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    Researcher at Tianjin University Discusses Research in Robotics (Performance prediction of industrial robot harmonic reducer via feature transfer and Gaussian process regression)

    47-47页
    查看更多>>摘要：New research on robotics is the subject of a new report. According to news reporting originating from Tianjin, People’s Republic of China, by NewsRx correspondents, research stated, “This paper addresses the problem of identifying faults in the harmonic reducers of industrial robots by analysing their vibration signals.” Our news reporters obtained a quote from the research from Tianjin University: “In order to solve the problem of obtaining fault data and rotation error from harmonic reducers in service, an accuracy performance prediction method based on transfer learning and Gaussian process regression (GPR) is proposed. The Euclidean distance between the spectral sequence of each component is proposed as the fitness index to optimise the transition bandwidth of the filter banks. The optimised empirical wavelet transform (OEWT) is used for signal decomposition to obtain sensitive frequency bands. A feature transfer method based on semi-supervised transfer component analysis (SSTCA) is proposed to achieve target domain feature transfer under missing data conditions.” According to the news editors, the research concluded: “A prediction model based on GPR is established using the mapped features to predict the performance and accuracy of the harmonic reducer. The effectiveness of the proposed method is verified through model evaluation indicators and degradation experiments.”
      原文链接:
    • NETL
    • NSTL

    New Findings from Indiana University in the Area of Robotics Reported (Designing Robots for Marketplace Success: a Case Study With Technology for Behavior and Habit Change)

    47-48页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news originating from Bloomington, Indiana, by NewsRx correspondents, research stated, “This research seeks to identify the factors that affect people’s decision to purchase, or to not purchase, social robots for their homes. To this aim, we focus on a specific technological use case: behavior and habit change.”Financial support for this research came from Social Science Research Commons, Indiana University. Our news journalists obtained a quote from the research from Indiana University, “As consumer behavior research suggests that preferred designs and price sensitivity will vary between those who are technology early adopters and those who are mainstream adopters, we look at how self-classification influences the aforementioned areas. To this end, we interview 18 individuals to identify behavioral change goals and note reactions to three videos of technology for habit change. In addition to assessing willingness-to-pay (WTP) by using established methods in market research, holistic product design cards are also created to aid this process and to support user design. Additionally, we compare how people’s purchase-based designs differ from their ideal designs. We find that although early adopters prefer domestic robots to be human-like in form and behavior, they exhibit significant downgrading, especially to a more device-like form, due to price.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Studies from Sun Yat-sen University Further Understanding of Artificial Intelligence (Research On the Uncanny Valley Effect In Artificial Intelligence News Anchors)

    48-49页
    查看更多>>摘要：Current study results on Artificial Intelligence have been published. According to news originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The uncanny valley effect has sparked interest in fields such as humanoid robotics and hyper-realistic virtual animation. Nonetheless, proof of its existence in artificial intelligence (AI) news anchors remains limited.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Sun Yat-sen University, “This study examined the existence and effect of the uncanny valley in AI news anchors. Particularly, it delved into human perception and behavior during interactions with AI news anchors. AI news anchors failed to establish emotional bonds with audiences, and thus fell within the uncanny valley. Audiences were sensitive to minor defects and oddities in the AI news anchors, and felt eerie while watching them.” According to the news editors, the research concluded: “Findings of this study can be used to formulate guidelines for the design of the appearance and behavior of not only AI news anchors but all humanoid AI characters.” This research has been peer-reviewed. For more information on this research see: Research On the Uncanny Valley Effect In Artificial Intelligence News Anchors. Multimedia Tools and Applications, 2024. Multimedia Tools and Applications can be contacted at: Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Research Reports on Machine Learning from Universidad Catolica de Santa Maria Provide New Insights (Classification of Motor Competence in Schoolchildren Using Wearable Technology and Machine Learning with Hyperparameter Optimization)

    49-50页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting out of Arequipa, Peru, by NewsRx editors, research stated, “Determining the classification of motor competence is an essential aspect of physical activity that must be carried out during school years. The objective is to evaluate motor competence in schoolchildren using smart bands, generate percentiles of the evaluation metrics, and classify motor performance through machine learning with hyperparameter optimization.” Financial supporters for this research include Catholic University of Santa Maria. The news reporters obtained a quote from the research from Universidad Catolica de Santa Maria: “A cross-sectional descriptive study was carried out on 764 schoolchildren (451 males and 313 females) aged 6 to 17 years. Five state schools in the city of Arequipa, Peru were evaluated. Weight, height, and waist circumference were assessed, and body mass index (BMI) was calculated. The tests evaluated in the schoolchildren measured walking and running for 6 minutes. These tests were carried out using smart bands, capturing cadence, number of steps, calories consumed, speed, stride, and heart rate. As a result, the percentiles were created through the LMS method [L (asymmetry: lambda), M (median: mu), and S (coefficient of variation: sigma)]. The cut-off points considered were p75 (above average). For classification, the machine-learning algorithms random forest, decision tree, support vector machine, naïve Bayes, logistic regression, k-nearest neighbor, neural network, gradient boosting, XGBboost, LightGBM, and CatBoost were used, and the hyperparameters of the models were optimized using the RandomizedSearchCV technique. In conclusion, it was possible to classify motor competence with the tests carried out on schoolchildren, significantly improving the accuracy of the machine-learning algorithms through the selected hyperparameters, with the gradient boosting classifier being the best result at 0.95 accuracy and in the ROC-AUC curves with a 0.98. The reference values proposed in this study can be used to classify the walking motor competence of schoolchildren.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Findings from University of Ghent Update Understanding of Nanocrystals (Prediction of Residual Stress Distribution Induced By Ultrasonic Nanocrystalline Surface Modification Using Machine Learning)

    50-51页
    查看更多>>摘要：Current study results on Nanotechnology Nanocrystals have been published. According to news originating from Ghent, Belgium, by NewsRx correspondents, research stated, “Ultrasonic Nanocrystalline Surface Modification (UNSM) offers an efficient and cost-effective approach for enhancing material mechanical properties by inducing Severe Plastic Deformation (SPD). It leads to grain refinement and substantial residual stress generation beneath the workpiece surface.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), China Scholarship Council, Ministry of Trade, Industry & Energy (MOTIE), Republic of Korea, Korea Institute for Advancement of Technology (KIAT) through the International Cooperative RD Program. Our news journalists obtained a quote from the research from the University of Ghent, “This study investigates the influence of key modification parameters, specifically static load, vibration amplitude, and strike tip size on compressive residual stress (CRS) distribution. A Finite Element Method (FEM)-based model for the UNSM process is developed, and validated against experimental outcomes, yielding a dataset of 45 unique cases across various modification scenarios. The Balancing Composite Motion Optimization (BCMO), as a meta-heuristic algorithm is used to optimize the hyperparameters of the Support Vector Regression (SVR) model. Additionally, the performance of Artificial Neural Network (ANN), Polynomial Chaotic Extension (PCE), and Kriging algorithms is evaluated in parallel. Among these Machine Learning (ML) models, the SVR-BCMO emerges as a pioneer for its accuracy in estimating residual stress. A sensitivity analysis employing Sobol’ indices further clarifies the distinct impact of each input parameter on residual stress distribution resulting from UNSM. In essence, this research offers a tool for rapidly estimating residual stress, even in cases of limited datasets. Furthermore, the findings help in making prompt decisions regarding of UNSM conditions.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Researchers from Florida Polytech University Detail Findings in Artificial Intelligence (Endoscopic Sleeve Gastroplasty: Stomach Location and Task Classification for Evaluation Using Artificial Intelligence)

    51-52页
    查看更多>>摘要：Current study results on Artificial Intelligence have been published. According to news originating from Lakeland, Florida, by NewsRx correspondents, research stated, “PurposeWe have previously developed grading metrics to objectively measure endoscopist performance in endoscopic sleeve gastroplasty (ESG). One of our primary goals is to automate the process of measuring performance.” Funders for this research include NIH National Institute of Biomedical Imaging & Bioengineering (NIBIB), National Institutes of Health (NIH) USA. Our news journalists obtained a quote from the research from Florida Polytech University, “To achieve this goal, the repeated task being performed (grasping or suturing) and the location of the endoscopic suturing device in the stomach (Incisura, Anterior Wall, Greater Curvature, or Posterior Wall) need to be accurately recorded.MethodsFor this study, we populated our dataset using screenshots and video clips from experts carrying out the ESG procedure on ex vivo porcine specimens. Data augmentation was used to enlarge our dataset, and synthetic minority oversampling (SMOTE) to balance it. We performed stomach localization for parts of the stomach and task classification using deep learning for images and computer vision for videos.ResultsClassifying the stomach’s location from the endoscope without SMOTE for images resulted in 89% and 84% testing and validation accuracy, respectively. For classifying the location of the stomach from the endoscope with SMOTE, the accuracies were 97% and 90% for images, while for videos, the accuracies were 99% and 98% for testing and validation, respectively. For task classification, the accuracies were 97% and 89% for images, while for videos, the accuracies were 100% for both testing and validation, respectively.ConclusionWe classified the four different stomach parts manipulated during the ESG procedure with 97% training accuracy and classified two repeated tasks with 99% training accuracy with images. We also classified the four parts of the stomach with a 99% training accuracy and two repeated tasks with a 100% training accuracy with video frames.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings from China University of Petroleum Broaden Understanding of Robotics (Multi-jointed Pneumatic Sealing Disc of Fluiddriven Pipeline Robot: Impacts of Structural Parameters On Performance)

    52-53页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Pipeline inspection gauge (PIG) is often blocked in the ageing pipelines of stacked objects. A novel pneumatically controlled sealing disc has been designed to address the issue of PIG’s blockage.” Financial supporters for this research include Beijing Research Center of CNOOC (China) Co., Ltd, CNOOC (China) Co. Ltd., Beijing Research Center. Our news journalists obtained a quote from the research from the China University of Petroleum, “Compared to traditional passive-controlled sealing discs, the new sealing disc incorporates a multi-jointed pneumatic webbed foot that enables active control. It provides both single execution mode and multiple execution mode capabilities, allowing it to achieve partial bending through the inflation of a single foot and thereby enhancing sealing performance during active control. A finite element method (FEM) was introduced and effectively validated by comparing simulation results with experimental results of the inflatable bending experiments of a single foot. Then the performance of the sealing disc with single execution mode and multiple execution mode was compared using the FEM, and the results show that they have good consistency. Next, the effects of four kinds of materials and five structural parameters on the performance of the sealing disc were studied using the FEM. The results show that the multi-material structure has better performance compared to the same materials and the Ecoflex series demonstrates greater responsiveness in inflation experiments. As the air pressure grows, the position parameter of the joint has little effect on the bending angle as well as the expansion ratio, and the bottom disc thickness has also slightly influence on the expansion ratio. Meanwhile, the thicker the webbed foot thickness, the smaller the slope angle and the thinner, the larger the bending angle; the smaller the slope angle, the thicker the webbed foot thickness, the larger the expansion ratio. The conclusions obtained in this paper are beneficial for the design and optimization of PIG sealing discs for actively controlling.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    New Machine Learning Study Results Reported from Shanghai Jiao Tong University (Integrated Moves Model and Machine Learning Method for Prediction of Co2 and No From Light-duty Gasoline Vehicle)

    53-54页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R-2 values. On average, the improvement for CO2 was 0.132, and for NO, it was 0.261.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Northern Jiangsu People's Hospital Reports Findings in Bioinformatics [Effects of chronic low-level lead (Pb) exposure on cognitive function and hippocampal neuronal ferroptosis: An integrative approach using bioinformatics analysis, machine ...]

    54-55页
    查看更多>>摘要：New research on Biotechnology Bioinformatics is the subject of a report. According to news reporting from Yangzhou, People’s Republic of China, by NewsRx journalists, research stated, “Lead (Pb), a pervasive and ancient toxic heavy metal, continues to pose significant neurological health risks, particularly in regions such as Southeast Asia. While previous research has primarily focused on the adverse effects of acute, high-level lead exposure on neurological systems, studies on the impacts of chronic, low-level exposure are less extensive, especially regarding the precise mechanisms linking ferroptosis a novel type of neuron cell death with cognitive impairment.” The news correspondents obtained a quote from the research from Northern Jiangsu People’s Hospital, “This study aims to explore the potential effects of chronic low-level lead exposure on cognitive function and hippocampal neuronal ferroptosis. This research represents the first comprehensive investigation into the impact of chronic low-level lead exposure on hippocampal neuronal ferroptosis, spanning clinical settings, bioinformatic analyses, and experimental validation. Our findings reveal significant alterations in the expression of genes associated with iron metabolism and Nrf2-dependent ferroptosis following lead exposure, as evidenced by comparing gene expression in the peripheral blood of lead-acid battery workers and workers without lead exposure. Furthermore, our in vitro and in vivo experimental results strongly suggest that lead exposure may precipitate cognitive dysfunction and induce hippocampal neuronal ferroptosis.”
      原文链接:
    • NETL
    • NSTL

    New Robotics Findings from Shanghai Jiao Tong University Described (Design, Modeling, and Control of a Novel Soft-rigid Knee Joint Robot for Assisting Motion)

    55-56页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting originating in Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “This paper presents the design, modeling, and control of a novel soft-rigid knee joint robot (SR-KR) for assisting motion. SR-KR is proposed to assist patients with knee joint injuries conducting gait training and completing walking movements.” Funders for this research include National Key Technology R&D Program, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from Shanghai Jiao Tong University, “SR-KR consists of a novel soft-rigid bidirectional curl actuator, a thigh clamping structure, and a crus clamping structure. The actuating part of SR-KR is composed of soft materials, which ensures the wearing comfort and safety, while the wearing parts contain rigid structure, which ensures the efficient transmission of torque. The bending deformation model of SR-KR is established, which reveal the relationship among SRKR’s bending curvature, working pressure, and output torque. Experiments show that SR-KR can provide more than 26.3 Nm torque for knee joint motion in human gait range. A double closed loop servo control system including attitude servo and pressure servo is built to better apply SR-KR.”
      原文链接:
    • NETL
    • NSTL