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

    Studies Conducted at Southeast University on Robotics Recently Reported (Global Analysis of Energy-based Swing-up Control for Soft Robot)

    29-30页
    查看更多>>摘要:Current study results on Robotics have been published. According to news reporting out of Nanjing, People's Republic of China, by NewsRx editors, research stated, "In this paper, we explore the energy-based swing-up control for a soft robot equipped with an actuated constant curvature soft pendulum and an unactuated rotational base joint. The aim is to swing the robot up towards its upright equilibrium point (UEP) with the pendulum at an upright position." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Southeast University, "We establish a necessary and sufficient condition to protect the control law from singularities and demonstrate the robot's potential motion towards a homoclinic orbit or a closed-loop equilibrium point. After examining the robot's closed-loop equilibrium points, we generate formulae to compute all such points and introduce two conditions concerning control parameters, eliminating all but the UEP and downward equilibrium point (DEP) with the robot in a downward position. We prove the instability of the equilibrium points with negative gravitational potential energy. This obviates the need for one of the two conditions concerning such equilibrium points. We also prove the robot's linear controllability at the UEP. Our obtained results reveal that, regardless of its initial state, the soft robot can be swung-up towards its UEP using the energybased controller, provided that it meets the proposed control parameter conditions, and can subsequently be balanced around the UEP using a locally stabilizing controller."

    Findings from China Three Gorges University Yields New Data on Robotics (Mobile Robot Path Planning With Two Stages Based On Hybrid Intelligent Optimisation Algorithm)

    30-31页
    查看更多>>摘要:A new study on Robotics is now available. According to news reporting from Yichang, People's Republic of China, by NewsRx journalists, research stated, "In this paper, a hybrid intelligent optimisation (HIO) algorithm is presented to solve the path planning problem of the mobile robot with two stages. At the first stage, the Dijkstra algorithm is used to determine the dimension of the element in the population and the current optimal solution for MRPP." The news correspondents obtained a quote from the research from China Three Gorges University, "At the second stage, a new screening mechanism is proposed, where the whole population is divided into three groups, i.e., the top element group, the middle element group, and the low element group by using the improved five -elements cycle model (IFECM). Then the PSO algorithm and the crossover operator are used to update the elements in the middle element group. The mutation operator is used to update the elements in the low element group. The updated middle element group, low element group are used to update the top element group and gbest. Finally, the key point deletion and turning point optimisation processing are implemented, which contribute to generating a flatter path and avoiding obstacles." According to the news reporters, the research concluded: "Compared with 14 other algorithms, simulation experiments on mobile robot path planning under eight different scenarios prove that the proposed method achieves the highest success rate in planning paths and the shortest generated paths." This research has been peer-reviewed.

    Findings from University of Science and Technology Beijing Has Provided New Data on Machine Learning (Selection of Mechanical Properties of Uranium and Uranium Alloys After Corrosion Based On Machine Learning)

    31-32页
    查看更多>>摘要:Investigators discuss new findings in Machine Learning. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "Uranium and uranium alloys play a vital role as service materials in strategic equipment, but their mechanical properties can be adversely affected by corrosion. Therefore, accurately predicting the mechanical properties of uranium and uranium alloys after corrosion holds significant importance." Financial support for this research came from NATIONAL KEY R & D PROGRAM OF CHINA. The news correspondents obtained a quote from the research from the University of Science and Technology Beijing, "In this study, we created a database to investigate the impact of oxygen corrosion on the mechanical properties of uranium and uranium alloys. Uti-lizing this database, we developed a featureguided decision tree algorithm to predict various tensile properties, including yield strength, tensile strength, elongation, and cross-section shrinkage. Our research highlights three key findings. Firstly, we established a machine learning modeling framework that effectively predicts tensile properties and exhibits potential for predicting other properties of uranium and uranium alloys. Secondly, through feature engineering, we uncovered crucial correlations involving reaction time, reaction temperature, alloy type, phase structure composition, and phase number. These correlations significantly enhanced the per-formance of machine learning models in predicting tensile properties after oxygen corrosion." According to the news reporters, the research concluded: "Lastly, by employing the decision tree algorithm guided by feature engineering, we successfully predicted the mechanical properties of uranium and uranium alloys after oxygen corrosion with a prediction error of less than 5%." This research has been peer-reviewed.

    Data on Beggiatoa Reported by Researchers at University of Tasmania (A Novel Adaptive Ensemble Learning Framework for Automated Beggiatoa Spp. Coverage Estimation)

    32-33页
    查看更多>>摘要:A new study on Gram-Negative Bacteria - Beggiatoa is now available. According to news originating from Hobart, Australia, by NewsRx correspondents, research stated, "The presence of Beggiatoa Spp. indicates anoxic conditions or 'poor condition' in marine sediments beneath aquaculture pens, resulting from organic enrichment. Currently, the most efficient approach to estimate Beggiatoa Spp. coverage, and thus the extent of the issue, involves video surveys which are scored by human observers for presence of this bacteria." Our news journalists obtained a quote from the research from the University of Tasmania, "However, this approach is highly time-consuming and relies heavily on the expertise and experience of the individuals involved, thus affecting its accuracy. Machine learning-based computer vision techniques, such as Convolutional Neural Networks (CNNs), offer the potential for automated estimation of Beggiatoa Spp. coverage. However, most existing machine learning methods focus solely on the estimation of the coverage via presence, absence of a single type of Beggiatoa Spp.. These approaches typically rely on binary classification to distinguish the object from the background when estimating coverage. Nevertheless, the inclusion of subordinate categories within high-level classifications poses a great challenge for accurately estimating their coverage rates. In this paper, an adaptive ensemble learning approach was proposed to estimate Beggiatoa Spp. coverage. Unlike other approaches, the proposed approach is capable in adaptively extracting and fusing features from underwater images and accurately estimating the coverages of multiple types of Beggiatoa Spp. through ensemble learning."

    Studies in the Area of Machine Learning Reported from Indian Institute of Technology (IIT) Madras (Evaluation and Analysis of Liquefaction Potential of Gravelly Soils Using Explainable Probabilistic Machine Learning Model)

    33-33页
    查看更多>>摘要:Investigators publish new report on Machine Learning. According to news reporting originating in Tamil Nadu, India, by NewsRx journalists, research stated, "Majority of the presently available machine learning (ML) models employed to assess the liquefaction potential of soils are for sands or sands containing silt fraction. In the current study, an explainable ML (EML) model has been developed using the updated liquefaction database of gravelly soils." Financial support for this research came from Ministry of Education, Govt. of India. The news reporters obtained a quote from the research from the Indian Institute of Technology (IIT) Madras, "The Chinese dynamic cone penetration test (DPT) and shear wave velocity test results of gravelly soils are used in the analysis. A new empirical correlation between these two in-situ tests' results is developed using the final processed database. The light gradient boosting machine (LightGBM) is trained using this processed dataset and further tuned using Fast and Lightweight AutoML library (FLAML). The final tuned model shows relatively better deterministic and probabilistic predictive performance for the test sites as compared to the conventional method. An EML technique, SHapley Additive exPlanations (SHAP) is applied to provide further comprehension into the predictions. The developed LightGBM-SHAP model has achieved a right balance between explainability and accuracy. The obtained SHAP plots are consistent with almost all the existing domain knowledge (DK) in gravelly soil liquefaction."

    Sun Yat-Sen University Reports Findings in Artificial Intelligence (Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers)

    34-35页
    查看更多>>摘要:New research on Artificial Intelligence is the subject of a report. According to news reporting from Nanning, People's Republic of China, by NewsRx journalists, research stated, "The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases." Funders for this research include Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project, Guangxi Natural Science Foundation Project. The news correspondents obtained a quote from the research from Sun Yat-Sen University, "Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p <0.05). SERPINE2 was found to be positively associated with memory B cells, while CPT1B correlated positively with CD8 T cells. Immunohistochemical assessments indicated that SERPINE2 was more prominently expressed in OS tissues relative to adjacent non-tumorous tissues. Conversely, CPT1B expression was elevated in the adjacent non-tumorous tissues compared to OS tissues. Lymphocyte counts from routine blood evaluations exhibited marked differences between normal and OS groups (p <0.001). The study highlights SERPINE2 and CPT1B as crucial biomarkers for OS prognosis and suggests that dysregulation of lymphocytes plays a significant role in OS pathogenesis."

    Findings on Robotics Reported by Investigators at University of Cassino and Southern Lazio (A Control Architecture for Safe Trajectory Generation In Human-robot Collaborative Settings)

    35-36页
    查看更多>>摘要:Research findings on Robotics are discussed in a new report. According to news reporting out of Cassino, Italy, by NewsRx editors, research stated, "This paper introduces a control architecture that enables a robotic system to ensure the safety of human operators entering its workspace. The proposed method utilizes an appropriate metric to measure safety levels and adjusts the robot's motion to maintain this metric above a minimum threshold." Financial support for this research came from Horizon 2020-Information and Communication Technologies. Our news journalists obtained a quote from the research from the University of Cassino and Southern Lazio, "To guarantee safety, the robot scales down and deviates from its intended path. For redundant robots, internal motion is exploited to enhance safety levels further. The approach is incorporated into a Hierarchical Quadratic Programming control framework, allowing the robot to address other control objectives simultaneously, such as handling joint limits." According to the news editors, the research concluded: "Experimental results with a dual-arm mobile robot developed as part of the EU-funded CANOPIES project demonstrate the effectiveness of the proposed method." This research has been peer-reviewed.

    Researchers at Portland State University Report New Data on Robotics (The Adoption of Social Robots In Service Operations: a Comprehensive Review)

    35-35页
    查看更多>>摘要:Data detailed on Robotics have been presented. According to news originating from Portland, Oregon, by NewsRx correspondents, research stated, "A bibliometric analysis has been conducted by utilizing keywords and metadata acquired from the SCOPUS database." Our news journalists obtained a quote from the research from Portland State University, "Following that, we focused on the adoption of social robots in service operations by conducting a systematic literature review of 79 articles shortlisted from Scopus, Web of Science, EBSCO Business Source Complete, and Google Scholar databases. We propose a research framework using the extended Antecedents, Decisions, Outcomes, and Technological-Organizational-Environmental framework, along with the applications and challenges with the adoption and diffusion of social robots in service operations." According to the news editors, the research concluded: "We found that social robots have mostly been adopted in the travel, tourism, hotel, and hospitality industries." This research has been peer-reviewed.

    Findings from Nanjing University of Aeronautics and Astronautics Update Knowledge of Machine Learning (Transfer Learning Model To Predict Flow Boiling Heat Transfer Coefficient In Mini Channels With Micro Pin Fins)

    36-37页
    查看更多>>摘要:Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Nanjing, People's Republic of China, by NewsRx journalists, research stated, "Flow boiling in mini channels with micro pin fins is a promising heat sink technique to achieve high-efficient aircraft thermal management. The accurate prediction of its heat transfer coefficient is critical for the practical design of two-phase heat exchanger based on mini channels with micro pin fins." Funders for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities, The "Chunhui Plan" Cooperative Research Project Foundation of Ministry of Education of China. The news reporters obtained a quote from the research from the Nanjing University of Aeronautics and Astronautics, "Previous investigation shows that heat transfer coefficient prediction accuracy of the machine learning method is generally better than that of conventional empirical correlations. However, the machine learning method cannot guarantee its prediction accuracy among different data domains. To extend the application region of the conventional machine learning method, a transfer learning framework was proposed in present study. First of all, an experimental system was built to acquire test data from different sample domains (i.e., the diamond pin fins with different geometries). Then a conventional machine learning model was developed based on the deep learning method. Furthermore, the developed deep learning model was adjusted with transfer learning process, and the performance of these two kinds of models (i.e., conventional machine learning model and transfer learning model) was comprehensively evaluated. Results showed that the conventional machine learning model had a good prediction accuracy with an overall deviation of 4.11 % but was only confined in the same data domain as training data. Differently, the transfer learning model with 70 % data set from the new domain can achieve appropriate prediction in the new domains with deviation of 4.28 %. The results of this paper demonstrated that the conventional machine learning model can extend into different domains with reasonable prediction accuracy through transfer learning frameworks."

    University of Science and Technology of China Reports Findings in Machine Learning (Machine Learning K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Accurate and Efficient Cubic-Scaling Exact Exchange Plus Random …)

    38-39页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news reporting originating from Anhui, People's Republic of China, by NewsRx correspondents, research stated, "The exact-exchange plus random-phase approximation (EXX+RPA) method has emerged as a crucial tool for precisely characterizing electronic structures in molecular and solid systems. We present an accurate and efficient implementation of EXX+RPA calculations that scale cubically and are conducted within plane waves." Our news editors obtained a quote from the research from the University of Science and Technology of China, "Our approach incorporates the interpolative separable density fitting (ISDF) algorithm, effectively mitigating the computational challenges associated with the plane wave basis set. To overcome the constraints of the conventional ISDF algorithm, characterized by the exceptionally high prefactor in QR factorization for interpolation point selection, we introduce an enhanced machine learning K-means method. This method incorporates a novel empirical weight function called 'SSM+' for more precise interpolation point selection, capturing physical information more accurately across diverse systems. Our machine learning approach offers a quasiquadratic scaling alternative, effectively replacing the computationally demanding cubic-scaling QRCP algorithm in plane-wave-based EXX+RPA calculations. Furthermore, we enhance the method's capabilities by optimizing GPU acceleration using MATLAB's integrated GPU toolkit. In particular, our approach reduces the computational scaling of ch from 3.80 to 2.13 and the overall computational scaling of EXX from 2.74 to 2.10. We achieve a remarkable GPU acceleration speedup of up to 35 x . Regarding CPU computation time, the standard quartic-scaling method requires 22 h to compute Si, while QRCP completes the calculation in only around 1 h, achieving a speedup up to 20 x . However, the utilization of the K-means algorithm reduces the time to 800 s, a substantial improvement of 100 x compared to the standard algorithm. By employing the K-means algorithm, the computational time for interpolative point calculation using QRCP decreases from 1 h to 1 min, resulting in a 55 x speed increase."