期刊,Robotics & Machine Learning Daily News 2024年卷Oct.31期_国家学术搜索

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Findings from Xiamen University Reveals New Findings on Robotics (Solving Roboti c Trajectory Sequential Writing Problem Via Learning Character's Structural and Sequential Information)

135-136页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Robotics is the subjec t of a report. According to news reportingoriginating from Xiamen, People's Rep ublic of China, by NewsRx correspondents, research stated, "Thewriting sequence of numerals or letters often affects aesthetic aspects of the writing outcomes. As such,it remains a challenge for robotic calligraphy systems to perform, mim icking human writers' implicitintention."Financial supporters for this research include Natural Science Foundation of Fuj ian Province, StrategicPartner Acceleration Award under the Ser Cymru II Progra mme, U.K..Our news editors obtained a quote from the research from Xiamen University, "Thi s article presents anew robot calligraphy system that is able to learn writing sequences with limited sequential information,producing writing results compati ble to human writers with good diversity. In particular, the systeminnovatively applies a gated recurrent unit (GRU) network to generate robotic writing action s with thesupport of a prelabeled trajectory sequence vector. Also, a new evalu ation method is proposed thatconsiders the shape, trajectory sequence, and stru ctural information of the writing outcome, therebyhelping ensure the writing qu ality. A swarm optimization algorithm is exploited to create an optimal setof p arameters of the proposed system. The proposed approach is evaluated using Arabi c numerals, andthe experimental results demonstrate the competitive writing per formance of the system against state-ofthe-art approaches regarding multiple cr iteria (including FID, MAE, PSNR, SSIM, and PerLoss), as wellas diversity perfo rmance concerning variance and entropy."

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Studies from Changshu Institute of Technology Have Provided New Information abou t Robotics (Interest Flooding Suppression-based Routing for Information-centric Internet of Robotic Things)

136-136页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on Ro botics. According to news reporting fromChangshu, People's Republic of China, b y NewsRx journalists, research stated, "With the development ofthe Internet of Things (IoT) and robots, the Internet of Robotic Things (IoRT) becomes possible to enabledata delivery and monitoring for post-disaster areas, where robots are distributed in harsh environmentsto produce real-time data for timely treatmen t. To rapidly access real-time data, we propose an interestflooding suppression -based routing method for information-centric IoRT."The news correspondents obtained a quote from the research from the Changshu Ins titute of Technology,"This method leverages robot resources to elect backbone r obots with sufficient resources toexecute forwarding operations, and integrates provider attributes with information-centric attributes toconstruct real-time routing information on active robot providers and distinguish between negative a ndpositive requests. Negative requests are abandoned to avoid unnecessary flood ing, and positive requestsare forwarded toward active providers in an unicast m anner to achieve data sharing among consumers."

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New Artificial Intelligence Findings from Financial University Published (Applic ation of a Model Life Cycle Concept to Investments in Artificial Intelligence Ev aluation on the Example of Large Language Models)

137-137页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on ar tificial intelligence. According to news reportingfrom Financial University by NewsRx journalists, research stated, "The life cycle of an artificial intelligence model is the object of research. The purpose of the study is to develop a mod el life-cycle methodologythat describes the economic content of the investment process in artificial intelligence technology."Our news editors obtained a quote from the research from Financial University: " During the study, bothgeneral scientific methods such as analysis, synthesis, c omparison, abstraction, induction and deductionwere used, as well as project me thodologies of the life-cycle, employed as the basis for the value creationlife -cycle of the model. The analysis was based on identifying the necessary stages of model developmentin terms of the CRISP-DM methodology and determining the fe atures of each of them in terms of cashflows. Modified versions of the model li fe-cycle containing risk assessment, including model risk, were alsotaken into account. In the process of research, the proposed generalized model life-cycle m ethodologywas specified for a specific AI technology - large language models. A s a result of the study, the authorproposed a three-stage model. The possible o ptionality between the stages and the characteristics of cashflows are describe d. It was concluded that an investment project for the development of AI contain sseveral real options - abandonment, reduction, expansion and replacement. For large language models,the life cycle structure and possible optionalities are p reserved. The peculiarity is that the value creationprocess involves cash flows from different areas of application of the model in business processes."

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New Machine Learning Findings Reported from Guangdong University of Technology ( Machine Learning Insights Into the Evolution of Flood Resilience: a Synthesized Framework Study)

138-138页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on Ma chine Learning. According to news reportingfrom Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "Enhancing urbanresilience repr esented a viable strategy to mitigate flooding induced by intense human activiti es andclimate change. However, existing studies often concentrated on system at tributes or isolated resiliencecharacteristics, failing to offer a holistic eva luation of urban flood resilience performance."Funders for this research include National Natural Science Foundation of China ( NSFC), Program forGuangdong Intro-ducing Innovative and Enterpreneurial Teams.The news correspondents obtained a quote from the research from the Guangdong Un iversity of Technology,"Thus, it was imperative to develop a comprehensive floo d resilience framework that incorporatedthe resilience evolution process includ ing resistance, economic and function recovery. Consequently, thisstudy endeavo red to devise a synthesized framework for evaluating urban flood resilience, sub sequentlyemploying a Convolutional Neural Network (CNN) model for simulation. T he findings indicated that: (1)Guangzhou's maximum resistance capacity diminish ed from 0.52 to 0.50 as rainfall return periods altered,while Dongguan exhibite d the lowest resistance, decreasing from 0.42 to 0.40. Regarding functional recovery capacity, Guangzhou ranked highest (0.35) and Foshan lowest (0.19); (2) acc ording to TriangularFuzzy Number-based AHP (TFN-AHP) analysis, the area classif ied as highest in resilience decreased from15.6% to 12.1% of the total, whereas the low resilience area increased from 7.6% to 8.7%; (3) Zhuhaiand Zhaoqing were primarily clustered along the resistance axis, in contrast, Dongguan was distinguishedby its advancement alo ng the axis of functional recovery.(4) CNN simulations yielded precise outcomes,with the Area Under the Receiver Operating Characteristic Curve (AUC) and predi ctive accuracy (ACC)values exceeding 0.8,respectively."

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New Robotics Findings from Lanzhou Jiaotong University Described (Research On th e Prediction of Motion Trajectory and Precise Control Method of Bionic Robotic F ish Based On Lssvr Interactive Network)

139-139页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on Ro botics. According to news reporting out ofGansu, People's Republic of China, by NewsRx editors, research stated, "The inaccuracy of the multi-finssynergy hydr odynamic model of the robotic fish and the lack of clarity between the control p arameters ofthe locomotion gait and swimming behavior of the robotic fish were addressed. We constructed a bionicrobotic fish pectoral fins and flexible body synergy motion gait model by using a Central Pattern Generator(CPG) network."Funders for this research include National Natural Science Foundation of China ( NSFC), Higher EducationalInstitutions Industrial Support Program of Gansu Provi nce, State Key Laboratory for StructuralAnalysis of Industrial Equipment Open F und Project, National Defense Basic Scientific Research programof China, Culti- vation Fund for Civil-Military Integration Innovation Team of Lanzhou Jiaotong U niversity.

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Reports from Sudha Rustagi College of Dental Sciences and Research Advance Knowl edge in Artificial Intelligence (Exploring the Landscape of Artificial Intellige nce Acceptance among Health-care Professionals: A Questionnaire Survey)

140-140页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on artificial intell igence are discussed in a new report. Accordingto news reporting out of Haryana , India, by NewsRx editors, research stated, "Artificial intelligence (AI)could be understood as the technology that mimics the human knowledge based solely on input data. Inrecent years, AI has become the talk of town and is being applie d to health care more and more."The news journalists obtained a quote from the research from Sudha Rustagi Colle ge of DentalSciences and Research: "The emergence of AI in health care has been groundbreaking, reshaping the wayof diagnosis, treatment, and monitoring of pa tients. There are many instances where AI can performwell in the field of healt h care. Hence, the objective of this study was to assess the knowledge, perspective, familiarity/attitude, practice, and barriers in the acceptance of AI of hea lth-care professionals. A 42-question survey was distributed in the form of Goog le survey tool containing seven sections: demographics,knowledge, practice, fam iliarity, perspective, acceptance, and barriers to AI use in health care. It wasdistributed through professional networks, organizations, and relevant online c ommunities. A total of 327health-care professionals (209 females, 115 males, an d 3 others) participated in the survey. Clinicians were217 (66.4%) , whereas 57 (17.4%) and 53 (16.2%) were undergraduate s and postgraduates, respectively,belonging to medicine (17.4%), d ental (76.8%), and allied sciences (5.8%). According t o 249 (76.1%)participants, they did not use AI-based tools in thei r practice. More than half of the study participantsagreed that AI can be effec tively and efficiently used in health-care delivery. Majority of the participant sagreed that AI-based tool's ability to reveal early disease risks (27.2% ) and help in surgical intervention(13.1%) were main advantages of AI. Among all the disadvantages, high investment costs (50.8%) andregulatory concerns (38.8%) were the major barriers in adoption of AI in health care."

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Findings from Chengdu University of Technology in Machine Learning Reported (Mac hine Learning-based Classification of Quality Grades for Concrete Vibration Beha viour)

141-141页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Researchers detail new data in Machine Learning. According to news reporting originatingfrom Chengdu, People's Republ ic of China, by NewsRx correspondents, research stated, "Thevibration behaviour of the vibrating rods is one of the key factors for the quality of concrete vib ration thatis essential for the long-term safety of concrete structures. Althou gh many vibration operation regulationshave been widely applied, evaluating met hod for concrete vibration samples is relatively rare."Funders for this research include National Natural Science Foundation of China ( NSFC), SichuanScience and Technology Program, Basic and Applied Basic Research Foundation of Guangdong Province,Guangdong Provincial Science and Technology Sp ecial Fund Project, Shantou University (STU) ScientificResearch Initiation Grant.

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New Findings from Stanford University in the Area of Machine Learning Reported ( A Physics-informed Machine Learning Model for the Prediction of Drop Breakup In Two-phase Flows)

142-142页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Data detailed on Machine Learning have been presented. According to news originatingfrom Stanford, California, by New sRx correspondents, research stated, "Predictive simulations of two-phaseflows are highly sought after because of their widespread applications in propulsion, energy, agriculture,and medicine. One crucial goal for many of these simulation s is the accurate and efficient prediction ofthe size distribution and number d ensity of atomized drops."Financial support for this research came from Advanced Simulation and Computing (ASC) programof the US Department of Energy's National Nuclear Security Adminis tration (NNSA) via the PSAAP-IIICenter at Stanford University.Our news journalists obtained a quote from the research from Stanford University , "The multi-scalenature of these flows makes it practically impossible to capt ure all scales within a single simulation. Inparticular, the breakup processes producing the smallest drops through secondary breakup often necessitateresolut ions far below the Kolmogorov scale. Consequently, models must be employed for s econdarybreakup. Existing physics- based and stochastic breakup models are not universal and fail to account forthe local and instantaneous flow field and dro p geometry. We present a physics-informed machine learningmodel for predicting the statistics of daughter drops generated during the breakup of under-resolved drops.By training on high-fidelity simulations, the model can predict breakup o utcomes from severely underresolvedinput fields. This is made possible by a ca reful choice of quantities of interest and by takinginspiration from the discre te nature of breakup events to encode the temporal evolution via a mixtureof si gmoid functions. We showcase proof-of-concept results from the canonical setting s of 3D Taylor-Green vortex flows and homogeneous isotropic turbulence. Compared to results generated by low-resolutionsimulations (i.e., without a model) and baseline state-of-the-art models, our approach achieves superioraccuracy in pre dicting drop size distribution and critical quantities of interest, such as surf ace area."

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New Robotics Findings from Tianjin University Described (A Showdown In the Kitch en: Exploring Consumers ‘ Preferences for Robotmade Versus Human-made Foods At Different Stages of Dietary Restraint)

143-144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 reporting originating from Tianjin, People's Republic of China, by NewsRx correspondents, research stated, "This research examines how consumers' preferences for robot-made versus human-made foods vary at different stages ofdietary restraint."Funders for this research include National Natural Science Foundation of China ( NSFC), TianjinMunicipal Education Commission.Our news editors obtained a quote from the research from Tianjin University, "Ac ross four studies,the authors demonstrate that consumers in the early stage of dietary restraint are more willing to purchasehuman-made foods, whereas those i n the later stages of dietary restraint are more willing to purchaserobot-made foods. These effects were mediated by calorie estimate and taste perception."

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Studies from National University of Trujillo Further Understanding of Robotics ( Automatic differential kinematics of serial manipulator robots through dual numb ers)

143-143页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Current study results on robotics have been published. According to news reporting fromNational University of Trujill o by NewsRx journalists, research stated, "Dual Numbers are an extension ofreal numbers known for its capability of performing exact automatic differentiation of one-valued functionstheoretically without error approximation."Our news reporters obtained a quote from the research from National University o f Trujillo: "Also,Differential Kinematics of robots involves the computation of the Jacobian, which is a matrix of partialderivatives of the Forward Kinematic equations with respect to the robot's joints. Thus, to perform theautomatic ca lculation of the Jacobian matrix, this paper presents an extension of dual numbe rs composedof a scalar real part and a vector dual part, where the real part re presents the angular value of the robotjoint, and the dual part represents the direction of the corresponding partial derivative for each joint.The presented method was implemented in Matlab through Object Orientes Programming (OOP), andthe results for calculating the Jacobian of the KUKA KR 500 robot model for 1000 random postures weresubsequently compared in terms of execution time and Mean Squared Error (MSE) with other conventionalmethods: the geometric method, the s ymbolic method, and the finite difference method."

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