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    Findings from Wuhan University Provides New Data on Androids (A Novel Design of Series Elastic Actuator Using Tensile Springs Array)

    75-75页
    查看更多>>摘要：Investigators publish new report on Robotics - Androids. According to news reporting originating in Wuhan, People's Republic of China, by NewsRx journalists, research stated, “Series elastic actuator (SEA) has already been used in robotics, especially the human-robot interaction field, to acquire compliance and force sensing ability. However, achieving a SEA design with linear and consistent elastic properties while having low friction, minor hysteresis, and good compliance is still challenging.” Financial supporters for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Research Project of China Disabled Persons ‘ Federation-on Assistive Technology, Key Research and Development Program of Hubei Province.
      原文链接:
    • NETL
    • NSTL

    Study Data from Nanjing Agricultural University Update Knowledge of Machine Learning [Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)]

    76-76页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting originating from Nanjing, People's Republic of China, by NewsRx correspondents, research stated, “In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples.” Financial supporters for this research include National Natural Science Foundation of China. Our news editors obtained a quote from the research from Nanjing Agricultural University: “This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%.”
      原文链接:
    • NETL
    • NSTL

    New Findings from University of Manchester Describe Advances in Machine Learning (Interpretable Machine Learning-based Analysis of Hydration and Carbonation of Carbonated Reactive Magnesia Cement Mixes)

    77-77页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating from Manchester, United Kingdom, by NewsRx correspondents, research stated, “This study explored the influence of different input variables on the hydration and carbonation degree of carbonated reactive magnesia cement (RMC) system by employing six machine learning algorithms. These included support vector machine (SVM), particle swarm optimization-based SVM (PSO-SVM), extreme learning machine (ELM), grey wolf optimizer-based SVM (GWO-SVM), kernel extreme learning machine (KELM), and extreme gradient boosting (XGBoost).” Financial supporters for this research include Royal Society, China Scholarship Council. Our news editors obtained a quote from the research from the University of Manchester, “The followed approach enabled the deep learning of the relevant database to achieve parameter prediction. Two feature analysis methodologies, i.e. partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP), were applied to uncover the operating laws underpinning the black box operation characteristics of machine learning models. Results revealed that GWO-SVM and XGBoost outperformed all other models in predicting the hydration and carbonation degree of the complete database set (R2 of the total database set was 0.9470/0.9775 and 0.9663/0.9727 for hydration and carbonation degree, respectively). Factors such as carbonation duration, CO2 concentration, pre-curing temperature, and w/b directly influenced the degree of hydration and carbonation.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Study Findings from Tokyo Metropolitan University Advance Knowledge in Computational Intelligence (Construction of Handwritten Indus Signs Dataset Employing Social Approach)

    78-78页
    查看更多>>摘要：Research findings on computational intelligence are discussed in a new report. According to news reporting from Tokyo, Japan, by NewsRx journalists, research stated, “This paper constructs a dataset of handwritten Indus signs employing a social approach.” Our news journalists obtained a quote from the research from Tokyo Metropolitan University: “A writing system called the Indus script was created in the Indus civilization. It has been decoded numerous times throughout the years, but it has not yet been fully deciphered. Due to a lack of information and the scarcity of evidence, the mystery of the Indus signs has not yet been fully solved. Recently, there has been an increase in demand for huge datasets in order to use cutting-edge machine learning techniques. Considering the restricted availability of images of authentic Indus signs, this paper proposes creating an Indus signs dataset by asking participants to draw the Indus signs while referring to the image of the original Indus signs. A web application was developed and used to collect the 44 participants' handwritten images of ten Indus signs.”
      原文链接:
    • NETL
    • NSTL
    • Fuji Technology Press Ltd

    Research on Machine Learning Reported by a Researcher at University of Hertfordshire (Burnt-in Text Recognition from Medical Imaging Modalities: Existing Machine Learning Practices)

    78-79页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news originating from Hertfordshire, United Kingdom, by NewsRx editors, the research stated, “In recent times, medical imaging has become a significant component of clinical diagnosis and examinations to detect and evaluate various medical conditions.” The news correspondents obtained a quote from the research from University of Hertfordshire: “The interpretation of these medical examinations and the patient's demographics are usually textual data, which is burned in on the pixel content of medical imaging modalities (MIM). Example of these MIM includes ultrasound and X-ray imaging. As artificial intelligence advances for medical applications, there is a high demand for the accessibility of these burned-in textual data for various needs. This article aims to review the significance of burned-in textual data recognition in MIM and recent research regarding the machine learning approach, challenges, and open issues for further investigation on this application.”
      原文链接:
    • NETL
    • NSTL
    • Fuji Technology Press Ltd

    Findings from Virginia Polytechnic Institute and State University (Virginia Tech) Has Provided New Data on Machine Learning (A Novel Machine Learning and Deep Learning Semi-supervised Approach for Automatic Detection of Insar-based Deformation …)

    79-80页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Blacksburg, Virginia, by NewsRx editors, the research stated, “Over the past two decades, Interferometric synthetic aperture radar (InSAR) has been invaluable for studying earth surface deformation and related effects. Deformation maps generated through multi-temporal InSAR processing methods are however difficult to interpret accurately by general individual users, decision-makers, and non-domain experts owing to the volume, variety, and velocity they are produced.” Financial supporters for this research include National Science Foundation (NSF), United States Geological Survey, United States Department of Energy (DOE). Our news journalists obtained a quote from the research from Virginia Polytechnic Institute and State University (Virginia Tech), “This paper proposes a semi-supervised machine learning based information mining approach to simplify these deformation maps and detect hotspots by extracting prominent signals from time series deformation. The approach initially combines two machine learning based clustering methods named time series k-means (TSKM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to derive clusters with unique spatiotemporal deformation behavior, using time series deformation output generated from Wavelet-based InSAR (WabInSAR) method. Clustering results generated from this unsupervised machine learning approach are later used as training labels to develop two deep learning models, one using long short term memory (LSTM) networks alone and another using a combination of LSTM and single-layer perceptron for supervised training. The developed LSTM and LSTM + Perceptron models efficiently learn from the cluster labels, reaching an accuracy of 97.3 %. Further, the deep learning models significantly reduce the computational time from orders of days (-5) to hours (-2) while training and from hours to minutes during prediction. We evaluate the developed approach over Los Angeles, a highly challenging area affected by umpteen deformation events that are challenging to categorize. The outcome of the proposed approach produces hotspots of deforming areas in Los Angeles, providing a generalized and more precise picture of events, much appreciable to non-domain experts."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports Outline Machine Learning Study Findings from University of Otago (Machine Learning-driven Hyperspectral Imaging for Non-destructive Origin Verification of Green Coffee Beans Across Continents, Countries, and Regions)

    80-81页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Dunedin, New Zealand, by NewsRx journalists, research stated, “Coffee is a target for geographical origin fraud. More rapid, cost-effective, and sustainable traceability solutions are needed.” Financial support for this research came from University of Otago. The news correspondents obtained a quote from the research from the University of Otago, “The potential of hyperspectral imaging-near-infrared (HSI-NIR) and advanced machine learning models for rapid and non-destructive origin classification of coffee was explored for the first time (ⅰ) to understand the sensitivity of HSI-NIR for classification across various origin scales (continental, country, regional), and (ⅱ) to identify discriminant wavelength regions. HSI-NIR analysis was conducted on green coffee beans from three continents, eight countries, and 22 regions. The classification performance of four different machine learning models (PLS-DA, SVM, RBF-SVM, Random Forest) was compared. Linear SVM provided near- perfect classification performance at the continental, country, and regional levels, and enabled a feature selection opportunity.” According to the news reporters, the research concluded: “This study demonstrates the feasibility of using HSI-NIR with machine learning for rapid and nondestructive screening of coffee origin, eliminating the need for sample processing.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Data on Robotics Detailed by Researchers at Columbia University (Robotically Controlled Head Oscillations During Overground Walking: a Comparison of Elderly and Young Adults)

    81-82页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news originating from New York City, New York, by NewsRx correspondents, research stated, “Head turns during walking have been used to assess balance, mobility, and vestibular function in both experimental and clinical applications. However, head turns in walking experiments have been limited to self-initiated head motions as opposed to controlled motions.” Financial support for this research came from ALS Association Grants, Columbia. Our news journalists obtained a quote from the research from Columbia University, “The aim of this study is to evaluate the effects of controlled head turns enabled by a robotic neck brace in elderly and young adults during overground walking under normal (HO) and altered vision (HVO). The robotic neck brace applied controlled sinusoidal head turns around the vertical axis at +/- 30(degrees), 0.4 Hz. The vision was altered using a virtual reality headset, where the visual field was aligned along the direction of the head oscillation. Ten elderly (EA, 65-85 yrs) and ten younger (YA, 22-32 yrs) adults were recruited. Spatiotemporal gait parameters, such as stride length (SL), stride width (SW), stride velocity (SV), stride time (ST), stance time percentage (STP), and direction of progression (DoP), as well as mediolateral and anterior-posterior margins of stability (MLMoS and AP(MoS)) were analyzed. Elderly participants showed greater gait changes than younger individuals, particularly during HVO, leading to the highest DoP deviations. Our results indicate that the elderly had difficulty relying on non visual cues to compensate for the altered vision. However, they had comparable MLMoS and more stable AP(MoS). Overall, older adults prioritized balance and stability, while young adults focused on preserving walking direction.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Researcher at Beijing Institute of Technology Targets Robotics (A Multi-Objective Trajectory Planning Method of the Dual-Arm Robot for Cabin Docking Based on the Modified Cuckoo Search Algorithm)

    82-83页
    查看更多>>摘要：Current study results on robotics have been published. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, “During the assembly of mechanical systems, the dual-arm robot is always used for cabin docking.” Funders for this research include National Natural Science Foundation of China; Science And Technology Cooperation Project of Yunnan Province. Our news journalists obtained a quote from the research from Beijing Institute of Technology: “In order to ensure the accuracy and reliability of cabin docking, a multi-objective trajectory planning method for the dual-arm robot was proposed. A kinematic model of the dual-arm robot was constructed based on the Denavit-Hartenberg (D-H) method firstly. Then, in the Cartesian space, the end trajectory of the dual-arm robot was confirmed by the fifth-order B-spline curve. On the basis of a traditional multi-objective cuckoo search algorithm, a modified cuckoo algorithm was built using the improved initial population generation method and the step size. The total consumption time and joint impact were selected as the objective functions, the overall optimal solution for the modified cuckoo algorithm was obtained using the normalized evaluation method.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Researchers from University of Strathclyde Detail Findings in Machine Learning (Space object identification and classification from hyperspectral material analysis)

    83-83页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting out of the University of Strathclyde by NewsRx editors, research stated, “This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects.” Financial supporters for this research include European Space Agency. The news reporters obtained a quote from the research from University of Strathclyde: “The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed.”
      原文链接:
    • NETL
    • NSTL