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Robotics & Machine Learning Daily News

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    New Machine Learning Findings from Lund University Discussed (Agreements ‘in the Wild’: Standards and Alignment In Machine Learning Benchmark Dataset Constructi on)

    65-66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 Machine Learning is th e subject of a report. According to news reporting out of Lund, Sweden, by NewsR x editors, the research stated, “This article presents an ethnographic case stud y of a corporate-academic group constructing a benchmark dataset of daily activi ties for a variety of machine learning and computer vision tasks. Using a socio- technical perspective, the article conceptualizes the dataset as a knowledge obj ect that is stabilized by both practical standards (for daily activities, datafi cation, annotation and benchmarks) and alignment work - that is, efforts includi ng forging agreements to make these standards effective in practice.”Financial supporters for this research include European Research Council (ERC), STS environment in Sweden. Our news journalists obtained a quote from the research from Lund University, “B y attending to alignment work, the article highlights the informal, communicativ e and supportive efforts that underlie the success of standards and the smoothin g of tensions between actors and factors. Emphasizing these efforts constitutes a contribution in several ways. This article’s ethnographic mode of analysis cha llenges and supplements quantitative metrics on datasets. It advances the field of dataset analysis by offering a detailed empirical examination of the developm ent of a new benchmark dataset as a collective accomplishment. By showing the im portance of alignment efforts and their close ties to standards and their limita tions, it adds to our understanding of how machine learning datasets are built.”
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    Data from Wuhan University of Technology Provide New Insights into Robotics (Yol ov5s-bc: an Improved Yolov5s-based Method for Real-time Apple Detection)

    66-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Robotics have been publi shed. According to news reporting originating in Wuhan, People’s Republic of Chi na, by NewsRx journalists, research stated, “The current apple detection algorit hms fail to accurately differentiate obscured apples from pickable ones, thus le ading to low accuracy in apple harvesting and a high rate of instances where app les are either mispicked or missed altogether. To address the issues associated with the existing algorithms, this study proposes an improved YOLOv5s-based meth od, named YOLOv5s-BC, for real-time apple detection, in which a series of modifi cations have been introduced.” Financial supporters for this research include Wuhan University of Technology, N ational Innovation and Entrepreneurship Training Program for College Students.
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    Recent Research from Northeast Petroleum University Highlight Findings in Machin e Learning (Machine Learning for Optimal Ultrafine Cement Plugging System In Si mulated High Permeability Sandstone Reservoirs)

    67-68页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting from Daqing, People’s Republ ic of China, by NewsRx journalists, research stated, “The properties of a cement system dictate its potential applications, yet creating a reliable cement plugg ing system with controllable setting times, robust injection capacity, and high compressive strength often requires a lot of time and resources in advanced oilf ield development. To address this issue, a machine learningbased XGBoost model was created to optimize plugging system performance, reduce costs, and enhance e fficiency by analysing the initial setting time, final setting time, viscosity, and compressive strength of ultra-fine cement.” Financial supporters for this research include PetroChina Innovation Foundation, Opening Project Foundation of State Key Laboratory of Inorganic Synthesis and P reparative Chemistry of Jilin University.
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    Findings on Machine Learning Discussed by Investigators at University of Manches ter (Material Recognition Using Robotic Hand With Capacitive Tactile Sensor Arra y and Machine Learning)

    68-69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 originating from Manchester, Unite d Kingdom, by NewsRx correspondents, research stated, “Autonomous manipulation u sing robot hands can benefit from tactile sensing, as it can collect information on variations in applied force and surface properties. This article presents a capacitive tactile sensor placed on the robot’s hand fingers.” Financial support for this research came from Beijing Tashan Technology Corporat ion Ltd. Our news editors obtained a quote from the research from the University of Manch ester, “Due to its unique structure and high sensitivity to material permittivit y, this sensing system can obtain capacitive data both when a robot finger is ap proaching an object and when it has touched the object. With threedimensional r eduction methods, that is, principal component analysis (PCA), independent compo nent analysis (ICA), and multidimensional scaling (MDS), a dataset is transforme d to be 2-D and then fed into two supervised classifications algorithms, that is , k-nearest neighbors (KNNs) and support vector machines (SVMs). In comparison t o previous studies, the MDS-based SVM achieves high material recognition accurac y, up to 98% for recognition of three different material classes, that is, plastic, paper, and glass using capacitance data only. Furthermore, it performs well in recognition of five different materials, that is, dry plastic, plastic with water drops, paper, dry glass, and glass with water drops. The reco gnition accuracy is as high as 93%. Computational time can be reduc ed by about 60% by combining the dimension reduction methods with classification algorithms.”
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    New Sepsis Study Findings Recently Were Reported by Researchers at Georgia Insti tute of Technology (Parsimonious Waveformderived Features Consisting of Pulse A rrival Time and Heart Rate Variability Predicts the Onset of Septic Shock)

    69-70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 Blood Disease s and Conditions - Sepsis have been published. According to news reporting origi nating in Atlanta, Georgia, by NewsRx journalists, research stated, “Sepsis is a major public health emergency and one of the leading causes of morbidity and mo rtality in critically ill patients. For each hour treatment is delayed, shock-re lated mortality increases, so early diagnosis and intervention is of utmost impo rtance.” Financial supporters for this research include National Institutes of Health (NI H) - USA, Surgical Critical Care Initiative - Department of Defense’s Health Pro gram-Joint Program Committee 6/Combat Casualty Care.
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    Investigators from China University of Petroleum Have Reported New Data on Machi ne Learning (Interpretable Lost Circulation Analysis : Labeled, Identified, and Analyzed Lost Circulation In Drilling Operations)

    70-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Beijing, Pe ople’s Republic of China, by NewsRx correspondents, research stated, “Lost circu lation (LC) is a serious problem in drilling operations, as it increases nonprod uctive time and costs. It can occur due to various complex factors, such as geol ogical parameters, drilling fluid properties, and operational drilling parameter s, either individually or in combination.” Our news editors obtained a quote from the research from the China University of Petroleum, “Therefore, studying the types, influencing factors, and causes of L C is crucial for effectively improving prevention and plugging techniques. Curre ntly, the expert diagnosis of LC types relies heavily on the experience and judg ment of experts, which may lead to inconsistencies and biases. Additionally, dif ficulties in obtaining data or missing important data can affect the efficiency and timeliness of diagnosis. Traditional physical modeling methods struggle to a nalyze complex factor correlations, and conventional machine learning techniques have limited interpretability. In this paper, we propose an interpretable lost circulation analysis (ILCA) framework that provides a new method for analyzing L C. First, we use Gaussian mixture model (GMM) clustering to analyze the LC chara cteristics of regional case data, efficiently and accurately labeling 296 LC eve nts. Second, we establish the relationship between geological features, drilling fluid properties, operational drilling parameters, and LC types using the XGBoo st algorithm. This enables timely identification of LC types during drilling ope rations using real - time data, with a precision greater than 85 %. Finally, we use interpretable machine learning techniques to conduct a comprehen sive quantitative analysis of influencing factors based on the established XGBoo st model, providing a clear explanation for the identification model. This enabl es drilling engineers to gain deeper insights into the factors influencing LC ev ents. In summary, the proposed ILCA framework is capable of efficiently labeling LC types based on regional case data, identifying LC types in a timely manner u sing real - time data, and conducting quantitative analysis of the factors and c auses of LC.”
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    Wuhan University Reports Findings in Strain Engineering (Interfacial Optimizatio n for AlN/Diamond Heterostructures via Machine Learning Potential Molecular Dyna mics Investigation of the Mechanical Properties)

    71-72页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Engineering - Strain Engineering is the subject of a report. According to news reporting originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research stated, “AlN/diam ond heterostructures hold tremendous promise for the development of next-generat ion highpower electronic devices due to their ultrawide band gaps and other exc eptional properties. However, the poor adhesion at the AlN/diamond interface is a significant challenge that will lead to film delamination and device performan ce degradation.” Our news editors obtained a quote from the research from Wuhan University, “In t his study, the uniaxial tensile failure of the AlN/diamond heterogeneous interfa ces was investigated by molecular dynamics simulations based on a neuroevolution ary machine learning potential (NEP) model. The interatomic interactions can be successfully described by trained NEP, the reliability of which has been demonst rated by the prediction of the cleavage planes of AlN and diamond. It can be rev ealed that the annealing treatment can reduce the total potential energy by enha ncing the binding of the C and N atoms at interfaces. The strain engineering of AlN also has an important impact on the mechanical properties of the interface. Furthermore, the influence of the surface roughness and interfacial nanostructur es on the AlN/diamond heterostructures has been considered.”
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    Research Data from Hubei University of Economics Update Understanding of Machine Learning (Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning Approach)

    72-73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 artific ial intelligence. According to news originating from Wuhan, People’s Republic of China, by NewsRx editors, the research stated, “Creating a walkable environment is an essential step toward the 2030 Sustainable Development Goals.” Funders for this research include Humanities And Social Science Fund of Ministry of Education of China. Our news editors obtained a quote from the research from Hubei University of Eco nomics: “Nevertheless, not all people can enjoy a walkable environment, and neig hborhoods with different socioeconomic status are found to vary greatly with wal kability. Former studies have typically unraveled the relationship between neigh borhood deprivation and walkability from a temporally static perspective and the produced estimations to a point-in-time snapshot were believed to incorporate g reat uncertainties. The ways in which neighborhood walkability changes over time in association with deprivation remain unclear. Using the case of the Hangzhou metropolitan area, we first measured the neighborhood walkability from 2016 to 2 018 by calculating a set of revised walk scores. Further, we applied a machine l earning algorithm, the kernel-based regularized least squares regression in part icular, to unravel how neighborhood walkability changes in relation to deprivati on over time. The results not only capture the nonlinearity in the relationship between neighborhood deprivation and walkability over time, but also highlight t he marginal effects of each neighborhood deprivation indicator.”
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    Researcher from LUT University Details Findings in Androids (Enhancing Safety an d Collaboration in Human Robot Interaction for Industrial Robotics)

    73-74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 androids is the subjec t of a new report. According to news reporting from Lappeenranta, Finland, by Ne wsRx journalists, research stated, “This research evaluates the aspect of collab oration between humans and robots in industrial robotics.” Our news journalists obtained a quote from the research from LUT University: “It highlights the advantages of using robots in non-ergonomic tasks while at the s ame time recognizing that there are challenges preventing them from achieving ma nipulation accuracy with precision. Collaborative robots also known as robots, h ave been proposed to address these limitations. Safety has been identified as on e of the most critical issues in collaborative environments, which calls for a d iscussion on various strategies and practices to ensure the safety of operators. The study explores many facets of human-robot interaction and collaboration suc h as physicality and proximity, house sharing, and collaboration. Furthermore, t his article argues that it is vital to consider human aspects of human-robot int eraction such as trustworthiness, mental effort, and fear. The final part presen ts a case study on incorporation of humans and robots in assembly and sealing pr ocess of refrigerator.”
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    New Robotics Findings Reported from Guangzhou University (Design and Architectur e of a Slender and Flexible Underwater Robot)

    74-74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 Robotics have been published. According to news originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “This paper presents the d esign and analysis of a biomimetic underwater snake-like robot, addressing the m ain limitations of current underwater robotic systems in terms of maneuverabilit y and adaptability in complex environments. The innovative design incorporates f lexible joint modules that significantly enhance the robot’s ability to navigate through narrow and irregular terrains, which is a notable limitation in traditi onal rigidly connected underwater robots.” Financial support for this research came from National key R&D<middle dot>plan.
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