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    Research Conducted at Changzhou University Has Provided New Information about Robotics (Performance Analysis and Optimal Design of a Novel Schoenflies-motion Asymmetric Parallel Mechanism)

    38-38页
    查看更多>>摘要:Current study results on Robotics have been published. According to news originating from Jiangsu, People’s Republic of China, by NewsRx correspondents, research stated, “Since previous studies of parallel mechanisms (PMs) have tended to favor symmetrical overall configuration to obtain relatively stable kinematic and dynamic performance and to satisfy isotropic requirements. The analysis of kinematic and dynamic performance of asymmetric mechanisms has been an issue of interest.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Changzhou University, “In this paper, an asymmetric SCARA-type PM with four-degrees-of-freedom (DOF) is proposed. First, the orientation characteristic set is calculated to obtain the DOF of the PM. Then, the inverse kinematics and the velocity and acceleration of each branch chain of the mechanism are analyzed. The dynamic model of the mechanism is established according to the principle of virtual work. The workspace of the mechanism is drawn according to the constraints that have been given to the mechanism’s kinematic pairs. The singularity, dexterity, motion/force transfer performance, and maximum acceleration performance of the mechanism are also analyzed. On this basis, the kinematic and dynamic performance evaluation indexes of the mechanism are studied. Finally, the workspace and acceleration performance of the mechanism are optimized based on the differential evolution (DE) algorithm to obtain the structural parameters when the mechanism achieves optimal performance.”

    New Findings from Harbin Institute of Technology in Robotics Provides New Insights (Research On Decoding Algorithm of Split-type Magnetic Encoder)

    39-39页
    查看更多>>摘要:Investigators publish new report on Robotics. According to news originating from Harbin, People’s Republic of China, by NewsRx correspondents, research stated, “Magnetic encoders are widely used in aerospace technology, robot joints, machine tool turntables, and other equipment applications. Interval misjudgment is a problem that occurs during synthesis of multiple interval angles because of the effects of the working environment and the encoder’s power supply.” Financial support for this research came from Capital Project of the China National Academy of Mechanical Sciences. Our news journalists obtained a quote from the research from the Harbin Institute of Technology, “This article proposes an approximate arctangent algorithm based on a combination of table lookup and interpolation to improve the speed at which the arctangent solution is calculated. Finally, a unique angle interval translation decoding method is proposed that can implement mechanical angle synthesis for split magnetic encoders.” According to the news editors, the research concluded: “The algorithm’s performance is then confirmed experimentally.”

    Stellenbosch University Researchers Discuss Findings in Robotics (Using a multi-robot system for improved path planning)

    39-40页
    查看更多>>摘要:Current study results on robotics have been published. According to news reporting from Stellenbosch University by NewsRx journalists, research stated, “Numerous terrestrial robotic platforms use computational power for path planning.” Our news correspondents obtained a quote from the research from Stellenbosch University: “These platforms typically use a vision system to identify obstacles and perform path planning. In cases where the vision systems are unable to function due to larger obstacles in the area, the paths are chosen as random functions of the given terrain to explore the environment and often results in missteps and moving away from the target location. In this research, we present a multi-robot system comprised of a terrestrial robot with a tethered aerial drone. By making use of the additive overhead view, the target location can be identified, and the exploration and path planning algorithms biased, subsequently reducing the computational cost, and creating a more efficient path planning approach.” According to the news reporters, the research concluded: “Focus has been placed on the control architecture of the system.”

    Sao Joao University Hospital Researcher Provides New Insights into Artificial Intelligence (P268 Artificial Intelligence and Panendoscopy Automatic Detection of Pleomorphic Lesions in Multibrand Device-Assisted Enteroscopy)

    40-41页
    查看更多>>摘要:Investigators discuss new findings in artificial intelligence. According to news reporting originating from Porto, Portugal, by NewsRx correspondents, research stated, “Device-assisted enteroscopy (DAE) stands as the sole diagnostic and therapeutic procedure capable of thoroughly examining the entire gastrointestinal (GI) tract. Nevertheless, its diagnostic yield falls short in ensuring a cost-effective panendoscopy and there is still a significant interobserver variability during the procedure.” Our news editors obtained a quote from the research from Sao Joao University Hospital: “Multi-layered convolutional neural networks (CNN) have proven beneficial in numerous medical applications, yet there is a noticeable gap in research regarding their implementation in DAE. The aim of this study is to develop and a validate a multidevice CNN for panendoscopic detection of pleomorphic lesions (vascular lesions, hematic residues, protruding lesions, ulcers and erosions) during DAE. In a retrospective analysis of 338 DAE procedures conducted at two specialized centers, frames from 152 single-balloon enteroscopies (Fujifilm®), 172 double-balloon enteroscopies (Olympus®), and 14 motorized spiral enteroscopies (Olympus®) were used to construct and validate the CNN. The dataset, comprising 40,655 images, was divided into a training dataset (90% of images, n=36,599) and a validation dataset (10% of images, n=4,066). We conducted a 5-fold cross validation, during training dataset. Primary outcomes were sensitivity, specificity,accuracy, and area under the precision recall curve (AUC-PR). During the training dataset’s 5-fold crossvalidation, the model demonstrated a mean sensitivity of 88.7% (88.0 89.5%), specificity of 98.0% (97.8 98.1%), PPV of 92.6% (92.0 93.1%), PPN of 97.0% (96.8 97.2%), with a mean accuracy of 96.0% (95.8 96.2%). During validation dataset, the CNN presented 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and AUC-PR of 0.97. The CNN processed 124 frames per second.”

    Second Affiliated Hospital of Zhejiang University School of Medicine Reports Findings in Robotics (Bioinspired handheld time-share driven robot with expandable DoFs)

    41-42页
    查看更多>>摘要:New research on Robotics is the subject of a report. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Handheld robots offer accessible solutions with a short learning curve to enhance operator capabilities. However, their controllable degree-of-freedoms are limited due to scarce space for actuators.” Financial supporters for this research include National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, State Key Laboratory of Fluid Power and Mechatronic Systems. Our news editors obtained a quote from the research from the Second Affiliated Hospital of Zhejiang University School of Medicine, “Inspired by muscle movements stimulated by nerves, we report a handheld time-share driven robot. It comprises several motion modules, all powered by a single motor. Shape memory alloy (SMA) wires, acting as ‘nerves’, connect to motion modules, enabling the selection of the activated module. The robot contains a 202-gram motor base and a 0.8 cm diameter manipulator comprised of sequentially linked bending modules (BM). The manipulator can be tailored in length and integrated with various instruments in situ, facilitating non-invasive access and high-dexterous operation at remote surgical sites. The applicability was demonstrated in clinical scenarios, where a surgeon held the robot to conduct transluminal experiments on a human stomach model and an ex vivo porcine stomach.”

    Lambung Mangkurat University Researchers Update Current Data on Machine Learning (A Comparative Study of Machine Learning Methods for Baby Cry Detection Using MFCC Features)

    42-43页
    查看更多>>摘要:Fresh data on artificial intelligence are presented in a new report. According to news originating from Lambung Mangkurat University by NewsRx editors, the research stated, “The vocalization of infants, commonly known as baby crying, represents one of the primary means by which infants effectively communicate their needs and emotional states to adults. While the act of crying can yield crucial insights into the well-being and comfort of a baby, there exists a dearth of research specifically investigating the influence of the audio range within a baby cry on research outcomes.” Our news editors obtained a quote from the research from Lambung Mangkurat University: “The core problem of research is the lack of research on the influence of audio range on baby cry classification on machine learning. The purpose of this study is to ascertain the impact of the duration of an infant’s cry on the outcomes of machine learning classification and to gain knowledge regarding the accuracy of results F1 score obtained through the utilization of the machine learning method. The contribution is to enrich an understanding of the application of classification and feature selection in audio datasets, particulary in the context of baby cry audio. The utilized dataset, known as donate-a-cry-corpus, encompasses five distinct data classes and possesses a duration of seven seconds. The employed methodology consists of the spectrogram technique, cross-validation for data partitioning, MFCC feature extraction with 10, 20, and 30 coefficients, as well as machine learning models including Support Vector Machine, Random Forest, and Naive Bayes. The findings of this study reveal that the Random Forest model achieved an accuracy of 0.844 and an F1 score of 0.773 when 10 MFCC coefficients were utilized and the optimal audio range was set at six seconds. Furthermore, the Support Vector Machine model with an RBF kernel yielded an accuracy of 0.836 and an F1 score of 0.761, while the Naive Bayes model achieved an accuracy 0.538 and F1 score of 0.539.”

    Study Findings from Maharshi Dayanand University Update Knowledge in Pattern Recognition and Artificial Intelligence (Image Steganography Using Optimized Twin Attention-Based Convolutional Capsule Network)

    43-44页
    查看更多>>摘要:New research on pattern recognition and artificial intelligence is the subject of a new report. According to news originating from Rohtak, India, by NewsRx editors, the research stated, “The secret information hiding inside the cover image is termed image steganography, in which the secret information may be either in visual or text format.” Our news journalists obtained a quote from the research from Maharshi Dayanand University: “The concealment of secret information inside the cover image is devised by converting the information into the standard form using conventional image steganography. Here, the cover image is usually systematically altered to carry the secret binary bits after the translation of secret data into binary bits. The cover image may get distorted due to overload, making the hidden information obvious. As a result, the conventional image steganography approaches have a limited ability to conceal. Hence, this research introduces a novel image steganography using the optimized deep learning technique. For novel image steganography, an improved archerfish hunting optimization-based twin attention convolution capsule network (ImAhoTACCNet) is introduced for image steganography.” According to the news reporters, the research concluded: “Here, the proposed ImAho is utilized for modifying the tunable parameters of the TACCNet to enhance the efficiency of the image steganography process in terms of minimum mean square error (MSE) and maximal peak signal-to-noise ratio (PSNR). Besides, secret information compression and recursive encryption techniques further enhance the security of secret information. The analysis of ImAho-TACCNet based on various assessment measures like PSNR, SSIM and MSE accomplished enhanced outcomes with the values of 62.37, 0.9923, and 0.0165 for the hidden network model and 64.23, 0.9989, and 0.0125 for the extraction network model.”

    Recent Findings from Federal University Rio de Janeiro Provides New Insights into Machine Learning (Application of Machine Learning Models for Convective Meteorological Events)

    44-45页
    查看更多>>摘要:Current study results on Machine Learning have been published. According to news reporting from Rio de Janeiro, Brazil, by NewsRx journalists, research stated, “This research developed models, based on machine learning (MA), for forecasting 16 h and 4 h of occurrence of a convective meteorological event (CME), 4 h for forecasting severity and evaluating the applicability of the optimal models of 12 UTC using thermodynamic instability indices (TII) data extracted from the WRF model with two different types of parameterization configuration in an attempt to develop a 30 h CME forecast model. In the training and testing of the MA algorithms, the classic TIIs (input) were used, obtained from the atmospheric profiles of the Brasilia upper air sounding and atmospheric discharges (output) detected in the study area for the characterization of CME, considering the period from 2012 to 2017.” Financial support for this research came from Department of Airspace Control (DECEA), through the Brazilian Organization for the Scientific and Technological Development of Airspace Control (CTCEA). The news correspondents obtained a quote from the research from Federal University Rio de Janeiro, “The optimal models applied to the modeled TIIs were evaluated through statistical metrics with configuration Ⅱ obtaining significant results. For CME detection, the results showed that the best models obtained POD, 1-FAR, F-and KAPPA with values respectively greater than 0.90, 0.80, 0.90, 0.80 and BIAS ranging from 0 .89 and 1.12. For the detection of event severity, the model presented the following statistical values (in parentheses): POD (0.82), 1-FAR (0.78), F-(0.82), KAPPA (0.59) and BIAS (0.97).” According to the news reporters, the research concluded: “The results of 16 h and 4 h CME prediction hindcasts (30 days) with developed models demonstrated acceptable performance in identifying the occurrence or non-occurrence of CME and its severity for the study area.”

    Researcher at Chengdu University Has Published New Data on Machine Learning (A Machine Learning Approach to Estimating Solar Radiation Shading Rates in Mountainous Areas)

    45-46页
    查看更多>>摘要:Investigators publish new report on artificial intelligence. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research stated, “Quantification of shading effects from complex terrain on solar radiation is essential to obtain precise data on incident solar radiation in mountainous areas.” Financial supporters for this research include National Natural Science Foundation of China. The news editors obtained a quote from the research from Chengdu University: “In this study, a machine learning (ML) approach is proposed to rapidly estimate the shading effects of complex terrain on solar radiation. Based on two different ML algorithms, namely, Ordinary Least Squares (OLS) and Gradient Boosting Decision Tree (GBDT), this approach uses terrain-related factors as input variables to model and analyze direct and diffuse solar radiation shading rates. In a case study of western Sichuan, the annual direct and diffuse radiation shading rates were most correlated with the average terrain shading angle within the solar azimuth range, with Pearson correlation coefficients of 0.901 and 0.97. The GBDT-based models achieved higher accuracy in predicting direct and diffuse radiation shading rates, with R2 values of 0.982 and 0.989, respectively, surpassing the OLS-based models by 0.081 and 0.023.”

    Study Data from Technical University Provide New Insights into Machine Learning (Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis)

    46-46页
    查看更多>>摘要:Investigators publish new report on artificial intelligence. According to news reporting out of Schweinfurt, Germany, by NewsRx editors, research stated, “In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials.” Funders for this research include Bavarian State Ministry of Environment And Consumer Protection; Center For Basic Materials Efficiency (Rez) At The Bavarian Environment Agency; Publication Fund of The Technical University of Applied Sciences Wuerzburg-schweinfurt. Our news correspondents obtained a quote from the research from Technical University: “Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities ( 0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures.” According to the news reporters, the research concluded: “The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.”