期刊,Robotics & Machine Learning Daily News 2024年卷Oct.9期_国家学术搜索

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Robotics & Machine Learning Daily News

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Researchers at King Mongkut's University of Technology Thonburi (KMUTT) Release New Data on Machine Learning (Effects of Spatial Microstructure Characteristics On Mechanical Properties of Dual Phase Steel By Inverse Analysis and Machine ... )

145-146页

查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting out of Bangkok, Thailand, by NewsRx edit ors, research stated, "This work aims to investigate complex relationship betwee n microstructure characteristics and mechanical properties of dual phase (DP) st eel through an inverse analysis based on Markov chain Monte Carlo (MCMC) method combined with meso-scale material modelling. In this framework, a machine learni ng approach as surrogate model was developed, in which support vector regression (SVR) and artificial neural network (ANN) were trained using results from repre sentative volume element (RVE) simulations coupled with damage model, which were previously calibrated with experimental data of commercial DP steel grades." Funders for this research include Petchra Pra Jom Klao Master's Degree Research Scholarship, Reserach Strengthening Project of the Faculty of Engineering from K ing Mongkut's University of Technology Thonburi, Thailand Advance Institute of S cience and Technology-Tokyo Institute of Technology (TAISTTokyo Tech), Thailand Science Research and Innovation (TSRI), National Science, Research and Innovati on Fund (NSRF), National Research Council of Thailand (NRCT).

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Researcher at Nanjing Normal University Publishes Research in Machine Learning ( Chemical Fractions and Magnetic Simulation Based on Machine Learning for Trace M etals in a Sedimentary Column of Lake Taihu)

148-149页

查看更多>>摘要：Current study results on artificial in telligence have been published. According to news reporting out of Nanjing, Peop le's Republic of China, by NewsRx editors, research stated, "In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters w ere analysed in the sedimentary column from the centre of Lake Taihu. The sedime ntary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 1 24 years old." Funders for this research include The Natural Science Foundation of Jiangsu Prov ince, China; The National Natural Science Foundation of China; The Open Fund of Key Laboratory of Water Treatment of Taihu Lake Basin, Ministry of Water Resourc es.

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Medical University of Sofia Reports Findings in Artificial Intelligence (Artific ial intelligence as a tool in drug discovery and development)

149-150页

查看更多>>摘要：New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Sofia, Bulgaria, by NewsRx journalists, research stated, "The rapidly advancing field o f artificial intelligence (AI) has garnered substantial attention for its potent ial application in drug discovery and development. This opinion review criticall y examined the feasibility and prospects of integrating AI as a transformative t ool in the pharmaceutical industry." The news reporters obtained a quote from the research from the Medical Universit y of Sofia, "AI, encompassing machine learning algorithms, deep learning, and da ta analytics, offers unprecedented opportunities to streamline and enhance vario us stages of drug development. This opinion review delved into the current lands cape of AI-driven approaches, discussing their utilization in target identificat ion, lead optimization, and predictive modeling of pharmacokinetics and toxicity . We aimed to scrutinize the integration of large-scale omics data, electronic h ealth records, and chemical informatics, highlighting the power of AI in uncover ing novel therapeutic targets and accelerating drug repurposing strategies. Desp ite the considerable potential of AI, the review also addressed inherent challen ges, including data privacy concerns, interpretability of AI models, and the nee d for robust validation in real-world clinical settings. Additionally, we explor ed ethical considerations surrounding AI-driven decision-making in drug developm ent. This opinion review provided a nuanced perspective on the transformative ro le of AI in drug discovery by discussing the existing literature and emerging tr ends, presenting critical insights and addressing potential hurdles."

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Researchers at China University of Geosciences Report New Data on Robotics (A Se lf-learning Memetic Algorithm for Human-robot Collaboration Scheduling In Energy -efficient Distributed Mixed Fuzzy Welding Shop)

150-151页

查看更多>>摘要：2024 OCT 09 (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 out of Wuhan, People's Republic of China, by NewsRx edi tors, research stated, "Due to the impact of economic globalization, distributed welding shop has become prevalent in real-world manufacturing systems. Moreover , focusing on human-centric, sustainable and resilient industry, Industry 5.0 pu ts more emphasis on human-robot collaboration (HRC) for its merit in promoting s ystem flexibility and adaptability." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Key Research and Development Program Project in Hubei Provin ce. Our news journalists obtained a quote from the research from the China Universit y of Geosciences, "However, owing to the instability of human performance, it be comes necessary to employ fuzzy processing time to simulate practical human prod uction. In the context of Industry 5.0, HRC scheduling in distributed mixed fuzz y welding shop is worth exploring, but no related research on this problem is re ported. Thus, to address this research gap, this paper investigates a human-robo t collaboration energy-efficient distributed mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC), aiming to minimize makespan and total energy consumption (TEC). To solve this issue, a self-learning memetic algorithm (SLMA) is propose d. In SLMA, a hybrid initialization is designed to yield a high-quality initial population. A genetic operator is proposed to improve the exploration capability . A self-learning variable neighborhood search (SLVNS), which hybridizes Q-learn ing and VNS, is developed to enhance the exploitation capability. A resource adj ustment strategy is presented to further optimize TEC. Additionally, to validate the effectiveness of the proposed SLMA, extensive experimental comparisons with 5 other optimization algorithms are conducted. Experimental results illustrate that SLMA outperforms its competitors. Note to Practitioners-Owing to the widesp read presence in manufacturing systems, distributed welding shop has attracted c onsiderable attention in both industry and academia. In the context of Industry 5.0, the incorporation of human-robot collaboration (HRC) scheduling in distribu ted welding shop can promote system productivity and flexibility. Meanwhile, due to the instability of human performance, employing fuzzy processing time to sim ulate human production more aligns with the practical manufacturing scenario. Th us, this paper investigates a human-robot collaboration energy-efficient distrib uted mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC). This problem mod el can be utilized in many welding manufacturing enterprises with HRC production mode. To solve this problem, we design a self-learning memetic algorithm (SLMA) to minimize both makespan and total energy consumption (TEC). The design of all components in SLMA is based on the characteristics of problem. The SLMA can off er the low-energy and high-efficiency schedules for practitioners."

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Researcher from University of Adelaide Publishes Findings in Machine Learning (D omain Adaptation for Satellite-Borne Multispectral Cloud Detection)

152-153页

查看更多>>摘要：Current study results on artificial in telligence have been published. According to news originating from Adelaide, Aus tralia, by NewsRx correspondents, research stated, "The advent of satellite-born e machine learning hardware accelerators has enabled the onboard processing of p ayload data using machine learning techniques such as convolutional neural netwo rks (CNNs)." Funders for this research include Smartsat Crc. Our news journalists obtained a quote from the research from University of Adela ide: "A notable example is using a CNN to detect the presence of clouds in the m ultispectral data captured on Earth observation (EO) missions, whereby only clea r sky data are downlinked to conserve bandwidth. However, prior to deployment, n ew missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missio ns will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and fu ture missions. In this paper, we address the domain gap problem in the context o f onboard multispectral cloud detection."

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Data on Intelligent Transport Systems Discussed by Researchers at Nottingham Tre nt University [The Accessibility of Public Electric Vehicle ( Ev) Charging Infrastructure: Evidence From the Cities of Nottingham and Frankfur t]

153-154页

查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Transportatio n - Intelligent Transport Systems. According to news reporting originating in No ttingham, United Kingdom, by NewsRx journalists, research stated, "The distribut ion of public electric vehicle (EV) charging infrastructure is a widespread appr oach for promoting EV adoption and decarbonising transportation. A significant a mount of literature explores the distribution of EV charging points at a country scale, but there is a lack of studies focusing on a district scale." Financial supporters for this research include Horizon 2020, European Union (EU) . The news reporters obtained a quote from the research from Nottingham Trent Univ ersity, "This study aims to contribute to this gap by gaining insights into the distribution of EV charging points per district within cities, such as Nottingha m and Frankfurt. The study investigates the current distribution of EV charging points across 38 postcode districts in Frankfurt and 9 postcode districts in Not tingham, using geographical data analysis and a linear regression approach. The following factors in response to the number of EV charging points per postcode d istrict (ZIP code) are examined: the percentage of apartment buildings/floor are a ratio, the availability of amenities, population, charging capacity (kW), area size, strategic approaches, including policy goals and principles. The results reveal disparities in access to EV charging infrastructure across districts and underscore the importance of expanding EV charging networks not only in district s located near urban centres or those with high availability of amenities but al so ensuring that users without home charging options are not left behind."

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Study Findings on Machine Learning Are Outlined in Reports from Chinese Academy of Sciences [Mapping low-lying states and B(E2;01+-> 21+) in even-even nuclei with machine learning]

155-155页

查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting out of Lanzhou, People's Re public of China, by NewsRx editors, research stated, "A machine-learning algorit hm, Light Gradient Boosting Machine, was applied for the first time to investiga te the fundamental experimental observables in even-even nuclei over the Segre c hart." Funders for this research include Chinese Academy of Sciences; Ministry of Scien ce And Technology of The People's Republic of China; National Natural Science Fo undation of China.

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Reports Outline Machine Learning Study Findings from Shanghai Jiao Tong Universi ty (Discovery of a Ni-based Superalloy With Low Thermal Expansion Via Machine Le arning)

156-157页

查看更多>>摘要：New research on Machine Learning is th e subject of a report. According to news reporting originating from Shanghai, Pe ople's Republic of China, by NewsRx correspondents, research stated, "Traditiona l Ni-based superalloys are widely used due to their excellent high-temperature p erformance; however, their high thermal expansion at elevated temperatures limit s their further application in the modern aerospace industry. To discover Ni-bas ed superalloys with low thermal expansion for hightemperature environments, suc h as 900 degrees C. In this study, we developed a Support Vector Regression (SVR ) model with a high coefficient of determination (R2 = 0.84) to predict the coef ficient of thermal expansion (CTE) in Ni-based superalloys."

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Researchers from North Carolina State University (NC State) Describe Findings in Machine Learning (Leveraging Student Planning In Game-based Learning Environmen ts for Self-regulated Learning Analytics)

157-158页

查看更多>>摘要：Investigators publish new report on Ma chine Learning. According to news reporting originating in Raleigh, North Caroli na, by NewsRx journalists, research stated, "The process of setting goals and cr eating plans is crucial for self-regulated learning (SRL), yet students often st ruggle to construct efficient plans and establish goals. Adaptive learning envir onments hold promise for assisting students with such processes through adaptive scaffolding." Financial support for this research came from National Science Foundation (NSF). The news reporters obtained a quote from the research from North Carolina State University (NC State), "Through the examination of data collected from 144 middl e school students, we present a datadriven analysis of students' explicit plann ing activities in Crystal Island, a narrative game-based learning environment. I n this game, students are provided with a planning support tool that aids them i n externalizing their science-related goals and plans before putting them into a ction. We extracted features from their planning tool use and connected them to several SRL processes and problem-solving outcomes. We found that students who e ngaged with the planning support tool were more likely to successfully complete the learning scenario. To investigate the potential for adaptive support with th is tool, we also constructed a student plan recognition framework aimed at predi cting students' goals and planned action sequences. This framework uses student gameplay sequences as input and student interactions with the planning tool as l abels for both prediction tasks. We evaluated these tasks using six machine lear ning models and found that all approaches improved on the majority baseline clas sification performance. We then investigated additional machine-learning archite ctures and a technique for detecting when students enact all steps in their plan s as methods for improving the framework. We demonstrated performance improvemen t with these enhancements."

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Patent Issued for Systems and methods for providing, in programmable motion devi ces, compliant end effectors with noise mitigation (USPTO 12090643)

158-162页

查看更多>>摘要：A patent by the inventors Anderson, Br etton (Westford, MA, US), filed on November 21, 2022, was published online on Se ptember 17, 2024, according to news reporting originating from Alexandria, Virgi nia, by NewsRx correspondents. Patent number 12090643 is assigned to Berkshire Grey Operating Company Inc. (Bed ford, Massachusetts, United States). The following quote was obtained by the news editors from the background informa tion supplied by the inventors: "The invention generally relates to programmable motion systems and relates in particular to end-effectors for programmable moti on devices (e.g., robotic systems) for use in object processing such as object s ortation or order fulfillment.

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