期刊,Robotics & Machine Learning Daily News 2024年卷Mar.12期_国家学术搜索

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Data on Support Vector Machines Reported by a Researcher at Shenyang University of Chemical Technology (Intelligent Optimization Algorithm for Support Vector Ma chine:Research and Analysis of Prediction Ability)

1-1页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on have been published.Acc ording to news reporting originating from Shenyang University of Chemical Techno logy by NewsRx correspondents,research stated,"Support vector machine is a very classical and popular model for data prediction." Financial supporters for this research include Doctoral Start-up Foundation of L iaoning Province.Our news correspondents obtained a quote from the research from Shenyang Univers ity of Chemical Technology:"Traditional support vector machines use grid search to determine its parameters.In order to improve the accuracy of prediction,mo re and more frameworks are proposed.Among them,the combination of support vect or machine and intelligent optimization algorithm is the most commonly used solu tion at present.The optimization objective is to determine the optimal penalty factor and kernel parameters of support vector machine to improve the prediction performance.In this paper,10 intelligent optimization algorithms that are wid ely used at present are used for the optimization research of support vector mac hine.The performance of these optimization algorithms in support vector machine parameter optimization is analyzed in detail."

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University Hospital Schleswig-Holstein Reports Findings in Artificial Intelligen ce (Opening Pandora's box by generating ICU diaries through artificial intellige nce:A hypothetical study protocol)

2-2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 Lubeck,Germany,by NewsRx editors,research stated,"Patients and families on Intensive Care Units (ICU) benefit from ICU diaries,enhancing their coping and understanding of their experiences.Staff shortages and a limited amount of time severely restri ct the application of ICU diaries." Our news journalists obtained a quote from the research from University Hospital Schleswig-Holstein,"To counteract this limitation,generating diary entries fr om medical and nursing records using an artificial intelligence (AI) might be a solution.Protocol for a hypothetical multi-center,mixed method study to identi fy the usability and impact of AI-generated ICU diaries,compared with hand-writ ten diaries.A hand-written ICU diary will be written for patients with expected length of stay 72 h by trained nursing staff and families.Additionally at disc harge,the medical and nursing records are analyzed by an AI software,transform ed into understandable,empathic diary entries,and printed as diary.Based on a n appointment with patients within 3 months,diaries are read in randomized order by trained clinicians with the patients and families.Patients and families wi ll be interviewed about their experiences of reading both diaries.In addition,usability of diaries will be evaluated by a questionnaire.Patients and families describe the similarities and differences of language and the content of the di fferent diaries.In addition,concerns can be expressed about the generation and data processing by AI.Professional nursing involves empathic communication,pa tient-centered care,and evidence-based interventions.Diaries,beneficial for I CU patients and families,could potentially be generated by Artificial Intellige nce,raising ethical and professional considerations about AI's role in compleme nting or substituting nurses in diary writing.Generating AI-based entries for ICU diaries is feasible,but raises serious questions about nursing ethics,empat hy,data protection,and values of professional nurses."

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Study Findings from New Mexico State University Broaden Understanding of Robotics (Combined Soft Grasping and Crawling Locomotor Robot for Exterior Navigation o f Tubular Structures)

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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-Data detailed on robotics have been pr esented.According to news originating from Las Cruces,New Mexico,by NewsRx correspondents,research stated,"This paper presents the design,development,and testing of a robot that combines soft-body grasping and crawling locomotion to navigate tubular objects." Financial supporters for this research include National Science Foundation.Our news journalists obtained a quote from the research from New Mexico State Un iversity:"Inspired by the natural snakes' climbing locomotion of tubular object s,the soft robot includes proximal and distal modules with radial expansion/con traction for grasping around the objects and a longitudinal contractileexpandab le driving module in-between for providing a bi-directional crawling movement al ong the length of the object.The robot's grasping modules are made of fabrics,and the crawling module is made of an extensible pneumatic soft actuator (ePSA).Conceptual designs and CAD models of the robot parts,textile-based inflatable structures,and pneumatic driving mechanisms were developed.The mechanical parts were fabricated using advanced and conventional manufacturing techniques.An A rduino-based electro-pneumatic control board was developed for generating cyclic patterns of grasping and locomotion.Different reinforcing patterns and materia ls characterize the locomotor actuators' dynamical responses to the varying inpu t pressures."

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Research Conducted at U.S.Geological Survey (USGS) Has Provided New Information about Machine Learning (Mlaapde:a Machine Learning Dataset for Determining Glo bal Earthquake Source Parameters)

4-4页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report.According to news reporting from Golden,Colorado,by NewsRx journalists,research stated,"The Machine Learning Asset Aggregatio n of the Preliminary Determination of Epicenters (MLAAPDE) dataset is a labeled waveform archive designed to enable rapid development of machine learning (ML) m odels used in seismic monitoring operations.MLAAPDE consists of more than 5.1 m illion recordings of 120 s long three-component broadband waveform data (raw cou nts) for P,Pn,Pg,S,Sn,and Sg arrivals." The news correspondents obtained a quote from the research from U.S.Geological Survey (USGS),"The labeled catalog is collected from the U.S.Geological Survey National Earthquake Information Center's (NEIC) Preliminary Determination of Ep icenters bulletin,which includes local to teleseismic observations for earthqua kes similar to M 2.5 and larger.Each arrival in the labeled dataset has been ma nually reviewed by NEIC staff.An accompanying Python module enables users to de velop customized training datasets,which includes different time series lengths,distance ranges,sampling rates,and/or phase lists.MLAAPDE is distinct from other publicly available datasets in containing local (14%),region al (36%),and teleseismic (50%) observations,in which local,regional,and teleseismic distance are 0 degrees-3 degrees,3 degrees-30 degrees,and 30 degrees+,respectively.A recent version of the dataset is publ icly available (see Data and Resources),and user-specific versions can be gener ated locally with the accompanying software."

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University of British Columbia Reports Findings in Acute Myeloid Leukemia (MAGIC-DR:An interpretable machine-learning guided approach for acute myeloid leukemi a measurable residual disease analysis)

5-6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 Oncology - Acute Myelo id Leukemia is the subject of a report.According to news reporting originating from Vancouver,Canada,by NewsRx correspondents,research stated,"Multiparamet er flow cytometry is widely used for acute myeloid leukemia minimal residual dis ease testing (AML MRD) but is time consuming and demands substantial expertise.Machine learning offers potential advancements in accuracy and efficiency,but h as yet to be widely adopted for this application." Financial support for this research came from Faculty of Medicine,University of British Columbia.Our news editors obtained a quote from the research from the University of Briti sh Columbia,"To explore this,we trained single cell XGBoost classifiers from 9 8 diagnostic AML cell populations and 30 MRD negative samples.Performance was a ssessed by cross-validation.Predictions were integrated with UMAP as a heatmap parameter for an augmented/interactive AML MRD analysis framework,which was ben chmarked against traditional MRD analysis for 25 test cases.The results showed that XGBoost achieved a median AUC of 0.97,effectively distinguishing diverse A ML cell populations from normal cells.When integrated with UMAP,the classifier s highlighted MRD populations against the background of normal events.Our pipel ine,MAGIC-DR,incorporated classifier predictions and UMAP into flow cytometry standard (FCS) files.This enabled a human-in-the-loop machine learning guided M RD workflow.Validation against conventional analysis for 25 MRD samples showed 100% concordance in myeloid blast detection,with MAGIC-DR also id entifying several immature monocytic populations not readily found by convention al analysis."

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Researchers at Shandong Jiaotong University Report New Data on Robotics (Planning and Execution of Dynamic Whole-body Locomotion for a Wheeled Biped Robot On Un even Terrain)

6-6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 Robotics is the subjec t of a report.According to news reporting originating in Jinan,People's Republ ic of China,by NewsRx journalists,research stated,"To improve the adaptabilit y of the wheeled biped robot (WBR) to uneven terrain,firstly an integrated mode ling method for wheeled-legs is proposed.The under-actuated part is effectively restrained by defining the interaction force between the WBR and the trunk." Funders for this research include National Natural Science Foundation of China ( NSFC),National Key Research and Development Program of China.The news reporters obtained a quote from the research from Shandong Jiaotong Uni versity,"The mapping relationship between the wheeled leg's end force and the j oint torques in the balanced state is built.Based on this premise,a control fr amework that does not rely on external sensors is proposed,and the trunk pose i s used as the task space to plan the generalized force output of the wheeled leg s and calculate the joint torques.Since the joint space position is not constra ined,the leg wheels will be based on the terrain conditions and are adaptively stretched and adjusted back and forth.To further improve the terrain adaptabili ty,a slope estimator and a stabilizer are constructed to deal with the attitude fluctuation caused by the sudden change of terrain."

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New Robotics and Automation Findings from Swiss Federal Institute of Technology Discussed (Dynablox:Real-time Detection of Diverse Dynamic Objects In Complex E nvironments)

7-7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 Robotics - Robotics and Automation.According to news reporting out of Zurich,S witzerland,by NewsRx editors,research stated,"Real-time detection of moving o bjects is an essential capability for robots acting autonomously in dynamic envi ronments.We thus propose Dynablox,a novel online mapping-based approach for ro bust moving object detection in complex unstructured environments." Funders for this research include Microsoft,Swiss National Science Foundation ( SNSF),Wallenberg Foundation,WASP Postdoctoral Scholarship,Swiss National Scie nce Foundation (SNSF).Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Technology,"The central idea of our approach is to incrementally es timate high confidence free-space areas by modeling and accounting for sensing,state estimation,and mapping limitations during online robot operation.The spa tio-temporally conservative free space estimate enables robust detection of movi ng objects without making any assumptions on the appearance of objects or enviro nments.This allows deployment in complex scenes such as multi-storied buildings or staircases,and for diverse moving objects such as people carrying various i tems,doors swinging or even balls rolling around.We thoroughly evaluate our ap proach on real-world data sets,achieving 86% IoU at 17 FPS in typ ical robotic settings.The method outperforms a recent appearance-based classifi er and approaches the performance of offline methods.We demonstrate its general ity on a novel data set with rare moving objects in complex environments."

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Findings from Tongji University Yields New Data on Robotics (Adaptive Approximat ion Tracking Control of a Continuum Robot With Uncertainty Disturbances)

8-8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 reporting originating in Shanghai,People's R epublic of China,by NewsRx journalists,research stated,"Continuum robot has c ertain compliance and intrinsic safety,which makes it an excellent substitute f or the traditional rigid robot in the various tasks,such as human-robot interac tion or medical surgery.However,due to the complex nonlinearities which are in duced by the compliance,parameter uncertainties,and unknown disturbances,good performance control scheme for the continuum robot is always a challenging task for the practical applications." Financial support for this research came from National Natural Science Foundatio n of China (NSFC).The news reporters obtained a quote from the research from Tongji University,"T hus,to overcome this challenge of the uncertain dynamics and unknown external d isturbances,this article develops a novel adaptive control scheme for a continu um robot using the function approximation technique (FAT).Specifically,for the proposed continuum robot,an adaptive FAT control (AFATC) strategy with no upda te laws is proposed to handle the uncertain parameters of the robot dynamics and external disturbances.The control law is expressed as a finite linear combinat ion of the orthogonal basis functions by the FAT.The proposed AFATC scheme uses a fixed control structure,and the weight matrices are not updated in time.The n,the stability of the proposed controller is proved based on the Lyapunov func tion.Afterwards,the simulation results indicate the proposed AFATC scheme has good control performance compared with the regressor-free adaptive control (RFAC ) method."

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University of Aizu Researcher Details Findings in Robotics (Enhanced Robot Motio n Block of A-Star Algorithm for Robotic Path Planning)

9-9页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on robotics have been published .According to news reporting originating from the University of Aizu by NewsRx correspondents,research stated,"An optimized robot path-planning algorithm is required for various aspects of robot movements in applications.The efficacy of the robot path-planning model is vulnerable to the number of search nodes,path cost,and time complexity." Our news reporters obtained a quote from the research from University of Aizu:" The conventional A-star (A*) algorithm outperforms other grid-based algorithms b ecause of its heuristic approach.However,the performance of the conventional A * algorithm is suboptimal for the time,space,and number of search nodes,depen ding on the robot motion block (RMB).To address these challenges,this paper pr oposes an optimal RMB with an adaptive cost function to improve performance.The proposed adaptive cost function keeps track of the goal node and adaptively cal culates the movement costs for quickly arriving at the goal node.Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes,which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity.To validate the performance and robustness of the proposed model,an extensive e xperiment was conducted.In the experiment,an open-source dataset featuring var ious types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes."

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Study Findings from University of Malaysia Pahang Broaden Understanding of Artif icial Intelligence (Advances In Materials Informatics:a Review)

10-10页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Artificial Intelligen ce have been presented.According to news reporting originating from Kuantan,Ma laysia,by NewsRx correspondents,research stated,"Materials informatics (MI) i s aimed to accelerate the materials discovery using computational intelligence a nd data science.Progress of MI depends on the strength of database and artifici al intelligence protocols comprising machine learning (ML) and deep learning (DL ) frameworks." Financial supporters for this research include Universiti Malaysia Pahang,Resea rch and Innovation Department of Universiti Malaysia Pahang through the First-in -the-world grant.Our news editors obtained a quote from the research from the University of Malay sia Pahang,"Conventional ML models are simple and interpretable,relying on sta tistical techniques and algorithms to learn patterns and make predictions with l imited data.Conversely,DL,an advancement of ML,employs mathematical neural n etworks to automatically extract features and handle intricate data at the cost of data size and computational complexity.This work aims to provide a state-of- the-art understanding of the tools,data sources and techniques used in MI and t heir benefits and challenges.We evaluate the growth of MI through its subfields and track the main path of its advancement for artificial intelligence-driven m aterials discovery.The advancements in computational intelligence via machine l earning and deep learning algorithms in different fields of materials science ar e discussed.As a specific example,understanding of materials properties using microstructural images is reviewed."

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