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 Artificial Intelligence. According to news reporting originating in Rennes, Fran ce, by NewsRx journalists, research stated, "Automobile traffic accidents repres ent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable art ificial intelligence (XAI) artifact design, integrating accident data for utiliz ation by diverse stakeholders and decision-makers." The news reporters obtained a quote from the research from the Rennes School of Business, "It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associa ted with varying injury severity levels, thereby contributing theoretically to e xplainable analytics. In the initial phase, we employed various advanced techniq ues such as data missing at random (MAR) with Bayesian dynamic conditional imput ation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHa pley Additive exPlanations (SHAP) for determining and analyzing the importance a nd dependence of risk factors on injury severity. Additionally, exploratory feat ure analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive m odels in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the S HAP approach, we employed model-agnostic interpretation techniques to separate e xplanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories."
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 reporting from Rapid City, South Dakota, by NewsRx journalists, research stated, "Characterizing defects in 2D materials, s uch as cracks in chemical vapor deposited (CVD)-grown hexagonal boron nitride (h BN), is essential for evaluating material quality and reliability." Financial supporters for this research include National Science Foundation (Nsf) Rii Fec Awards; Nsf Cbet Award; National Institute of General Medical Sciences of The National Institutes of Health. The news journalists obtained a quote from the research from South Dakota School of Mines and Technology: "Traditional characterization methods are often time-c onsuming and subjective and can be hindered by the limited optical contrast of h BN. To address this, we utilized a YOLOv8n deep learning model for automated cra ck detection in transferred CVD-grown hBN films, using MATLAB's Image Labeler an d Supervisely for meticulous annotation and training. The model demonstrates pro mising crack-detection capabilities, accurately identifying cracks of varying si zes and complexities, with loss curve analysis revealing progressive learning."
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 from Urbana, Illinois, by NewsRx journali sts, research stated, "Building energy efficiency has been a cornerstone of gree nhouse gas mitigation strategies for decades. However, impact evaluations have r evealed that energy savings typically fall short of engineering model forecasts that currently guide funding decisions." Financial supporters for this research include Alfred P. Sloan Foundation, Illin ois Department of Commerce and Economic Opportunity's Illinois Home Weatherizati on Assistance Program, European Research Council (ERC). The news correspondents obtained a quote from the research from the University o f Illinois, "This creates a resource allocation problem that impedes progress on climate change. Using data from the Illinois implementation of the U.S.'s large st energy efficiency program, we demonstrate that a data -driven approach to pre dicting retrofit impacts based on previously realized outcomes is more accurate than the status quo engineering models."
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 reporting originating in Xi'an, People's Repu blic of China, by NewsRx journalists, research stated, "Due to continuous high d ynamic load of industrial robots, rotate vector (RV) reducers may be more prone to wear and pitting and there will be abnormal vibration and sound caused by inc ipient faults. However, many experienced experts can identify the health state o f machines by the sensitivity of their hearing to some special sounds." Financial support for this research came from National Key Research and Developm ent Program of China. The news reporters obtained a quote from the research from Xi'an Jiaotong Univer sity, "To simulate human hearing and visualize the sound heard by experts when R V reducer is running, Mel-filter bank is designed and Melspectrograms are introd uced to show the sound energy distribution of RV reducers under various conditio ns. A spliced feature is constructed to describe the texture and boundary of the energy distribution. Moreover, a sound-vibration fusion approach is proposed to fuse the spliced features of sound and vibration for accurately identifying of RV reducer health states. Single-joint RV reducers experiments are performed to obtain sound and vibration data sets under different health states. The identifi cation results of 50 runs show that the average accuracy of the proposed sound-v ibration spectrogram fusion method is 93.83 %, greatly preferable t o traditional methods."
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 originating in Fujian, People's Republic of China, by NewsRx journalists, research stated, "The wave of Industry 4.0 and ca rbon neutrality is propelling the global manufacturing towards intelligent and s ustainable transformation. While existing research fails to adequately evaluate the effects of industrial robots (IR) on firm energy intensity from a microscopi c perspective, this gap obstructs our understanding of green intelligent manufac turing." Financial support for this research came from Key Projects of Philosophy and Soc ial Sciences Research, Ministry of Education.
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 from Lecce, Italy, by NewsRx journalists, research stated, "PurposeUsing AI to strengthen creativity a nd problem-solving capabilities of professionals involved in innovation manageme nt holds huge potential for improving organizational decision-making. However, t here is a lack of research on the use of AI technologies by innovation managers. "
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published. According to news reporting from Athens, Georgia, by NewsRx journa lists, research stated, "The traditional asphalt mix design requires the prepara tion of many samples to test, which consumes much time and labor. Moreover, sele cting aggregate gradation and asphalt content based on individual experience unt il a mixture's properties meet a specification is a trial-and-error procedure." Financial support for this research came from Center for Integrated Asset Manage ment for Multimodal Transportation Infrastructure Systems (CIAMTIS), a US Depart ment of Transportation University Transportation Center, United States.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news originating from Taiyuan, People's Republic of China, by NewsRx correspondents, research stated, "Inversion of magnetic basement interfac es in basins is essential for interpreting potential field data and studying geo thermal resource distribution, as well as basin formation and evolution. This pa per introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processi ng and machine learning techniques." Financial support for this research came from Shanxi Institute of Geological Sur vey CO., LTD.. Our news journalists obtained a quote from the research from the Taiyuan Univers ity of Technology, "The method creates magnetic base interface models and corres ponding magnetic anomaly data through the random midpoint displacement method an d magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical cont inuation, spatial derivatives, and fractional transformations. Feature attribute s are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analys is of both idealized and noisy models. The proposed machine learning-based appro ach is more intelligent, efficient, and accurately represents the relief of magn etic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural inform ation, paving the way for further research in magnetic interface inversion."
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 H ESAM University by NewsRx correspondents, research stated, "The aim of this arti cle is to study the degradation of a composite material under static pressure." Our news editors obtained a quote from the research from HESAM University: "The high pressure condition is similar to the one encountered inside hydrogen tanks. Damage modeling was used to evaluate the behavior of hydrogen tanks to high pre ssure. A practical approach, coupling a finite element method (FEM) simulation a nd machine learning (ML) algorithm, is suggested. The representative volume elem ent (RVE) was used in association with a choice of a behavior law and a damage l aw as an input data. Algorithms for ML classification such as K-nearest neighbor s (k-NN) and a special k-NN with a dynamic time warping metric were used."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on are discussed in a new report. According to news reporting from Nanjing University of Science and Technology by NewsRx journalists, research stated, "Effective fault diagnosis a nd fault-tolerant control method for aeronautics electromechanical actuator is c oncerned in this paper." Financial supporters for this research include National Natural Science Foundati on of China; Open Fund of Aerospace Servo Drive And Transmission Technology Labo ratory of China; Fundamental Research Funds For The Central Universities of Chin a.
NETL
NSTL