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    New Machine Learning Research from Swinburne University of Technology Discussed (Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction)

    10-11页
    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 ar tificial intelligence. According to news reporting from Hawthorn, Australia, by NewsRx journalists, research stated, “The Resin Transfer Moulding process receiv es great attention from both academia and industry, owing to its superior manufa cturing rate and product quality.” The news correspondents obtained a quote from the research from Swinburne Univer sity of Technology: “Particularly, the progression of its mould filling stage is crucial to ensure a complete reinforcement saturation. Contemporary process sim ulation methods focus primarily on physics-based approaches to model the complex resin permeation phenomenon, which are computationally expensive to solve. Thus , the application of machine learning and data-driven modelling approaches is of great interest to minimise the cost of process simulation. In this study, a com prehensive dataset consisting of mould filling patterns of the Resin Transfer Mo ulding process at different injection locations for a composite dashboard panel case study is presented.”
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    Colorectal Department Reports Findings in Colon Cancer (Comparison of early surg ical outcomes of robotic and laparoscopic colorectal cancer resection reported b y a busy district general hospital in England)

    11-12页
    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 - Colon Cance r is the subject of a report. According to news reporting originating from Londo n, United Kingdom, by NewsRx correspondents, research stated, “Robotic platforms provide a stable tool with high-definition views and improved ergonomics compar ed to laparoscopic approaches. The aim of this retrospective study was to compar e the intra- and short-term postoperative results of oncological resections perf ormed robotically (RCR) and laparoscopically (LCR) at a single centre.” Our news editors obtained a quote from the research from Colorectal Department, “Between February 2020 and October 2022, retrospective data on RCR were compared to LCR undertaken during the same period. Parameters compared include total ope rative time, length of stay (LOS), re-admission rates, 30- day morbidity. 100 RCR and 112 LCR satisfied inclusion criteria. There was no difference between the t wo group’s demographic and tumour characteristics. Overall, median operative tim e was shorter in LCR group [200 vs. 247.5 min, p<0.005], but this advantage was not observed with pelvic and muti-quadrant resections. There was no difference in the rate of conversion [5(5%) vs. 5(4.5%), p> 0.95] . With respect to perioperative outcomes, there was no difference in the overall morbidity, or mortality between RCR and LCR, in particular requirement for bloo d transfusion [3(3%) vs. 5(4.5%), p 0.72], prolonged ileus [9(9% ) vs. 15(13.2%), p 0.38], surgical site infectio ns [5(4%) vs. 5(4.4%), p> 0.95], anastomotic leak [7(7 % ) vs. 5(4.4%), p 0.55], and re-operation rate [9(9%) vs. 7(6.3%), p 0.6]. RCR had shorter LOS by one night, but this did not reach statistical significance. No di fference was observed in completeness of resection but there was a statically si gnificant increase in lymph node harvest in the robotic series.”
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    Studies from Shanghai Jiao Tong University Reveal New Findings on Robotics (Kine matic and Joint Compliance Modeling Method to Improve Position Accuracy of a Rob otic Vision System)

    12-13页
    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 reporting out of Shanghai, People’s Republic of China , by NewsRx editors, research stated, “In the field of robotic automation, achie ving high position accuracy in robotic vision systems (RVSs) is a pivotal challe nge that directly impacts the efficiency and effectiveness of industrial applica tions.” Funders for this research include National Natural Science Foundation of China. The news correspondents obtained a quote from the research from Shanghai Jiao To ng University: “This study introduces a comprehensive modeling approach that int egrates kinematic and joint compliance factors to significantly enhance the posi tion accuracy of a system. In the first place, we develop a unified kinematic mo del that effectively reduces the complexity and error accumulation associated wi th the calibration of robotic systems. At the heart of our approach is the formu lation of a joint compliance model that meticulously accounts for the intricacie s of the joint connector, the external load, and the self-weight of robotic link s. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for t he accurate integration of vision systems into robotic operations.”
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    Reports Outline Robotics Study Findings from Shandong University (Feature Extrac tion and Robot Path Planning Method In 3d Visionguided Welding for Multi-blade Wheel Structures)

    13-14页
    查看更多>>摘要：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 originating in Jinan, People’s Republic of China, by NewsRx journalists, research stated, “Multi -blade wheel structures are esse ntial components in many industrial and mechanical systems. Substituting manual welding with robot welding can significantly enhance processing efficiency for t hem.” Financial support for this research came from Taishan Industry Leading Talent Pr oject.
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    Department of Applied Geophysics Researchers Publish New Data on Machine Learnin g (A machine learning approach for the prediction of pore pressure using well lo g data of Hikurangi Tuaheni Zone of IODP Expedition 372, New Zealand)

    14-15页
    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 new report. According to news reporting originating from J harkhand, India, by NewsRx correspondents, research stated, “Pore pressure (PP) information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development. PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design, and mud weight estimation for wellbore stability.” Financial supporters for this research include Science And Engineering Research Board; Iilinois State Museum; Department of Science And Technology, Ministry of Science And Technology, India.
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    Chinese Research Academy of Environmental Sciences Reports Findings in Artificia l Intelligence (An artificial intelligence-based model for predicting reproducti ve toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflor us)

    15-16页
    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 originating from Beijing, People ’s Republic of China, by NewsRx correspondents, research stated, “The joint toxi city effects of mixtures, particularly reproductive toxicity, one of the main ca uses of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models t o predict the concentration response curve concerning the joint reproductive tox icity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer B rachionus calyciflorus: concentration addition (CA), independent action (IA), an d two deep neural network (DNN) models.”
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    Researchers from University of Bari Publish Research in Machine Learning (Machin e Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges)

    16-16页
    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 artificial in telligence have been published. According to news originating from the Universit y of Bari by NewsRx correspondents, research stated, “The graph model is nowaday s largely adopted to model a wide range of knowledge and data, spanning from soc ial networks to knowledge graphs (KGs), representing a successful paradigm of ho w symbolic and transparent AI can scale on the World Wide Web.” The news journalists obtained a quote from the research from University of Bari: “However, due to their unprecedented volume, they are generally tackled by Mach ine Learning (ML) and mostly numeric based methods such as graph embedding model s (KGE) and deep neural networks (DNNs). The latter methods have been proved lat ely very efficient, leading the current AI spring. In this vision paper, we intr oduce some of the main existing methods for combining KGs and ML, divided into t wo categories: those using ML to improve KGs, and those using KGs to improve res ults on ML tasks. From this introduction, we highlight research gaps and perspec tives that we deem promising and currently under-explored for the involved resea rch communities, spanning from KG support for LLM prompting, integration of KG s emantics in ML models to symbol-based methods, interpretability of ML models, an d the need for improved benchmark datasets.”
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    Investigators from Swiss Federal Institute of Technology Release New Data on Mac hine Learning (Time-to-green Predictions for Fully-actuated Signal Control Syste ms With Supervised Learning)

    17-17页
    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 reporting out of Zurich, Switzerland, by New sRx editors, research stated, “Recently, efforts have been made to standardize s ignal phase and timing (SPaT) messages. These messages contain signal phase timi ngs of all signalized intersection approaches.” Financial support for this research came from Swiss National Science Foundation (SNSF). Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Technology, “This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles . Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains ch allenging. This paper proposes a time series prediction framework using aggregat ed traffic signal and loop detector data. We utilize state-of-the-art machine le arning models to predict future signal phases’ duration. The performance of a Li near Regression (LR), Random Forest (RF), a light gradient-boosting machine (Lig htGBM), a bidirectional Long-Short-Term-Memory neural network (BiLSTM) and a Tem poral Convolutional Network (TCOV) are assessed against a naive baseline model.”
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    Investigators from Graduate School of Biomedical Engineering Target Machine Lear ning (Shape Programmable and Multifunctional Soft Textile Muscles for Wearable a nd Soft Robotics)

    18-18页
    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 Sydney, Australia, by NewsRx corr espondents, research stated, “Textiles are promising candidates for use in soft robots and wearable devices due to their inherent compliance, high versatility, and skin comfort. Planar fluidic textile-based actuators exhibit low profile and high conformability, and can seamlessly integrate additional components (e.g., soft sensors or variable stiffness structures [VSSs] ) to create advanced, multifunctional smart textile actuators.” Financial support for this research came from National Heart Foundation of Austr alia.
      原文链接:
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    Swiss Federal Institute of Technology Zurich (ETH) Researcher Updates Knowledge of Machine Learning (Automatic Detection of the Running Surface of Railway Track s Based on Laser Profilometer Data and Supervised Machine Learning)

    19-19页
    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 ar tificial intelligence. According to news originating from Zurich, Switzerland, b y NewsRx correspondents, research stated, “The measurement of the longitudinal r ail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head.” Financial supporters for this research include Federal Office For The Environmen t; Federal Office of Transport; Eth Zurich.
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