期刊,Robotics & Machine Learning Daily News 2024年卷Mar.8期_国家学术搜索

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Nanjing University of Science and Technology Researchers Publish New Data on Mac hine Learning (Discovery of high energy and stable prismane derivatives by the h igh-throughput computation and machine learning combined strategy)

1-1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 Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "Motivated by the excellent detonation performance of octanitrocubane, prismane is another potential backbo ne with high strain energy in energetic molecule design." Funders for this research include National Natural Science Foundation of China. The news correspondents obtained a quote from the research from Nanjing Universi ty of Science and Technology: "In this work, we aim to screen out candidates of highly energetic molecules from the space of prismane derivatives. The high-thro ughput computation (HTC) is performed based on 200 molecules derived from the mo lecule space of 1503 prismane derivatives with four substituents. Based on the c alculated results, the machine learning (ML) models of density, detonation veloc ity, detonation pressure, heat of formation and detonation heat are established, and thereby the performances of the remaining 1303 samples are predicted. It is found that the -NHNO2 group increases density, while both -NO2 and -C(NO2)3 gro ups promote detonation performances. Based on the detonation velocity and bond d issociation energy as criteria representing energy and molecular stability, four molecules were screened out with good detonation performance and acceptable the rmal stability."

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Reports from Indian Institute of Information Technology Add New Study Findings t o Research in Machine Learning (Machining Process Automation in Computer Numeric al Control Turning Using Robot-Assisted Imaging and CNN-Based Machine Learning)

2-2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 artificial intelligence are presented in a new report. According to news reporting from Chennai, India, by NewsRx journalists, research stated, "With the emergence of the Industrial In ternet of Things and Industry 4.0, industrial automation has grown as an importa nt vertical in recent years. Smart manufacturing techniques are now becoming ess ential to keep up with the global industrial competition." The news reporters obtained a quote from the research from Indian Institute of I nformation Technology: "Decreasing machine's downtime and increasing tool life a re crucial factors in reducing machining process costs. Therefore, introducing c omplete process automation utilizing an intelligent automation system can enhanc e the throughput of manufacturing processes. To achieve this, intelligent manufa cturing systems can be designed to recognize materials they interact with and au tonomously decide what actions to take whenever needed. This paper aims to prese nt a generalized approach for fully automated machining processes to develop an intelligent manufacturing system. As an objective to accomplish this, the presen ce of workpiece material is automatically detected and identified in the propose d system using a convolutional neural network (CNN) based machine learning (ML) algorithm. Furthermore, the computer numerical control (CNC) lathe's machining t oolpath is automatically generated based on workpiece images for a surface finis hing operation. Machining process parameters (spindle speed and feed rate) are a lso autonomously controlled, thus enabling full machining process automation. Th e implemented system introduces cognitive abilities into a machining system, cre ating an intelligent manufacturing ecosystem."

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Studies from James Cook University Yield New Information about Premature Birth ( Machine Learning for Understanding and Predicting Neurodevelopmental Outcomes In Premature Infants: a Systematic Review)

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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 Pregnan cy Complications - Premature Birth. According to news reporting originating in C airns, Australia, by NewsRx journalists, research stated, "Machine learning has been attracting increasing attention for use in healthcare applications, includi ng neonatal medicine. One application for this tool is in understanding and pred icting neurodevelopmental outcomes in preterm infants." Financial support for this research came from CAUL. The news reporters obtained a quote from the research from James Cook University, "In this study, we have carried out a systematic review to identify findings a nd challenges to date. This systematic review was conducted in accordance with t he Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines . Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors. The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural ne tworks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing b rain structure and function. Studies to date also sought to identify features pr edictive of outcomes; however, results varied greatly. Initial studies in this f ield have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes. Impact Th is systematic review looks at the question of whether machine learning can be us ed to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, bu t many challenges and opportunities remain. Quality assessment of relevant artic les is conducted using the Newcastle-Ottawa Scale. This work identifies challeng es that remain and suggests several key directions for future research."

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University of Malta Reports Findings in Artificial Intelligence (Diabetic Foot S creening Guidelines and the Role of Artificial Intelligence: Time to Turn the Ti de!)

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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 reporting originating in Msida, Malta, by NewsRx journalists, research stated, "Despite medical and technologica l advancements, foot amputations continue to rise. Thus, the effort of diabetic foot management should be toward prevention and early diagnosis." The news reporters obtained a quote from the research from the University of Mal ta, "Healthcare professionals need to be trained, equipped, and supported with a dequate resources to be able to identify and deliver appropriate foot care. Ever y effort should be made to minimize the impact of complications and to ensure pr ompt access to care for everyone. Artificial intelligence and smart technology c ould provide a significant opportunity to improve efficiency in diabetes care, w hich may reduce diabetic foot complications. The possible potential of the new t echnologies which are emerging together with their current developing applicatio ns for diabetic foot care are suggested." According to the news reporters, the research concluded: "A call for immediate c hange in diabetes foot screening guidelines is imperative to save limbs and live s." This research has been peer-reviewed.

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Reports from University of Verona Add New Study Findings to Research in Machine Learning (A Machine Learning-Oriented Survey on Tiny Machine Learning)

4-5页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Verona, Italy, by NewsRx editors, research stated, "The emergence of Tiny Machine Learning (TinyM L) has positively revolutionized the field of Artificial Intelligence by promoti ng the joint design of resource-constrained IoT hardware devices and their learn ing-based software architectures." Funders for this research include European Commission. The news editors obtained a quote from the research from University of Verona: " TinyML carries an essential role within the fourth and fifth industrial revoluti ons in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-dat e overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and compl ete literature survey. In particular, firstly, we will examine the three differe nt workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-orien ted, and co-design. Secondly, we propose a taxonomy that covers the learning pan orama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. T hirdly, this survey will present the distinct features of hardware devices and s oftware tools that represent the current state-of-the-art for TinyML intelligent edge applications."

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Findings from Indian Institute of Technology (IIT) Madras Update Understanding o f Fluids Physics (Transition of Edney Shock-shock Interactions Due To the Whippi ng Phenomenon of Liquid Jet In Supersonic Crossflow)

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查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Physics - Flu ids Physics. According to news reporting out of Tamil Nadu, India, by NewsRx edi tors, research stated, "In this paper, we experimentally study the unsteady dyna mics of shock-shock interaction between the bow shock generated by a liquid jet in supersonic crossflow (LJISC) and an oblique shock. Images of shock-shock inte ractions were captured using highspeed focusing schlieren." Financial supporters for this research include Department of Science & Technology (DOST), Philippines, Department of Science & Technology (India), Russian Science Foundation (RSF), National Centre for Combustion Resea rch and Development.

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University Hospital Basel Reports Findings in Artificial Intelligence (Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel art ificial intelligence algorithm)

6-7页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Artificial Intelligence is the su bject of a report. According to news reporting originating in Basel, Switzerland, by NewsRx journalists, research stated, "Multiple smart devices capable of aut omatically detecting atrial fibrillation (AF) based on single-lead electrocardio grams (SL-ECG) are presently available. The rate of inconclusive tracings by man ufacturers' algorithms is currently too high to be clinically useful." The news reporters obtained a quote from the research from University Hospital B asel, "This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart device artificial intelligence (AI)-based algorithm fo r detecting AF from 4 commercially available smart devices (AliveCor KardiaMobil e, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and 4 smart device SL-ECGs. The novel AI algori thm (PulseAI, Belfast, United Kingdom) was compared with each manufacturer's alg orithm. We enrolled 206 patients (31% female, median age 64 years) . AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI algorithm vs manufacturer algorithm were 88% vs 81% ( = .34) and 97% vs 77% (<.001) for the AliveCor KardiaMobile, 86% v s 81% ( = .45) and 95% vs 83% (<.001) for the Apple Watch 6, 91% vs 67% (<.01) and 94% vs 82% (<.001) f or the Fitbit Sense, and 86% vs 82% ( = .63) and 94% vs 80% (<.001) for the Samsung Galaxy Watch3, respectively. In addition, the proportion of SL-ECGs with an inconclusive diag nosis (1.2 %) was significantly lower for all smart devices using th e AI-based algorithm compared to manufacturer's algorithms (14%-17% ), <.001."

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Studies from International University of La Rioja Have Provided New Data on Mach ine Learning (Service Anomaly Detection In Dry Bulk Terminals: a Machine Learnin g Approach)

7-8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 Ma chine Learning. According to news reporting originating in La Rioja, Spain, by N ewsRx editors, the research stated, "Bulk terminals are complex environments due to a number of variables that affect terminal performance. Although the analysi s of big datasets is destined to become an important component of terminal manag ement, previous research has not addressed this issue yet." Financial support for this research came from China Merchants Energy Shipping. The news reporters obtained a quote from the research from the International Uni versity of La Rioja, "This paper aims to shed new light on the operation of dry bulk terminals through a two-stage method based on unsupervised machine learning techniques. The first step gives an overview of the terminal's performance, rev ealing the strongest associations between the variables, while the second calcul ates an anomaly score for each vessel through an optimised implementation of the isolation forest. As a result, we detect anomalous services which could be dire ctly attributable to the terminal operator."

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Reports Summarize Machine Learning Study Results from China University of Petrol eum (East China) (Base On Temporal Convolution and Spatial Convolution Transform er for Fluid Prediction Through Well Logging Data)

8-9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 Machine Learning. According to news reporting originating in Shandong, People's Republi c of China, by NewsRx journalists, research stated, "Fluid prediction is importa nt in exploration work, helping to determine the location of exploration targets and the reserve potential of the estimated area. Machine learning methods can b etter adapt to different data distributions and nonlinear relationships through model training, resulting in better learning of these complex relationships." The news reporters obtained a quote from the research from the China University of Petroleum (East China), "We started by using the convolution operation to pro cess the log data, which includes temporal convolution and spatial convolution. Temporal convolution is specifically designed to capture time series relationshi ps in time series data. In well log data, time information is often critical for understanding fluid changes and other important details. Temporal convolution l earns trends and cyclical changes in the data. The spatial convolution operation makes the model more sensitive to the local features in the logging data throug h the design of the local receptive field and improves the sensitivity to fluid changes. Spatial convolution helps capture spatial correlations at different dep ths or locations. This can help the model understand the change of fluid in the vertical direction and identify the spatial relationship between different fluid s. Then, we use the transformer module to predict the fluid. The transformer mod ule uses a self-attention mechanism that allows the model to focus on informatio n with different weights at different locations in the sequence. In the well log data, this helps the model to better capture the formation characteristics at d ifferent depths or time points and improves the modeling ability of time series information. The fully connected structure in the transformer module enables eac h position to interact directly with other locations in the sequence. By applyin g it to the data of Tarim Oilfield, the experimental results show that the convo lutional transformer model proposed in this paper has better results than other machine learning models."

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New Machine Learning Study Findings Recently Were Reported by Researchers at Zur ich University of Applied Sciences (Identifying Performance Limiting Parameters In Perovskite Solar Cells Using Machine Learning)

9-10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 Ma chine Learning. According to news reporting originating in Winterthur, Switzerla nd, by NewsRx journalists, research stated, "Herein, it is shown that machine le arning (ML) methods can be used to predict the parameter that limits the solar-c ell performance most significantly, solely based on the current density-voltage (J-V) curve under illumination. The data (11'150 J-V curves) to train the model is based on device simulation, where 20 different physical parameters related to charge transport and recombination are varied individually." Financial supporters for this research include Horizon 2020, European Union (EU), ZHAW digital in the framework of a DIZH fellowship. The news reporters obtained a quote from the research from the Zurich University of Applied Sciences, "This approach allows to cover a wide range of effects tha t could occur when varying fabrication conditions or during degradation of a dev ice. Using ML, the simulated J-V curves are classified for the changed parameter with accuracies above 80%, where Random Forests perform best. It t urns out that the key parameters, short-circuit current density, open-circuit vo ltage, maximum power conversion efficiency, and fill factor are sufficient for a ccurate predictions. To show the practical relevance, the ML algorithms are then applied to reported devices, and the results are discussed from a physics persp ective. It is demonstrated that if some specified conditions are met, satisfying results can be reached. The proposed workflow can be used to better understand a device's behavior, e.g., during degradation, or as a guideline to improve its performance without costly and time-consuming lab-based trial-and-error methods. Machine learning (ML) methods are used to predict the most limiting parameter o f perovskite solar cells' performance, solely based on the current-voltage curve . With simulation tools, 20 different physical parameters related to charge tran sport and recombination are varied individually. The simulated current-voltage c urves are classified by ML for the changed parameter, with accuracies above 80% ."

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