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    New Findings from Changchun University of Technology in the Area of Robotics Des cribed (A Jellyfish Robot Based On Two-bar and Four-spring Tensegrity Structures )

    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 Ro botics. According to news originating from Changchun, People’s Republic of China , by NewsRx correspondents, research stated, “Traditional underwater robots prim arily use propellers, which have the disadvantages of producing considerable noi se and having a large impact on the marine environment. In this study, we design a jellyfish robot based on the tensegrity structure, and the properties of the tensegrity structure are used to enable the underwater robot to move like a jell yfish.”Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Jilin Provincial Sci- ence and Technology Development Plan P roject.

    Research on Robotics Detailed by a Researcher at Konkuk University (Simulation a nd Controller Design for a Fish Robot with Control Fins)

    10-11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 have been pr esented. According to news originating from Seoul, South Korea, by NewsRx corres pondents, research stated, “In this paper, a nonlinear simulation block for a fi sh robot was designed using MATLAB Simulink. The simulation block incorporated a dded masses, hydrodynamic damping forces, restoring forces, and forces and momen ts due to dorsal fins, pectoral fins, and caudal fins into six-degree-of-freedom equations of motion.” Funders for this research include Korea Research Institute For Defense Technolog y Planning And Advancement.

    New Findings from Tsinghua University in the Area of Robotics Described (A study on the dynamics of a novel seven degrees of freedom spray-painting robot with a telescopic forearm)

    11-12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. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “In this article, the dynamics of a seve n degrees of freedom robot with a telescopic forearm for the spray-painting of l arge workpiece is investigated.” The news reporters obtained a quote from the research from Tsinghua University: “A novel two-step inverse kinematic solving method that combines numerical and a nalytical approaches is proposed to solve the inverse kinematics of the seven de grees of freedom robot with two redundant degrees of freedom for spray-painting applications. Based on the kinematic model, the dynamic model is derived using t he Newton-Euler method. For the dynamic parameter identification, the dynamic mo del is written in a linear form with respect to dynamic parameters. Considering the effect of the telescopic forearm on the dynamics of the robot, two novel per formance indices concerning the inertial load and gravitational load in the join t space are proposed to evaluate the effect of the telescopic forearm on the dyn amic load of the robot.”

    Findings from Shandong University Broaden Understanding of Robotics (Determinist ic Learning-based Neural Pid Control for Nonlinear Robotic Systems)

    12-13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 from Jinan, People’s Repu blic of China, by NewsRx correspondents, research stated, “Traditional proportio nal-integral-derivative (PID) controllers have achieved widespread success in in dustrial applications. However, the nonlinearity and uncertainty of practical sy stems cannot be ignored, even though most of the existing research on PID contro llers is focused on linear systems.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).

    Researchers at Imperial College London Release New Data on Robotics (Pats-wheel: a Passively-transformable Single-part Wheel for Mobile Robot Navigation On Unst ructured Terrain)

    13-14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 London, United Kingdom, by NewsRx editors , research stated, “Most mobile robots use wheels that perform well on even and structured ground, like in factories and warehouses. However, they face challeng es traversing unstructured terrain such as stepped obstacles.” Financial support for this research came from Natural Intelligence for Robotic M onitoring of Habitats.

    New Findings Reported from Beijing Institute of Technology Describe Advances in Machine Learning (Machine-learning-inspired Quantum Control In Many-body Dynamic s)

    14-15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 from Beijing, People’s Republ ic of China, by NewsRx journalists, research stated, “Achieving precise preparat ion of quantum many-body states is crucial for the practical implementation of q uantum computation and quantum simulation. However, the inherent challenges pose d by unavoidable excitations at critical points during quench processes necessit ate careful design of control fields.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Key Research and Development Program of China.

    Findings from Hubei University of Education in Apoptosis Reported [Prediction of Apoptosis Signal-regulating Kinase 1 (Ask1) Inhibition With Machin e Learning Methods]

    15-16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 Cellular Phys iology - Apoptosis have been published. According to news reporting originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research state d, “Apoptosis signal-regulated kinase 1 (ASK1) has recently confirmed as an attr active therapeutic target for drug discovery. The development of small molecule inhibitors of ASK1 has attracted increasing attention.” Funders for this research include Hubei Provincial Department of Education, Scie ntific Research Project of Hubei Provincial Department of Education, Scientific Research Foundation of Hubei University of Education for Talent Introduction.

    Researchers from Texas A&M University Report Findings in Machine Le arning (Interpretable Machine Learning for Predicting Urban Flash Flood Hotspots Using Intertwined Land and Builtenvironment Features)

    16-17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 from College Station, Te xas, by NewsRx correspondents, research stated, “Pluvial flash floods are fast-m oving hazards and causes significant disruptions in urban areas. With the increa se in heavy precipitations, the ability to proactively identify flash floods hot spots in cities is critical for flood nowcasting and predictive monitoring of ri sks.” Financial support for this research came from National Science Foundation (NSF). Our news editors obtained a quote from the research from Texas A&M University, “While rainfall runoff models and hydrologic models are useful model s for flash flood prediction, these models are computationally expensive and eff ort intensive to be used for flood nowcasting. To address this challenge, this s tudy presents interpretable machine learning models for predicting urban flash f lood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification pro blem, and three recent flash flood events in U.S. cities are selected for data c ollection and model validation. Various features related to land and built envir onment characteristics are constructed using diverse datasets, and the occurrenc es of flash floods are captured using crowdsource data from the events. Using th ese features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the m odels can achieve good accuracy (0.8) in identifying flooded/non-flooded locatio ns. Especially, the models can achieve high true positive rate (0.83-0.89) and l ow missing rate, demonstrating the methods’ practicability for accurately predic ting flooded hotspots. The model interpretation results indicate that land featu res related to hydrological and topological features have greater impacts on fla sh flood risk, than built environment features. Further analysis reveals that th e feature importance, model performance, and model transferability performance v ary among cities and localized specifications of the models are needed for accur ate prediction of flash flood for a particular city.”

    New Machine Learning Study Findings Have Been Reported by a Researcher at State University of New York (SUNY) Buffalo (The Sociodemographic Biases in Machine Le arning Algorithms: A Biomedical Informatics Perspective)

    17-18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 new report. According to news reporting out of Buffalo, New York, by NewsRx editors, research stated, “Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guid e clinical and other health care decisions.” Funders for this research include Nih Nlm. Our news editors obtained a quote from the research from State University of New York (SUNY) Buffalo: “Machine learning algorithms, however, may house biases th at propagate stereotypes, inequities, and discrimination that contribute to soci oeconomic health care disparities. The biases include those related to some soci odemographic characteristics such as race, ethnicity, gender, age, insurance, an d socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in larg e language models pose potential drawbacks. These biases affect the lives and li velihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash canno t be underestimated.”

    Medical University of Vienna Reports Findings in Robotics (Excitation of natural spinal reflex loops in the sensory-motor control of hand prostheses)

    18-19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 from Vienna, Austria, by NewsRx journ alists, research stated, “Sensory feedback for prosthesis control is typically b ased on encoding sensory information in specific types of sensory stimuli that t he users interpret to adjust the control of the prosthesis. However, in physiolo gical conditions, the afferent feedback received from peripheral nerves is not o nly processed consciously but also modulates spinal reflex loops that contribute to the neural information driving muscles.” The news correspondents obtained a quote from the research from the Medical Univ ersity of Vienna, “Spinal pathways are relevant for sensory-motor integration, b ut they are commonly not leveraged for prosthesis control. We propose an approac h to improve sensory-motor integration for prosthesis control based on modulatin g the excitability of spinal circuits through the vibration of tendons in a clos ed loop with muscle activity. We measured muscle signals in healthy participants and amputees during different motor tasks, and we closed the loop by applying v ibration on tendons connected to the muscles, which modulated the excitability o f motor neurons. The control signals to the prosthesis were thus the combination of voluntary control and additional spinal reflex inputs induced by tendon vibr ation. Results showed that closed-loop tendon vibration was able to modulate the neural drive to the muscles. When closed-loop tendon vibration was used, partic ipants could achieve similar or better control performance in interfaces using m uscle activation than without stimulation. Stimulation could even improve prosth etic grasping in amputees.”