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

    Investigators from Technical University Darmstadt (TU Darmstadt) Target Machine Learning (General Purpose Potential for Glassy and Crystalline Phases of Cu-zr A lloys Based On the Ace Formalism)

    10-11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 Machine Learning. According to news reporting originating from Darmstadt, German y, by NewsRx correspondents, research stated, “A general purpose machine -learni ng interatomic potential (MLIP) for the Cu-Zr system is presented based on the a tomic cluster expansion formalism [R. Drautz, Phys. Rev. B 99 , 014104 (2019)].” Funders for this research include German Research Foundation (DFG), German Resea rch Foundation (DFG). Our news editors obtained a quote from the research from Technical University Da rmstadt (TU Darmstadt), “By using an extensive set of Cu-Zr training data genera ted withdensity functional theory, this potential describes a wide range of prop erties of crystalline as well as amorphous phases within the whole compositional range. Therefore, the machine learning interatomic potential (MLIP) can reprodu ce the experimental phase diagram and amorphous structure with considerably impr oved accuracy. A massively different short-range order compared to classica inte ratomic potentials is found in glassy Cu-Zr samples, shedding light on the role of the full icosahedral motif in the material.”
      原文链接:
    • NETL
    • NSTL

    Research from Zhejiang A&F University Provides New Study Findings o n Food Research (Identification of Dendrobium Using Laser- Induced Breakdown Spec troscopy in Combination with a Multivariate Algorithm Model)

    11-12页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on food research have been publ ished. According to news reporting out of Hangzhou, People’s Republic of China, by NewsRx editors, research stated, “Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price am ong different varieties. Therefore, achieving an efficient classification of Den drobium is crucial.” Funders for this research include Scientific Research Foundation of Zhejiang A A nd F University. Our news reporters obtained a quote from the research from Zhejiang A& F University: “However, most of the existing identification methods for Dendrobi um make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial product ion. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendr obium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constr ucted fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100% . Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Ne ighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperfor ms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10 %, 10%, and 17%.”
      原文链接:
    • NETL
    • NSTL

    University of Patras Researchers Discuss Research in Robotics (A Voice-Enabled R OS2 Framework for Human-Robot Collaborative Inspection)

    12-13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 Patras, Greece, by NewsRx corresponde nts, research stated, “Quality inspection plays a vital role in current manufact uring practice since the need for reliable and customized products is high on th e agenda of most industries.” Funders for this research include European Commission. The news correspondents obtained a quote from the research from University of Pa tras: “Under this scope, solutions enhancing human-robot collaboration such as v oice-based interaction are at the forefront of efforts by modern industries towa rds embracing the latest digitalization trends. Current inspection activities ar e often based on the manual expertise of operators, which has been proven to be timeconsuming. This paper presents a voice-enabled ROS2 framework towards enhan cing the collaboration of robots and operators under quality inspection activiti es. A robust ROS2-based architecture is adopted towards supporting the orchestra tion of the process execution flow. Furthermore, a speech recognition applicatio n and a quality inspection solution are deployed and integrated to the overall s ystem, showcasing its effectiveness under a case study deriving from the automot ive industry. The benefits of this voiceenabled ROS2 framework are discussed an d proposed as an alternative way of inspecting parts under human-robot collabora tive environments.”
      原文链接:
    • NETL
    • NSTL

    Data on Machine Learning Published by a Researcher at Pukyong National Universit y (LncRNA Expression Profile-Based Matrix Factorization for Predicting lncRNA- D isease Association)

    13-13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 ar tificial intelligence. According to news reporting from Busan, South Korea, by N ewsRx journalists, research stated, “Long non-coding RNAs (lncRNAs) play signifi cant roles in multiple biological processes and contribute to the progression an d development of various human diseases.” Our news correspondents obtained a quote from the research from Pukyong National University: “Therefore, it is necessary to decipher novel lncRNA-disease associ ations from the perspective of biomarker detection. Numerous computational model s have been designed to identify lncRNA-disease associations using machine learn ing. However, many of these models fail to effectively incorporate heterogeneous biological datasets, which can lead to reduced model accuracy and performance. In this study, we propose a novel lncRNA expression profile-based matrix factori zation method that applies lncRNA expression profiles to identify lncRNA-disease association (EMFLDA). Matrix factorization is a machine learning method that ex hibits excellent performance not only in recommender systems, but also in variou s scientific areas. We also applied lncRNA expression profiles as weights for th e proposed model, which allowed for the integration of heterogeneous information and thereby improved performance.”
      原文链接:
    • NETL
    • NSTL

    Reports Outline Robotics Study Findings from Fudan University (A Novel Redundant Cooperative Control Strategy for Robotic Pollination)

    14-14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 out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “This paper proposes a novel Redunda nt Cooperative Control (RCC) strategy to address the pollination sequence planni ng problem for a mobile pollination robot in the greenhouse. Firstly, the RCC st rategy divides the robot’s workspace into subspaces based on the distribution of flowers through clustering.” Our news journalists obtained a quote from the research from Fudan University, “ Subsequently, the RCC strategy plans the traveling salesman problem within the v isual field of these subspaces, seeking a compromise solution for an optimal pat h. Finally, the RCC strategy utilizes the redundant motion capabilities of the m obile chassis and robotic arm to accomplish the pollination task during the move ment. The RCC strategy exploits the available redundancy in both the agricultura l robot’s mobile platform and end-effector, carefully balancing computational co mplexity and path quality. To validate the effectiveness of the RCC strategy, we designed a series of experiments conducted on our pollination robot platform, m easuring different strategies. The results demonstrate that the RCC strategy ach ieves an average pollination rate of 7.5 s per flower, representing a 36.4% improvement compared to the most common intermittence strategy.”
      原文链接:
    • NETL
    • NSTL

    New Data from Shanghai University Illuminate Research in Machine Learning (Ensem ble learning for impurity prediction in high-purity indium purified via vertical zone refining)

    15-15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 ar tificial intelligence. According to news reporting out of Shanghai, People’s Rep ublic of China, by NewsRx editors, research stated, “The complexity of raw mater ials and multi-step purification processes presents considerable technical chall enges in establishing universally applicable process parameters for the producti on of high-purity metals.” Our news journalists obtained a quote from the research from Shanghai University : “Machine learning has emerged as an indispensable tool in the field of materia ls science, facilitating the accurate prediction of target variables and acceler ating process optimization, thereby yielding substantial reductions in both expe rimental costs and time. This study explores the utilization of high-precision m achine learning models to predict the residual impurity content in high-purity i ndium after vertical zone refining. A dataset comprising 82 experimental dataset s was employed to determine the optimal hyperparameters for XGBoost and LightGBM models through Bayesian optimization. The XGBoost and LightGBM models demonstra ted mean absolute errors (MAEs) of 0.022 and 0.023, respectively, as determined via leave-oneout cross-validation (LOOCV). Their comparable predictive performa nce to the previously established Ridge regression model (MAE = 0.024) prompted the exploration of fusion techniques, including mean, weighted, and stacking fus ion, to further enhance accuracy. Remarkably, the weighted fusion model exhibite d the most optimal predictive capabilities, supported by comprehensive evaluatio n metrics, including an MAE of 0.020, root mean squared error (RMSE) of 0.026, a nd a coefficient of determination (R2 score) of 0.830.”
      原文链接:
    • NETL
    • NSTL

    Chinese Academy of Agricultural Sciences Reports Findings in Vaccines (CodonBERT : a BERT-based architecture tailored for codon optimization using the cross-atte ntion mechanism)

    16-16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 Immunization - Vaccine s is the subject of a report. According to news reporting originating from Chang chun, People’s Republic of China, by NewsRx correspondents, research stated, “Du e to the varying delivery methods of messenger RNA (mRNA) vaccines, codon optimi zation plays a critical role in vaccine design to improve the stability and expr ession of proteins in specific tissues. Considering the many-to-one relationship between synonymous codons and amino acids, the number of mRNA sequences encodin g the same amino acid sequence could be enormous.”
      原文链接:
    • NETL
    • NSTL

    Research Data from Shandong University Update Understanding of Robotics (NP-MBO: A newton predictor-based momentum observer for interaction force estimation of legged robots)

    17-17页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on robotics have bee n published. According to news reporting originating from Jinan, People’s Republ ic of China, by NewsRx correspondents, research stated, “Swift perception of int eraction forces is a crucial skill required for legged robots to ensure safe hum an-robot interaction and dynamic contact management.” Funders for this research include Youth Innovation Technology Project of Higher School in Shandong Province; National Key Research And Development Program of Ch ina Stem Cell And Translational Research; National Natural Science Foundation of China.
      原文链接:
    • NETL
    • NSTL

    New Findings from Universiti Kebangsaan Malaysia Describe Advances in Artificial Intelligence (Deep artificial intelligence applications for natural disaster ma nagement systems: A methodological review)

    17-18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 ar tificial intelligence. According to news originating from Selangor, Malaysia, by NewsRx correspondents, research stated, “Deep learning techniques through seman tic segmentation networks have been widely used for natural disaster analysis an d response. The underlying base of these implementations relies on convolutional neural networks (CNNs) that can accurately and precisely identify and locate th e respective areas of interest within satellite imagery or other forms of remote sensing data, thereby assisting in disaster evaluation, rescue planning, and re storation endeavours.” Our news reporters obtained a quote from the research from Universiti Kebangsaan Malaysia: “Most CNN-based deep-learning models encounter challenges related to the loss of spatial information and insufficient feature representation. This is sue can be attributed to their suboptimal design of the layers that capture mult iscale-context information and their failure to include optimal semantic informa tion during the pooling procedures. In the early layers of CNNs, the network enc odes elementary semantic representations, such as edges and corners, whereas, as the network progresses toward the later layers, it encodes more intricate seman tic characteristics, such as complicated geometric shapes. In theory, it is adva ntageous for a segmentation network to extract features from several levels of s emantic representation. This is because segmentation networks generally yield im proved results when both simple and intricate feature maps are employed together . This study comprehensively reviews current developments in deep learning metho dologies employed to segment remote sensing images associated with natural disas ters.”
      原文链接:
    • NETL
    • NSTL

    Data on Robotics Reported by Researchers at University of Melbourne (Effects of Shared Control On Cognitive Load and Trust In Teleoperated Trajectory Tracking)

    18-19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 Parkville, Australia, by NewsRx corre spondents, research stated, “Teleoperation is increasingly recognized as a viabl e solution for deploying robots in hazardous environments. Controlling a robot t o perform a complex or demanding task may overload operators resulting in poor p erformance.” Financial support for this research came from Australian Research Council.
      原文链接:
    • NETL
    • NSTL