首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Data on Computational Intelligence Detailed by Researchers at National Universit y of Defense Technology (Multi-ship Dynamic Weapon-target Assignment Via Coopera tive Distributional Reinforcement Learning With Dynamic Reward)

    21-21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 - Computational Intelligence. According to news originating from Chang sha, People’s Republic of China, by NewsRx correspondents, research stated, “In fleet air defense, the efficient coordination of multiple ships to complete weap on-target assignment has always been a critical challenge, primarily due to the varying combat capabilities and duties associated with each ship. Consequently, the traditional ‘weapon-target’ assignment mode has turned into a ‘ship-weapon-t arget’ assignment mode in the multi-ship dynamic weapon-target assignment (MS-DW TA) problem we proposed, with a larger solution space.” Our news journalists obtained a quote from the research from the National Univer sity of Defense Technology, “In this problem, different ships possess distinct a ttributes, such as defense duties, weapon types, and loaded missile quantities. To solve this problem, we proposed an Attention enhanced multiagent Distributio nal reinforcement learning method with Dynamic Reward (ADDR). Different from sta ndard reinforcement learning method, ADDR learns to estimate the distribution, a s opposed to only the expectation of future return, enabling better adaptation t o air defense scenarios with significant randomness. The multi-head attention ne twork integrates both the ship situation and the target situation to appropriate ly adjust the output of each agent, which explicitly considers the agent-level i mpact of ships to the whole fleet. Moreover, due to the missile fight time, ship s may not immediately receive rewards after executing actions. To address this d elayed phenomenon, we designed a dynamic reward mechanism to accurately adjust t he delayed rewards.”
      原文链接:
    • NETL
    • NSTL

    Study Findings from Minneapolis Heart Institute Provide New Insights into Artifi cial Intelligence (Interventional Cardiologists’ Perspectives Andknowledge Towar ds Artificial Intelligence)

    22-22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 Minneapolis, Minnesota, by NewsRx journalists, research stated, “Artificial intelligence (AI) is increas ingly utilized in interventional cardiology (IC) and holds the potential torevol utionize the field. We conducted a global, web-based, anonymous survey of IC fel lows and attendings to assess the knowledge andperceptions of interventional car diologists regarding AI use in IC.” The news correspondents obtained a quote from the research from Minneapolis Hear t Institute, “A total of 521 interventional cardiologists participated in the su rvey. The median age range of participants was 36 to45 years, most (51.5% ) practice in the United States, and 7.5% were women. Most (84.7% ) could explain well or somehowknew what AI is about, and 63.7% we re optimistic/very optimistic about AI in IC. However, 73.5% belie ved that physiciansknow too little about AI to use it on patients and most (46.1 %) agreed that training will be necessary. Only 22.1% werecurrently implementing AI in their personal clinical practice, while 60.6% estimated implementation of AI in their practiceduring the next 5 years. Most ag reed that AI will increase diagnostic efficiency, diagnostic accuracy, treatment selection, andhealthcare expenditure, and decrease medical errors. The most tri ed AI-powered tools were image analysis (57.3%), ECGanalysis (61.7% ), and AI-powered algorithms (45.9%). Interventional cardiologists practicing in academic hospitals weremore likely to have AI tools currently impl emented in their clinical practice and to use them, women had a higher likelihoo d ofexpressing concerns regarding AI, and younger interventional cardiologists w ere more optimistic about AI integration in IC.”
      原文链接:
    • NETL
    • NSTL

    Study Data from China Academy of Engineering Physics Update Knowledge of Robotic s (Review of Flexible Robotic Grippers, with a Focus on Grippers Based on Magnet orheological Materials)

    23-23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 robotics have bee n published. According to news reporting from Mianyang, People’s Republic of Chi na, by NewsRx journalists, research stated, “Flexible grippers are a promising a nd pivotal technology for robotic grasping and manipulation tasks.” Funders for this research include National Natural Science Foundations of China.
      原文链接:
    • NETL
    • NSTL

    University of Lisbon Researcher Provides New Insights into Machine Learning (Pre dicting Fractional Shrub Cover in Heterogeneous Mediterranean Landscapes Using M achine Learning and Sentinel-2 Imagery)

    24-24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 artificial intelligence is now available. According to news originating from Lisbon, Portugal, by NewsR x editors, the research stated, “Wildfires pose a growing threat to Mediterranea n ecosystems. This study employs advanced classification techniques for shrub fr actional cover mapping from satellite imagery in a fire-prone landscape in Quint a da Franca (QF), Portugal.” Funders for this research include Silvanus Project; Fct/mctes.
      原文链接:
    • NETL
    • NSTL

    Capital Medical University Reports Findings in Bioinformatics (Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays)

    25-25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 Biotechnology - Bioinf ormatics is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Septic patient s with T2DM were prone to prolonged recovery and unfavorable prognoses. Thus, th is study aimed to pinpoint potential genes related to sepsis with T2DM and devel op a predictive model for the disease.” Our news journalists obtained a quote from the research from Capital Medical Uni versity, “The candidate genes were screened using protein-protein interaction ne tworks (PPI) and machine learning algorithms. The nomogram and receiver operatin g characteristic curve were developed to assess the diagnostic efficiency of the biomarkers. The relationship between sepsis and immune cells was analyzed using the CIBERSORT algorithm. The biomarkers were validated by qPCR and western blot ting in basic experiments, and differences in organ damage in mice were studied. Three genes (MMP8, CD177, and S100A12) were identified using PPI and machine le arning algorithms, demonstrating strong predictive capabilities. These biomarker s presented significant differences in gene expression patterns between diseased and healthy conditions. Additionally, the expression levels of biomarkers in mo use models and blood samples were consistent with the findings of the bioinforma tics analysis. The study elucidated the common molecular mechanisms associated w ith the pathogenesis of T2DM and sepsis and developed a gene signature-based pre diction model for sepsis.”
      原文链接:
    • NETL
    • NSTL

    New Findings from University of Hull in the Area of Machine Learning Reported (O perando Study of the Dynamic Evolution of Multiple Fe-rich Intermetallics of an Al Recycled Alloy In Solidification By Synchrotron X-ray and Machine Learning)

    26-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Machine Learning have been prese nted. According to news reporting out of Kingston upon Hull, United Kingdom, by NewsRx editors, research stated, “Using synchrotron X-ray diffraction, tomograph y and machine-learning enabled phase segmentation strategy, we have studied unde r operando conditions the nucleation, co-growth and dynamic interplays among the dendritic and multiple intermetallic phases of a typical recycled Al alloy (Al5 Cu1.5Fe1Si, wt.%) in solidification with and without ultrasound. Th e research has revealed and elucidated the underlying mechanisms that drive the formation of the very complex and convoluted Fe-rich phases with rhombic dodecah edron and 3D skeleton networks (the so-called Chinese-script type morphology).” Funders for this research include Engineering & Physical Sciences Research Council (EPSRC), National Natural Science Foundation of China (NSFC), Y unnan International Cooperation Base in Cloud Computation for Non-ferrous Metal Processing, University of Hull, China Scholarship Council.
      原文链接:
    • NETL
    • NSTL

    Reports Summarize Artificial Intelligence Findings from National Institute of Te chnology Warangal (Artificial Intelligence-based Forecasting of Dual-fuel Mode C i Engine Behaviors Powered With the Hydrogen-diesel Blends)

    27-27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 reporting out of Telangana, Indi a, by NewsRx editors, research stated, “The vehicle sector has seen an upsurge i n energy demand due to population expansion. Due to the escalating costs and exh austion of fossil fuels, researchers are now dedicating more of their endeavors to exploring other options.” Our news journalists obtained a quote from the research from the National Instit ute of Technology Warangal, “The objective of this investigation, which is being conducted on a single-cylinder DI diesel engine propelled by hydrogen and diese l, is to optimize engine load. We evaluated the investigation on hydrogen/diesel composites against unadulterated diesel (D100). Different variations of fuels w ith varying percentages of hydrogen supplementation, viz., DH5 (diesel and 5% hydrogen), DH10 (diesel and 10% hydrogen), and DH15 (diesel and 15 % hydrogen), were analyzed. In the experimental investigation, we used an injection pressure of more than 220 bar at 18:1 compression ratio for lo ad optimization, and we used a single fuel injector with a diameter of 0.25 mm, The results showd improved brake thermal efficiency of 1.8% for DH 5, 5.2% for DH10, and 17.6% for DH15, along with a d rop in fuel consumption of 1.7% for DH5, 14.5% for D H10, and 31.6% for DH15. In addition, the addition of hydrogen to diesel demonstrates a promising reduction in smoke emissions of 10.5% , carbon monoxide (CO), and hydrocarbon (HC) emissions by DH15 under full load c onditions. The results were subjected to regression analysis using an artificial intelligence network (ANN), to enhance the performance and reduce emissions of fuel mixtures. The ANN proves to be a very good method for the regression of dat a and prediction.”
      原文链接:
    • NETL
    • NSTL

    Recent Findings in Machine Learning Described by Researchers from Sapienza Unive rsity of Rome (Interpretable Machine Learning Models for Displacement Demand Pre diction In Reinforced Concrete Buildings Under Pulse-like Earthquakes)

    28-28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 originating from Rome, Italy, by NewsRx corr espondents, research stated, “This work proposes a novel procedure to guide the development of machine learning models for estimating the seismic demand in exis ting reinforced concrete (RC) buildings. The proposed approach is organized acro ss two scales.” Financial supporters for this research include Sapienza University of Rome, Euro pean Union - NextGeneration EU.
      原文链接:
    • NETL
    • NSTL

    Michigan State University Reports Findings in Machine Learning (Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusi ng for Lipids)

    29-29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 East Lansing, Michigan , by NewsRx editors, research stated, “Accurate elucidation of gas-phase chemica l structures using collision cross section (CCS) values obtained from ion-mobili ty mass spectrometry benefits from a synergism between experimental and results. We have shown in recent work that for a molecule of modest size with a proscrib ed conformational space we can successfully capture a conformation(s) that can m atch experimental CCS values.” Our news journalists obtained a quote from the research from Michigan State Univ ersity, “However, for flexible systems such as fatty acids that have many rotata ble bonds and multiple intramolecular London dispersion interactions, it becomes necessary to sample a much greater conformational space. Sampling more conforme rs, however, accrues significant computational cost downstream in optimization s teps involving quantum mechanics. To reduce this computational expense for lipid s, we have developed a novel machine learning (ML) model to facilitate conformer filtering according to the estimated gasphase CCS values. Herein we report tha t the implementation of our CCS knowledge-based approach for conformational samp ling resulted in improved structure prediction agreement with experiment by achi eving favorable average CCS prediction errors of 2% for lipid syst ems in both the validation set and the test set. Moreover, most of the gas-phase candidate conformations obtained by using CCS focusing achieved lower energy-mi nimum geometries than the candidate conformations without focusing. Altogether, the implementation of this ML model into our modeling workflow has proven to be beneficial for both the quality of the results and the turnaround time.”
      原文链接:
    • NETL
    • NSTL

    Studies from Guangxi University Provide New Data on Machine Learning (Machine Le arning-Enhanced ORB Matching Using EfficientPS for Error Reduction)

    30-30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 originating from Guangxi, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “The study c omes up with a new way to improve the accuracy of image matching in binocular vi sion systems, especially those that are mounted on vehicles. It combines machine learning with the ORB (Oriented FAST and Rotated BRIEF) image-matching algorith ms.” Our news journalists obtained a quote from the research from Guangxi University: “Standard ORB matching frequently encounters mismatches in complex and repetiti ve environments. To minimize false positives in matches, our strategy utilizes t he EfficientPS (Efficient Panoptic Segmentations) algorithm, a panoramic segment ation technique that uses machine learning in conjunction with ORB. The procedur e begins with the EfficientPS approach, which delivers fine-grained and efficien t segmentation of images, assigning semantic category labels and unique identifi ers to each pixel. The ORB feature point matching process is refined using seman tic data to filter out mismatches between foreground objects and the background effectively. This machine-learning-augmented method significantly decreases the frequency of erroneous matches in intricate settings. Empirical findings from th e KITTI dataset demonstrate that in non-targeted environments, the accuracy of o ur proposed method (0.978) is marginally less than that of LoFTR (0.983). Still, it surpasses other methods when utilizing 50 ORB parameters. In more intricate situations, such as multi-target scenarios with an increased number of ORB param eters (200), our method maintains a high level of accuracy (0.883), outperformin g the conventional ORB (0.732) and rivaling the performance of DL-BDLMR (0.790) and ORB-MFD-FPMC (0.835). Our method’s processing time is competitive and slight ly higher than the standard ORB, but it improves accuracy. In scenarios without targets and with single targets, our method’s processing time (0.195 seconds and 0.211 seconds, respectively) is greater than that of ORB and ORB-MFD-FPMC. Yet, it is significantly lower than that of LoFTR.”
      原文链接:
    • NETL
    • NSTL