首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Findings on Robotics Detailed by Investigators at Nanyang Technological Universi ty (Safe Human Dual-robot Interaction Based On Control Barrier Functions and Coo peration Functions)

    39-39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 reportingout of Singapore, Singapore, by New sRx editors, research stated, "Nowadays, humans are allowed to workside-by-side with a robot team, which consists of dual robots in most cases. To ensure human safety, themotion of each robot should be reactive to and compliant with human s via human-in-the-loop control."Financial support for this research came from National Research Foundation, Sing apore, under theNRF Medium Sized Centre scheme (CARTIN).Our news journalists obtained a quote from the research from Nanyang Technologic al University,"Furthermore, when the robots conduct a cooperative task, the rea ctive and compliant motion of eachrobot must fulfill the constraints imposed by the cooperation with the other robot. It is challenging toguarantee human safe ty and robot cooperation simultaneously, especially in a decentralized architecture. This letter presents a decentralized control framework that guarantees both human safety and robotcooperation in human dual-robot interaction. First, the high-order time-varying control barrier functions(HO-TV-CBFs) are defined to re present human safety, based on which a safety control set is formulatedto guara ntee human safety. Second, the cooperation functions are introduced to abstract the cooperationconstraints of different cooperative tasks of dual robots, which are further guaranteed by a cooperationcontrol set. Then, a centralized contro l framework based on the safety and cooperation control setsis constructed. Fin ally, the centralized control framework is decentralized while maintaining the s ameperformance."
      原文链接:
    • NETL
    • NSTL

    Data on Machine Learning Reported by Researchers at Beijing Jiaotong University (Machine Learning-based Multi-objective Optimization and Physical-geometrical Co mpetitive Mechanisms for 3d Woven Thermal Protection Composites)

    45-45页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting out of Beijing, Peopl e's Republic of China, by NewsRx editors, research stated, "3D wovencomposite m aterials are prime candidates for thermal protection due to their significant th ermophysicalproperties, which necessitates accurate prediction of these propert ies and precise meso-structural design.This study introduces a fusion framework that integrates machine learning-based sensitivity analysis andmulti-objective optimization design."Funders for this research include National Key R & D Program of Ch ina, National Natural ScienceFoundation of China (NSFC).
      原文链接:
    • NETL
    • NSTL

    Medical University of Silesia Reports Findings in Artificial Intelligence (An in vestigative analysis - ChatGPT's capability to excel in the Polish speciality ex am in pathology)

    46-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According tonews reporting out of Katowice, Polan d, by NewsRx editors, research stated, "This study evaluatesthe effectiveness o f the ChatGPT-3.5 language model in providing correct answers to pathomorphologyquestions as required by the State Speciality Examination (PES). Artificial int elligence (AI) in medicine isgenerating increasing interest, but its potential needs thorough evaluation."Our news journalists obtained a quote from the research from the Medical Univers ity of Silesia,"A set of 119 exam questions by type and subtype were used, whic h were posed to the ChatGPT-3.5 model. Performance was analysed with regard to t he success rate in different question categoriesand subtypes. ChatGPT-3.5 achie ved a performance of 45.38%, which is significantly below the minimum PES pass threshold. The results achieved varied by question type and subtype, with better resultsin questions requiring ‘comprehension and critical thinking ' than ‘memory'. The analysis shows that,although ChatGPT-3.5 can be a useful t eaching tool, its performance in providing correct answers topathomorphology qu estions is significantly lower than that of human respondents. This conclusion highlights the need to further improve the AI model, taking into account the spec ificities of the medicalfield."
      原文链接:
    • NETL
    • NSTL

    Nankai University Researcher Provides New Insights into Machine Learning (Machin e Learning Models for Predicting Bioavailability of Traditional and Emerging Aro matic Contaminants in Plant Roots)

    47-48页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-A new study on artificial intelligence is now available. According to news originatingfrom Tianjin, People's Republic of China, by NewsRx correspondents, research stated, "To predict thebehavior o f aromatic contaminants (ACs) in complex soil-plant systems, this study develope d machine learning (ML) models to estimate the root concentration factor (RCF) o f both traditional (e.g., polycyclicaromatic hydrocarbons, polychlorinated biph enyls) and emerging ACs (e.g., phthalate acid esters, arylorganophosphate ester s)."Funders for this research include The Major Scientific And Technological Innovat ion Project of ShandongProvince.
      原文链接:
    • NETL
    • NSTL

    Findings from Southwest Jiaotong University Reveals New Findings on Machine Lear ning (Tftsvm: Near Color Recognition of Polishing Red Lead Via Svm Based On Thre shold and Feature Transform)

    49-50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 reportingout of Chengdu, People's Republic o f China, by NewsRx editors, research stated, "With the extensiveapplication ofm achine vision in themanufacturing industry, target region recognition in complex industrialscenes is becoming a vital research territory. In the automatic poli shing of molds, polishing red lead, asan auxiliary tool for polishing positioni ng, can intuitively determine the areas to be polished."Financial support for this research came from Sichuan Science and Technology Pro gram.Our news journalists obtained a quote from the research from Southwest Jiaotong University, "Its brightcolor information are very suitable for vision-based rec ognition. Due to the interference of the near color inthe polishing environment , the traditional color recognition method has the appearance of over-segmentation. In this paper, we propose a novel near-color recognition method via SVM base d on threshold andfeature transform (TFTSVM) to improve the identification accu racy of polishing red lead. Specifically, thismethod adopts a threshold-based c olor recognition algorithm to extract two kinds of color features of redlead co lor and its near color in HSV color space and skillfully finds it is distinguish able in three dimensions.To reduce the computational complexity, a machine lear ning segmentation model is constructed, whichrealizes dimension reduction by in tegrating the feature transformation method of sample transformationand project ion transformation to achieve the best segmentation effect. Experimental results on selfestablisheddataset demonstrate that the proposed method has an excelle nt identification effect on thered lead area in the field polishing environment and also shows good robustness under the condition thatthere are reflections o n the mold surface. It meets the requirements of mechanical arm polishing and im proves the safety and reliability of automatic polishing. In addition, we also c ompare different machinelearning algorithms and advanced studies to verify the correctness of the algorithm."
      原文链接:
    • NETL
    • NSTL

    COMSATS University Islamabad Researchers Add New Data to Research in Machine Lea rning (Active-Darknet: An Iterative Learning Approach for Darknet Traffic Detect ion and Categorization)

    51-52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 reportingoriginating from Islamabad, P akistan, by NewsRx correspondents, research stated, "Darknet refers to asignifi cant portion of the internet that is hidden and not indexed by traditional searc h engines."Funders for this research include Deanship of Research And Graduate Studies, Kin g Khalid University,Through The Small Group Research Project.The news correspondents obtained a quote from the research from COMSATS Universi ty Islamabad:"It is often associated with illicit activities such as the traffi cking of illicit goods, such as drugs, weapons,and stolen data. To keep our onl ine cyber spaces safe in this era of rapid technological advancement andglobal connectivity, we should analyse and recognise darknet traffic. Beyond cybersecur ity, this attentionto detail includes safeguarding intellectual property, stopp ing illegal activity, and following the law. Inorder to improve accuracy and pr ecision in identifying illicit activities, this study presents a novel approachnamed Active-Darknet that uses an active learning-based machine learning model f or detecting darknettraffic. In order to guarantee high-quality analysis, our m ethodology includes extensive data preprocessing,such as numerically encoding c ategorical labels and improving the representation of minority classes usingdat a balancing. In addition to machine learning models, we also use Deep Neural Net works (DNN),Bidirectional Long Short-Term Memory (BI-LSTM) and Flattened-DNN fo r experimentation."
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Study Findings Reported from Shanghai Normal University (Re alized Volatility Forecasting for Stocks and Futures Indices With Rolling Ceemda n and Machine Learning Models)

    54-54页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators discuss new findings in Machine Learning. According to news reportingout of Shanghai, People's Republic of China, by NewsRx editors, research stated, "As an essential indexfor measur ing market risk, realized volatility (RV) possesses mixed features and volatilit y aggregation,which makes it difficult for machine learning (ML) models to iden tify its features and trends directly foraccurate prediction. Hence, this study first uses the rolling CEEMDAN (complete ensemble empirical modedecomposition with adaptive noise) method to decompose the original RV sequence of the major s tockmarket indices as well as the bean and the metal futures indices."Financial support for this research came from Shanghai Planning Project of Philo sophy and SocialScience.
      原文链接:
    • NETL
    • NSTL

    Research Conducted at Zhengzhou University Has Provided New Information about Ma chine Learning (Advance and Prospect of Machine Learning Based Fault Detection a nd Diagnosis In Air Conditioning Systems)

    58-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on Machine Learning are discussed in a new report. Accordingto news reporting originating from Zhen gzhou, People's Republic of China, by NewsRx correspondents,research stated, "F ault detection and diagnosis (FDD) are crucial aspects of maintaining efficient andenergy-saving heating ventilation and air conditioning (HVAC) systems. Condi tions such as inadequatemaintenance, poor equipment performance, improper insta llation and defective control mechanisms canall contribute to a reduction in th e operational efficiency of HVAC systems, resulting in unnecessary energywastag e." Funders for this research include National Natural Science Foundation of China ( NSFC), Henan provincial key science and technology research projects.
      原文链接:
    • NETL
    • NSTL

    Findings from Linyi University Has Provided New Data on Nanoplastics (Algae-base d Self-driven Microrobot for Efficient Removal of Nanoplastics From Water Enviro nment)

    61-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-Researchers detail new data in Nanotechnology - N anoplastics. According to news originatingfrom Linyi, People's Republic of Chin a, by NewsRx correspondents, research stated, "Nanoplastics (NPs)have the chara cteristics of various species, wide distribution, low concentration and difficul t degradation.In recent years, the researches of NPs have mainly focused on its toxicity, origin and migration, and thequantitative detection and removal tech nology of NPs is an urgent technical problem to be solved."Financial supporters for this research include National Natural Science Foundati on of China (NSFC),Natural Science Foundation of Shandong Province.Our news journalists obtained a quote from the research from Linyi University, " Here, an activebiohybrid microrobots (DPA-algae robots, diphenolic acids-alage robots) was designed and developed fordynamic removal of NPs in water environme nt. Firstly, diphenolic acids were functionalized on algaeto realize the specif ic adsorption of nanoplastics through hydrophobic force, electrostatic attractio nand van der Waals force between diphenolic acids and nanoplastic particles. Se condly, to realize therapid identification of NPs, the functionalized algae wer e assembled into microrobots through rapid clickchemistry reaction, thus the se lfpropulsion ability of algae can be utilized to accelerate the identificationa nd enrichment of target objects. The removal rate of nanoplastics by microalgae robots has increased to83.1 % at the concentration of 0.125 mg/ m L within 2 h compared with the unmodified algae, which is avery significant imp rovement. In addition, the removal rate was 84.1 % to 87.7 % in different media, sothe dynamic removal of NPs is expected to be applied into the filtration process of waterworks by preparinga large number of algae-based microrobots."
      原文链接:
    • NETL
    • NSTL

    Studies from Durban University of Technology Yield New Data on Machine Learning (Prediction of Wastewater Quality Parameters Using Adaptive and Machine Learning Models: a South African Case Study)

    62-62页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators discuss new findings in Machine Learning. According to news reportingoriginating from Durban, South Afr ica, by NewsRx correspondents, research stated, "The wastewatertreatment proces s often faces challenges in monitoring water quality parameters (WQ), to overcom e thisthere is a need for developing innovative modeling approaches. Hence, the present study is motivatedby the potential application of adaptive and machine learning (ML) models as soft sensors to predict theWQ in one of the largest Mu nicipal Wastewater Treatment Plants (MWWTP) in KwaZulu-Natal, SouthAfrica."Financial support for this research came from National Research Foundation - Sou th Africa.Our news editors obtained a quote from the research from the Durban University o f Technology, "Sevendifferent adaptive and ML algorithms were examined and comp ared, varying from adaptive strategies to MLarchitectures such as Long Short-Te rm Memory (LSTM), Bidirectional LSTM (BiLSTM), Time Difference(TD), Just in Tim e Learning (JIT), Moving Window (MW), and fusion of adaptive strategies (JITTD,and JITTDMW), Support Vector Regression (SVR), and Artificial Neural Network (AN N). Based on theresults, BiLSTM consistently provided the most accurate estimat ion of effluent parameters, with an errorrate ranging from 3.12 to 9.75 % for all variables. For Chemical Oxygen Demand (COD), ammonia, pH,and Total Susp ended Solids (TSS), BiLSTM model yielded low errors (Mean Absolute Error (MAE) values of 1.54, 0.1, 0.22, and 1.14) with lower correlation coefficient values (<0.7) compared to the sixother models proposed. As for conductivity, COD, TSS, p H, ammonia, LSTM, and JITTDMW, JITTDperformed well with MAE values between 1 an d 8 but had difficulty estimating soluble reactive phosphate(SRP). From a futur e perspective, these models could be applied to other MWWTPs facing similar challenges, potentially helping to improve their performance and effectiveness."
      原文链接:
    • NETL
    • NSTL