查看更多>>摘要：Current study results on Robotics-Ro botics and Automation have been published. According to news originating from Fu zhou, People's Republic of China, by NewsRx correspondents, research stated, "Th e comprehension of 3D semantic scenes holds paramount significance in autonomous driving and robotics technology. Nevertheless, the simultaneous achievement of real-time processing and high precision in complex, expansive outdoor environmen ts poses a formidable challenge." Funders for this research include National Natural Science Foundation of China ( NSFC), Major Science and Technology Program of Fujian Province, Foundation of Fu jian Province. Our news journalists obtained a quote from the research from Fuzhou University, "In response to this challenge, we propose a novel occupancy network named RTONe t, which is built on a teacher-student model. To enhance the ability of the netw ork to recognize various objects, the decoder incorporates dilated convolution l ayers with different receptive fields and utilizes a multi-path structure. Furth ermore, we develop an automatic frame selection algorithm to augment the guidanc e capability of the teacher network."
查看更多>>摘要：Our news journalists obtained a quote from the research from Tianjin University, "Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivat ive (PID) control is applied to maintain robot stability post-compensation.Findi ngsExperiments were conducted on the TriMule-600 hybrid robot. The results indic ate that the following errors of the five grating sensors are reduced by 94% , 93%, 80%, 75% and 88% re spectively, after compensation."
查看更多>>摘要：Investigators publish new report on Ro botics. According to news reporting from Macau, People's Republic of China, by N ewsRx journalists, research stated, "The rapid development of extended reality ( XR), including virtual and augmented reality, offers promising solutions to medi ate human-robot interactions and support robotic construction in various forms. However, there is still a lack of systematic analysis of how emerging XR technol ogies and robotics could be effectively integrated to achieve mutual benefits an d improve overall construction performance." Financial support for this research came from University of Macau.
查看更多>>摘要：Investigators publish new report on . According to news reporting originating from Changsha, People's Republic of Chin a, by NewsRx correspondents, research stated, "Multi-output regression aims to u tilize the correlation between outputs to achieve information transfer between d ependent outputs, thus improving the accuracy of predictive models." Financial supporters for this research include National Natural Science Foundati on of China. The news correspondents obtained a quote from the research from National Univers ity of Defense Technology: "Although the Bayesian support vector machine (BSVR) can provide both the mean and the predicted variance distribution of the data to be labeled, which has a large potential application value, its standard form is unable to handle multiple outputs at the same time. To solve this problem, this paper proposes a multi-output Bayesian support vector machine model (MBSVR), wh ich uses a covariance matrix to describe the relationship between outputs and ou tputs and outputs and inputs simultaneously by introducing a semiparametric late nt factor model (SLFM) in BSVR, realizing knowledge transfer between outputs and improving the accuracy of the model."
查看更多>>摘要：Investigators discuss new findings in Machine Learning-Computational Intelligence. According to news reporting origi nating in Shenyang, People's Republic of China, by NewsRx journalists, research stated, "Spatial-temporal sequence prediction is a hotspot problem, which is ver y important to the development of society. Due to the complexity and mutual infl uence of different regions, spatial-temporal data has complicated and dynamic sp atial-temporal correlation." Funders for this research include National Natural Science Foundation of China ( NSFC), Ministry of Education, China-111 Project.
查看更多>>摘要：Fresh data on Robotics are presented i n a new report. According to news reporting out of Jinan, People's Republic of C hina, by NewsRx editors, research stated, "This letter concentrates on the chall enging task of language-conditioned grasping detection in clutter, where the gra sping postures of objects should be generated for robots according to complicate d human instructions. Existing methods typically employ well-trained object dete ctors and leverage language similarities to localize a single object." Financial supporters for this research include Central Government Guiding Local Science and Technology Development Foundation of Shandong Province, Jinan City a nd University Cooperation Development Strategy Project, Meituan Academy of Robot ics Shenzhen.
查看更多>>摘要：2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Lea rning-Computational Intelligence. According to news reporting originating in Q ingdao, People's Republic of China, by NewsRx journalists, research stated, "Alt hough model based H-infinity control scheme for nonlinear continuous-time (CT) s ystems with unknown system dynamics has been extensively studied, model-free H-i nfinity control of nonlinear CT systems via Q-learning is still a challenging pr oblem. This paper develops a novel Q-learning based model-free H-infinity contro l scheme for nonlinear CT systems, where the adaptive critic and actor continuou sly and simultaneously update each other, eliminating the need for iterative ste ps." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Shandong Provincial, Developme nt Plan for Youth Innovation Teams in Higher Education Institutions in Shandong Province, Anhui Province Key Laboratory of Advanced Numerical Control Servo Tech nology.
查看更多>>摘要：Researchers detail new data in artific ial intelligence. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction da ta." Our news editors obtained a quote from the research from Shanghai Ocean Universi ty: "To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict th e weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature co rrelations. The random forest method is then used to determine the weights of fa ctors, followed by the construction of an optimized backpropagation (BP) neural network using the ISOA-BP hybrid optimization algorithm. The model's performance is compared with other machine learning algorithms, including support vector re gression, decision tree, gradient decision tree, and the BP network before optim ization. The results show that the random forest method effectively quantifies t he impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors."
查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news originating from Truro, Canada, by NewsRx co rrespondents, research stated, "Legacy soil datasets are a valuable resource and should be used to the greatest extent possible. However, such datasets may be i ncomplete, and lack observations for every attribute, as the dataset may be comp iled from multiple studies." Funders for this research include Forest Innovation Program of the Canadian Wood Fibre Centre, Natural Resources Canada, British Columbia Ministry of Water and Land Resource Stewardship, Natural Sciences and Engineering Research Council of Canada (NSERC), John R. Evans Leaders Fund, Spanish Government.
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting from Dearborn, Michigan, by NewsR x journalists, research stated, "ABSTRACT: Accurate river discharge forecasts fo r short to intermediate time intervals are crucial for decision-making related t o flood mitigation, the seamless operation of inland waterways management, and o ptimal dredging." Funders for this research include Coastal And Hydraulics Laboratory. The news correspondents obtained a quote from the research from University of Mi chigan-Dearborn: "River routing models that are physics based, such as RAPID (‘r outing application for parallel computation of discharge') or its variants, are used to forecast river discharge. These physics-based models make numerous assum ptions, including linear process modeling, accounting for only adjacent river in flows, and requiring brute force calibration of hydrological input parameters. A s a consequence of these assumptions and the missing information that describes the complex dynamics of rivers and their interaction with hydrology and topograp hy, RAPID leads to noisy forecasts that may, at times, substantially deviate fro m the true gauged values. In this article, we propose hybrid river discharge for ecast models that integrate physics-based RAPID simulation model with advanced d ata-driven machine learning (ML) models. They leverage runoff data of the waters hed in the entire basin, consider the physics-based RAPID model, take into accou nt the variability in predictions made by the physics-based model relative to th e true gauged discharge values, and are built on state-of-the-art ML models with different complexities. We deploy two different algorithms to build these hybri d models, namely, delta learning and data augmentation."
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