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    Researchers at Stanford University Report New Data on Artificial Intelligence (F ull-colour 3d Holographic Augmented-reality Displays With Metasurface Waveguides )

    76-77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 Artificial In telligence have been published. According to news originating from Stanford, Cal ifornia, by NewsRx correspondents, research stated, “Emerging spatial computing systems seamlessly superimpose digital information on the physical environment o bserved by a user, enabling transformative experiences across various domains, s uch as entertainment, education, communication and training 1-3. However, the wi despread adoption of augmented-reality (AR) displays has been limited due to the bulky projection optics of their light engines and their inability to accuratel y portray three-dimensional (3D) depth cues for virtual content, among other fac tors 4,5.” Funders for this research include Stanford Graduate Fellowship in Science and En gineering, National Research Council for Economics, Humanities & S ocial Sciences, Republic of Korea, Kwanjeong Scholarship, Meta Research PhD Fell owship, National Science Foundation (NSF), ARO, Samsung, Sony Research Award Pro gram, Stanford Nanofabrication Facility (SNF), National Science Foundation (NSF) , National Nanotechnology Coordinated Infrastructure.

    Findings from Vrije Universiteit Brussel (VUB) Yields New Data on Robotics (Real -time Constraint-based Planning and Control of Robotic Manipulators for Safe Hum an-robot Collaboration)

    77-78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 Robotics is now availab le. According to news reporting out of Brussels, Belgium, by NewsRx editors, res earch stated, “A recent trend in industrial robotics is to have robotic manipula tors working side-by-side with human operators. A challenging aspect of this coe xistence is that the robot is required to reliably solve complex path-planning p roblems in a dynamically changing environment.” Our news journalists obtained a quote from the research from Vrije Universiteit Brussel (VUB), “To ensure the safety of the human operator while simultaneously achieving efficient task realization, this paper introduces a computationally ef ficient planning and control architecture that combines a Rapidly-exploring Rand om Tree (RRT) path planner with a trajectory-based Explicit Reference Governor ( ERG) by means of a reference selector. The resulting scheme can steer the robot arm to the desired end-effector pose in the presence of actuator saturation, lim ited joint ranges, speed limits, a cluttered static obstacle environment, and mo ving human collaborators. The effectiveness of the proposed framework is experim entally validated on the Franka Emika Panda robot arm and fed with feedback info rmation from state-of-the-art depth perception systems. Our method outperforms b oth the standalone RRT and ERG algorithms in cluttered static environments where it overcomes: i) the RRT’s inability to handle dynamic constraints which result in constraint violations and ii) the ERG’s undesirable property of getting trap ped in local minima.”

    Findings from Georgia Technical Research Institute Provides New Data about Machi ne Learning (Identifying Cislunar Orbital Families Via Machine Learning On Light Curves)

    78-79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 Atlanta, Georgia, by NewsRx journalists, research stated, “Current methods of performing Initial Orbi t Determination (IOD) in near-earth orbital regions cannot be directly extended to cislunar space due to changes in gravitational models that must be utilized. For the case of cislunar orbits, the Moon’s gravitational influence necessitates that orbital motions be described by three-body dynamics.” Funders for this research include Research Institute, Georgia Institute of Techn ology, GTRI’s Independent Research and Development (IRAD) funds.

    Study Results from Jiangxi University of Finance and Economics Update Understand ing of Computational Intelligence (Population Aging, Housing Price, and Househol d Consumption)

    80-81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 computational intelligence have been published. According to news originating from Jiangxi, P eople’s Republic of China, by NewsRx correspondents, research stated, “Populatio n aging and housing price can affect household consumption, and population aging can indirectly affect household consumption through housing price.” Financial supporters for this research include National Social Science Fund of C hina; Jiangxi Provincial Department of Education Science And Technology.

    Researchers from Czech Technical University Describe Findings in Robotics and Au tomation (Fast Swarming of Uavs In Gnss-denied Feature-poor Environments Without Explicit Communication)

    81-82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 Robotics - Robotics a nd Automation have been presented. According to news originating from Prague, Cz ech Republic, by NewsRx correspondents, research stated, “A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in f eature-poor environments without any external localization and communication is introduced in this letter. A novel model of a UAV neighborhood is proposed to ac hieve robust onboard mutual perception and flocking state feedback control, whic h is designed to decrease the inter-agent oscillations common in standard reacti ve swarm models employed in fast collective motion.” Financial support for this research came from CTU. Our news journalists obtained a quote from the research from Czech Technical Uni versity, “The novel swarming methodology is supplemented with an enhanced Multi- Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MR SE and the neighborhood model may rely on information exchange between agents, w e introduce a communication-less version of the swarming framework based on esti mating communicated states to decrease dependence on the often unreliable commun ication networks of large swarms.”

    Investigators from University of Minnesota Zero in on Machine Learning (Computat ionally Efficient Solution of Mixed Integer Model Predictive Control Problems Vi a Machine Learning Aided Benders Decomposition)

    82-83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 originating from Minneapolis, Minnesota, by Ne wsRx correspondents, research stated, “Mixed integer Model Predictive Control (M PC) problems arise in the operation of systems where discrete and continuous dec isions must be taken simultaneously to compensate for disturbances. The efficien t solution of mixed integer MPC problems requires the computationally efficient online solution of mixed integer optimization problems, which are generally diff icult to solve.” Financial support for this research came from National Science Foundation (NSF).

    Study Results from Dhirubhai Ambani Institute of Information and Communication T echnology Provide New Insights into Robotics (Collaborative Dispersion By Silent Robots)

    83-84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 Robotics are disc ussed in a new report. According to news reporting out of Gujarat, India, by New sRx editors, research stated, “In the dispersion problem, a set of k co -located mobile robots must relocate themselves in distinct nodes of an unknown network. The network is modeled as an anonymous graph G = (V, E), where the graph’s node s are not labeled.” Funders for this research include Science and Engineering Research Board (SERB) , Department of Science and Technology, Govt. of India, Science Engineering Rese arch Board (SERB), India, Research Initiation Grant - IIT Bhilai, India.

    Findings from King’s College London in Robotics and Automation Reported (Univers al Actuation Module and Kinematic Model for Heart Valve Interventional Catheter Robotization)

    87-88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 Robotics - Robotics a nd Automation have been presented. According to news reporting out of London, Un ited Kingdom, by NewsRx editors, research stated, “Catheters have been widely us ed to deal with heart valve diseases. However, the diversity in handle structure s and bending curvatures imposes significant complexities in safe delivery and p ositioning.” Financial support for this research came from China Scholarship Council. Our news journalists obtained a quote from the research from King’s College Lond on, “In this letter, we designed a module for single knob actuation assembled co axially on the catheter handle, composed of a chuck for universal clamping of di ameters from 15 to 45 mm and a position-adjustable shaft to accommodate various spacing between knobs. In addition, we proposed a two-curvature with pseudo join ts (TC-PJ) model for bending control of bendable sections (BSs) in catheters. Th e verification was decoupled into two steps based on the other three deformation patterns. Firstly, comparing the two-curvature (TC) model with pseudo-rigid-bod y (PRB), constant curvature (CC), and Euler spiral (ES) models to simulate plana r bending and elongation, the results showed a more accurate shape representatio n. Then, five distinct catheters were employed to test the clamping universality of the module and tip positioning precision of the TC-PJ model which took torsi on and shear strain into consideration. The rootmean- square error (RMSE) and th e standard deviation (SD) of tip position and direction were analysed. Results i ndicated the module’s suitability for clamping these catheters, with the large g uide sheath exhibiting minimal position RMSE (SD) of around 0.10 (0.051) mm and 0.049 (2.15) degrees, while the puncture catheter demonstrated the highest posit ion and direction RMSE (SD) extending to about 1.16 (0.53) mm and 0.70 (31.33) d egrees, primarily attributed to the coupling of two sequential bendable componen ts.”

    Ulm University Reports Findings in Delirium [Introducing a ma chine learning algorithm for delirium prediction-the Supporting SURgery with GEr iatric Co-Management and AI project (SURGE-Ahead)]

    88-89页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Nervous System Diseases and Condi tions - Delirium is the subject of a report. According to news reporting origina ting from Ulm, Germany, by NewsRx correspondents, research stated, “Post-operati ve delirium (POD) is a common complication in older patients, with an incidence of 14-56 %. To implement preventative procedures, it is necessary to identify patients at risk for POD.” Financial support for this research came from German Federal Ministry of Educati on and Research. Our news editors obtained a quote from the research from Ulm University, “In the present study, we aimed to develop a machine learning (ML) model for POD predic tion in older patients, in close cooperation with the PAWEL (patient safety, cos t-effectiveness and quality of life in elective surgery) project. The model was trained on the PAWEL study’s dataset of 878 patients (no intervention, age 70, 2 09 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical c onsiderations and a recursive feature elimination. A logistic regression and a l inear support vector machine (SVM) were trained, and evaluated using receiver op erator characteristics (ROC). The selected features were American Society of Ane sthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular f iltration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cogni tive assessment subscores ‘memory’, ‘orientation’ and ‘verbal fluency’, pre-exis ting dementia, clinical frailty scale, age, recent falls, post-operative isolati on and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78-0.85 ] in the training set, 0.81 [95% CI 0.71-0.88] in the test set and 0.76 [95 % CI 0.71-0.79] in a cross-centre validation. W e present a clinically useful and explainable ML model for POD prediction.” According to the news editors, the research concluded: “The model will be deploy ed in the Supporting SURgery with GEriatric Co-Management and AI project.”

    Findings in the Area of Artificial Intelligence Reported from University of Shef field (Ai-based Optimisation of Total Machining Performance: a Review)

    89-90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 originating in Sheffi eld, United Kingdom, by NewsRx journalists, research stated, “Advanced modelling and optimisation techniques have been widely used in recent years to enable int elligent manufacturing and digitalisation of manufacturing processes. In this co ntext, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components.” The news reporters obtained a quote from the research from the University of She ffield, “Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered an d implemented. The state-of-the-art in application of various optimisation and a rtificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researche d operation and implemented machine learning technique, respectively. The surfac e roughness turns out to be the most critical quality measure considered. The di fferent optimisation targets in artificial intelligence applications are elabora ted and analysed to highlight the need for a holistic approach that covers all c ritical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper.”