of robotic drilling chatter effectively and timely. Moreover, it detected the chatter 58.7 and 136.8 ms earlier on average than the synchroextracting-based and multisynchrosqueezing-based methods, respectively.”
查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating in Belfast, United Kingdom, by NewsRx journalists, research stated, “Energy poverty affects billions worldwide, including people in developed and developing countries. Identifying those living in energy poverty and implementing successful solutions require timely and detailed survey data, which can be costly, time-consuming, and difficult to obtain, particularly in rural areas.” Financial support for this research came from HM Treasury. The news reporters obtained a quote from the research from Queen's University Belfast, “Through machine learning, this study investigates the possibility of identifying vulnerable households by combining satellite remote sensing with socioeconomic survey data in the UK. In doing so, this research develops a machine learning-based approach to predicting energy poverty in the UK using the low income low energy efficiency (LILEE) indicator derived from a combination of remote sensing and socioeconomic data. Data on energy con-sumption, building characteristics, household income, and other relevant variables at the local authority level are fused with geospatial satellite imagery. The findings indicate that a machine learning algorithm incorporating geographical and environmental information can predict approximately 83% of districts with significant energy poverty.”
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