查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on artificial intelligence are present ed in a new report. According to news reporting originating from Houston, Texas, by NewsRx correspondents, research stated, "Dew point pressure (DPP) emerges as a pivotal factor crucial for forecasting reservoir dynamics regarding condensat e-to-gas ratio and addressing production/completion hurdles, alongside calibrati ng EOS models for integrated simulation." Our news journalists obtained a quote from the research from University of Houst on: "However, DPP presents challenges in terms of predictability. Acknowledging these complexities, we introduce a state-of-the-art approach for DPP estimation utilizing advanced machine learning (ML) techniques. Our methodology is juxtapos ed against published empirical correlation-based methods on two datasets with li mited sizes and diverse inputs. With superior performance over correlation-based estimators, our ML approach demonstrates adaptability and resilience even with restricted training datasets, spanning various fluid classifications. We acquire d condensate PVT data from publicly available sources and GeoMark RFDBASE, encom passing dew point pressure (the target variable), as well as compositional data (mole percentages of each component), temperature, molecular weight (MW), and sp ecific gravity (SG) of heptane plus, which served as input variables. Before ini tiating the study, thorough assessments of measurement quality and results using statistical methods were conducted leveraging domain expertise."
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on machine translati on have been published. According to news originating from Tamil Nadu, India, by NewsRx editors, the research stated, "The data-hungry statistical machine trans lation (SMT) and neural machine translation (NMT) models offer state-of-the-art results for languages with abundant data resources. However, extensive research is imperative to make these models perform equally well for low-resource languag es." Our news correspondents obtained a quote from the research from Vellore Institut e of Technology: "This paper proposes a novel approach to integrate the best fea tures of the NMT and SMT systems for improved translation performance of low-res ource English-Tamil language pair. The suboptimal NMT model trained with the sma ll parallel corpus translates the monolingual corpus and selects only the best t ranslations, to retrain itself in the next iteration. The proposed method employ s the SMT phrase-pair table to determine the best translations, based on the max imum match between the words of the phrasepair dictionary and each of the indiv idual translations. This repeating cycle of translation and retraining generates a large quasi-parallel corpus, thus making the NMT model more powerful. SMT-int egrated incremental training demonstrates a substantial difference in translatio n performance as compared to the existing approaches for incremental training. T he model is strengthened further by adopting a beam search decoding strategy to produce k best possible translations for each input sentence."
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Multielement dec ision-making is crucial for the robust deployment of human-robot collaboration ( HRC) systems in flexible manufacturing environments with personalized tasks and dynamic scenes. Large Language Models (LLMs) have recently demonstrated remarkab le reasoning capabilities in various robotic tasks, potentially offering this ca pability." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Shanghai Rising-Star Program, Shanghai Municipal Science and Technology Major Project, Shanghai Science and Technology Commission Project, F undamental Research Funds for the Central Universities.
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in robotic s. According to news reporting out of West Anhui University by NewsRx editors, r esearch stated, "A time-synchronized (TS) convergence control method for robotic manipulators is proposed." Funders for this research include Scientific Research Projects of Universities i n Anhui Province; Startup Fund For Distinguished Scholars of West Anhui Universi ty; Smart Agriculture And Forestry And Smart Equipment Scientific Research And I nnovation Team. Our news journalists obtained a quote from the research from West Anhui Universi ty: "Adversely to finite-time control, a notion of time-synchronization converge nce is introduced based on the ratio persistence property, which can ensure that all system components converge simultaneously in a finite time. Firstly, a robu st disturbance observer is constructed to be compatible with the time-synchroniz ed control framework and precisely estimate system uncertainties. Furthermore, w e design a (finite) timesynchronized controller to ensure that all states of th e robotic manipulator simultaneously converge to an equilibrium point, irrespect ive of initial conditions."
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics - Robotics and Automation. According to news reporting out of Paris, Fran ce, by NewsRx editors, research stated, "The robotic shape control of deformable linear objects has garnered increasing interest within the robotics community. Despite recent progress, the majority of shape control approaches can be classif ied into two main groups: open-loop control, which relies on physically realisti c models to represent the object, and closed-loop control, which employs less pr ecise models alongside visual data to compute commands." Financial support for this research came from SOFTMANBOT project from the Europe an Union'sHorizon 2020 Research and Innovation Programme. Our news journalists obtained a quote from the research from Sorbonne University , "In this letter, we present a novel 3D shape control approach that includes th e physically realistic Cosserat model into a closed-loop control framework, usin g vision feedback to rectify errors in real-time. This approach capitalizes on t he advantages of both groups: the realism and precision provided by physics-base d models, and the rapid computation, therefore enabling real-time correction of model errors, and robustness to elastic parameter estimation inherent in vision- based approaches. This is achieved by computing a deformation Jacobian derived f rom both the Cosserat model and visual data."
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Robotics. Acc ording to news reporting originating in Assam, India, by NewsRx journalists, res earch stated, "We initiate the study of the Mutual Visibility problem using N op aque luminous point robots that have inaccurate movements. Each robot operates i n Look-Compute-Move cycles and has a persistent light attached to it to have a w eak form of communication between robots using a constant number of colors." Financial supporters for this research include Government of India through the P rime Minister's Research Fellowship (PMRF), Council of Scientific & Industrial Research (CSIR) - India.
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on robotics have bee n published. According to news reporting from Shenzhen, People's Republic of Chi na, by NewsRx journalists, research stated, "The measurement step of the existin g calibration approaches for robotic manipulators can take a considerable amount of time to settle a robotic manipulator down at certain static configurations, making the calibration approaches time-consuming." The news reporters obtained a quote from the research from Harbin Institute of T echnology: "For applications of robotic manipulators requiring periodic recalibr ation (e.g., human-robot collaborative production lines and robotic inspecting s ystems), the time consumption of the data collection phase is a critical issue. This paper proposes a fast kinematic calibration approach for robotic manipulato rs, based on the measurement of a robotic manipulator tracking only a smooth and continuous time-optimal trajectory, rather than static measurement. Data sample s on configurations are recorded continuously without settling the robotic manip ulator down."
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting from Cambridge, Massachusetts, by NewsRx journalists, research stated, "Microrobotic platforms hold significant potential to advance a variety of fields, from medicine to environmental sensing. Herein, minimally functional robotic entities modeled on readily achievable state-of-th e-art features in a modern lab or cleanroom are computationally simulated." Financial support for this research came from Army Research Office.
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting originating from Harbin, People's R epublic of China, by NewsRx correspondents, research stated, "Modular self-recon figurable robots (MSRRs) have significantly progressed in hardware and algorithm development. However, they are generally used in terrestrial environments, leav ing broad scenarios to be explored and benefited." Financial supporters for this research include National Science Fund for Disting uished Young Scholars, National Natural Science Foundation of China (NSFC).
查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning - Co mputational Intelligence have been presented. According to news reporting out of Nanjing, People's Republic of China, by NewsRx editors, research stated, "In re cent works on semantic segmentation, there has been a significant focus on desig ning and integrating transformer-based encoders. However, less attention has bee n given to transformer-based decoders." 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 Nanjing Univers ity of Aeronautics and Astronautics, "We emphasize that the decoder stage is equ ally vital as the encoder in achieving superior segmentation performance. It dis entangles and refines high-level cues, enabling precise object boundary delineat ion at the pixel level. In this paper, we introduce a novel transformer-based de coder called MUSTER, which seamlessly integrates with hierarchical encoders and consistently delivers high-quality segmentation results, regardless of the encod er architecture. Furthermore, we present a variant of MUSTER that reduces FLOPS while maintaining performance. MUSTER incorporates carefully designed multi-head skip attention (MSKA) units and introduces innovative upsampling operations. Th e MSKA units enable the fusion of multi-scale features from the encoder and deco der, facilitating comprehensive information integration. The upsampling operatio n leverages encoder features to enhance object localization and surpasses tradit ional upsampling methods, improving mIoU (mean Intersection over Union) by 0.4% to 3.2%. On the challenging ADE20K dataset, our best model achieves a single-scale mIoU of 50.23 and a multi-scale mIoU of 51.88, which is on-par w ith the current state-of-the-art model."
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