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    Studies from University of Texas Austin Describe New Findings in Machine Learning (Isop Plus : Machine Learning-assisted Inverse Stack-up Optimization for Advanced Package Design)

    20-21页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting originating in Austin, Texas, by NewsRx journalists, research stated, “The future of computing requires heterogeneous integration, including the recent adoption of chiplet methodology, where high-speed cross-chip interconnects and packaging are critical for the overall system performance. As an example of advanced packaging, a high-density interconnect (HDI) printed circuit board (PCB) has been widely used in complex electronics ranging from cell phones to computing servers.” Financial support for this research came from National Science Foundation (NSF). The news reporters obtained a quote from the research from the University of Texas Austin, “A modern HDI PCB may have over 20 layers, each with its unique material properties and geometrical dimensions, i.e., stack-up, to meet various design constraints and performance requirements. Stack-up design is usually done manually in the industry, where experienced designers may devote many hours adjusting the physical dimensions and materials in order to meet the desired specifications. This process, however, is timeconsuming, tedious, and suboptimal, largely depending on the designer’s expertise. In this article, we propose to automate the stack-up design with a new framework, ISOP+, using machine learning (ML) for inverse stack-up optimization for advanced package design with adaptive weight adjustment and multilevel optimization. Given a target design specification, ISOP+ automatically searches for ideal stack-up design parameters while optimizing performance. A novel ML-assisted hyperparameter optimization method is developed to make the search efficient and reliable. Experimental results demonstrate that ISOP+ is better in figure-of-merit (FoM) than conventional simulated annealing and Bayesian optimization algorithms, with all our design targets met with a shorter runtime.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Findings on Machine Learning Discussed by Investigators at Tsinghua University (Vibration-based Health Monitoring of the Offshore Wind Turbine Tower Using Machine Learning With Bayesian Optimisation)

    21-22页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Structural health monitoring of towers for offshore wind turbines (OWTs) is critical because they serve as support structures. Although the modal parameters of the tower can be identified through operational modal analysis, evaluating the realistic characteristics of structures is challenging, and the modal parameters are susceptible to fluctuations owing to shifting environmental and operational conditions (EOCs).” Financial supporters for this research include China Three Gorges Corporation, National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Tsinghua University, “In this study, we aim to improve the vibration-based structural health monitoring strategy for monitoring OWT towers. Remotesensing products from ERA5, which provide information regarding the marine state, are used to extend the data source. Machine learning (ML) with Bayesian optimisation is utilised to minimise the effects of EOCs on modal parameters. We demonstrate that the modal parameters of the tower are significantly influenced by the EOCs by applying the developed approach to an offshore wind turbine. Moreover, the effects of the EOCs on the modal parameters are minimised by applying ML models with Bayesian optimisation. A comparison between the monitoring divided into different operating conditions and the monitoring of all operating conditions indicate that there is minimal room for performance improvement of ML models with thorough condition segmentation.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Research from Zhejiang Gongshang University Has Provided New Study Findings on Artificial Intelligence (A Study on the Influence of Artificial Intelligence on Image Art Design)

    22-23页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting out of Zhejiang Gongshang University by NewsRx editors, research stated, “With the continuous development of artificial intelligence technology, the field of video art design is undergoing unprecedented changes.” Our news reporters obtained a quote from the research from Zhejiang Gongshang University: “The purpose of this paper is to deeply discuss the influence of artificial intelligence era on video art design. The artificial intelligence generation system can assist in image generation, video processing, 3D modelling and other work, which greatly improves the creation efficiency. At the same time, these systems provide inspiration and creativity to artists by learning and analysing a large number of artworks. In terms of innovation, AI breaks traditional art and design patterns and supports more open and diverse expression. Machine learning-based generation systems can create stunning, novel images and videos that demonstrate creativity beyond imagination. Artificial intelligence inspires us to reflect on the subjectivity of aesthetic standards and to appreciate more the unique aesthetic orientation of algorithms.”
      原文链接:
    • NETL
    • NSTL

    Reports Summarize Robotics Findings from Beihang University (Adaptive Consensus Tracking Control for Robotic Manipulators With Nonlinear Time-varying Fault-tolerant Actuator and Unknown Control Input Directions)

    23-24页
    查看更多>>摘要：Researchers detail new data in Robotics. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The consensus tracking mechanism of a multi-agent system consisting of third-order nonlinear single joint manipulators was investigated in this study based on the pertinent industrial context. Unlike many previous studies on consensus control that presumed a known control direction, this investigation addressed the complex challenges posed by unknown control directions and time-varying actuators.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Beihang University, “Specifically, we explored both time-invariant and time-varying actuator faults in scenarios wherein all parameters are unknown. The Nussbaum function was utilized to resolve time-varying nonlinear fault-tolerance and unknown control direction problems. In the design process of the control law, the backstepping method combining with Nussbaum function is adopted to address unmatched nonlinearity and unmatched uncertainty system, and the radial basis function neural network is used to approximate the unknown parameters The resulting control law allows all robotic manipulators to track the angle and angular velocity of the leader.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    University of Western Macedonia Researcher Details Findings in Artificial Intelligence (A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision)

    24-25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 artificial intelligence. According to news originating from Kozani, Greece, by NewsRx correspondents, research stated, “Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions.” The news journalists obtained a quote from the research from University of Western Macedonia: “This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation.”
      原文链接:
    • NETL
    • NSTL

    Data from Jiangsu University Advance Knowledge in Machine Learning (A highly ductile carbon material made of triangle rings: A study of machine learning)

    25-26页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from Jiangsu, People’s Republic of China, by NewsRx correspondents, research stated, “Carbon materials exhibit diverse mechanical properties, from hard diamond to soft graphite. However, carbon materials with high ductility are rare, because of strong covalent bonds between carbon atoms.” Financial supporters for this research include National Natural Science Foundation of China. Our news journalists obtained a quote from the research from Jiangsu University: “Here, we propose that the structures of triangular lattice have higher ductility than those of hexagonal or quadrangle lattice. A two-dimensional (2D) carbon network, named a carbon Kagome lattice (CKL), is used as an example to verify the point. The carbon structure has a Kagome lattice similar to the triangular lattice. Because empirical potentials cannot well simulate mechanical properties of carbon structures with triangular carbon rings, we work out a neuroevolution potential (NEP) based on a machine learning method. Structural evolution and phase transition under strain have been studied based on the NEP. The results indicate that the ductility of 2D CKL can approach 80%, and even at a high temperature, the ductility can reach 48%. The ductile values are the highest in all 2D crystal materials except the molecular materials.”
      原文链接:
    • NETL
    • NSTL

    Research Results from Hubei University of Technology Update Understanding of Robotics (Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer)

    26-26页
    查看更多>>摘要：Researchers detail new data in robotics. According to news originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research stated, “This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot.” Financial supporters for this research include National Natural Science Foundation of China. Our news correspondents obtained a quote from the research from Hubei University of Technology: “GSGWO has been improved from the gray wolf optimizer (GWO), which provide slow convergence speed and easy to fall into local optimum, especially without obstacle-crossing function. Firstly, aiming at the defects of GWO, the chaotic map is introduced to enrich the initial population and improve the convergence factor curve. Then, the convergence strategy of the golden sine optimizer is introduced to improve the shortcomings of GWO, such as insufficient convergence speed in the later stage and the ease with which it falls into the local optimum. Finally, by adjusting the working environment model, path generation method and fitness function, the path-planning problem of the obstacle-crossing robot is adapted. In order to verify the feasibility of the algorithm, four standard test functions and three different scale environment models are selected for simulation experiments. The results show that in the performance test of the algorithm, the GSGWO has higher convergence speed and accuracy than the GWO under different test functions.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Reports from Maastricht University Provide New Insights into Machine Learning (Towards Adaptive Support for Self-regulated Learning of Causal Relations: Evaluating Four Dutch Word Vector Models)

    27-28页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Maastricht, Netherlands, by NewsRx correspondents, research stated, “Advances in computational language models increasingly enable adaptive support for self-regulated learning (SRL) in digital learning environments (DLEs; eg, via automated feedback). However, the accuracy of those models is a common concern for educational stakeholders (eg, policymakers, researchers, teachers and learners themselves).” Funders for this research include Nationaal Regieorgaan Onderwijsonderzoek, Netherlands Initiative for Education Research (PROO) under the Dutch Research Council (NRO). Our news editors obtained a quote from the research from Maastricht University, “We compared the accuracy of four Dutch language models (ie, spaCy medium, spaCy large, FastText and ConceptNet NumberBatch) in the context of secondary school students’ learning of causal relations from expository texts, scaffolded by causal diagram completion. Since machine learning relies on human-labelled data for the best results, we used a dataset with 10,193 students’ causal diagram answers, compiled over a decade of research using a diagram completion intervention to enhance students’ monitoring of their text comprehension. The language models were used in combination with four popular machine learning classifiers (ie, logistic regression, random forests, support vector machine and neural networks) to evaluate their performance on automatically scoring students’ causal diagrams in terms of the correctness of events and their sequence (ie, the causal structure). Five performance metrics were studied, namely accuracy, precision, recall, F1 and the area under the curve of the receiver operating characteristic (ROC-AUC). The spaCy medium model combined with the neural network classifier achieved the best performance for the correctness of causal events in four of the five metrics, while the ConceptNet NumberBatch model worked best for the correctness of the causal sequence.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    New Findings from North China University of Water Resources and Electric Power Describe Advances in Support Vector Machines (An Ensemble Model for Monthly Runoff Prediction Using Least Squares Support Vector Machine Based On Variational Modal ...)

    28-29页
    查看更多>>摘要：Researchers detail new data in Support Vector Machines. According to news reporting from Zhengzhou, People’s Republic of China, by NewsRx journalists, research stated, “In order to enhance the runoff prediction accuracy, an ensemble prediction model based on least squares support vector machine (LSSVM) is proposed by including variational mode decomposition (VMD), dung beetle optimization algorithm (DBO), and error correction (EC) strategy. First, the monthly runoff time series is decomposed using DBO-optimized VMD (DVMD), yielding a series of intrinsic mode functions (IMF) series and a residual (Res).” Funders for this research include Special project for collaborative innovation of science and technology, Henan Province University Scientific and Technological Innovation Team. The news correspondents obtained a quote from the research from the North China University of Water Resources and Electric Power, “Then, the LSSVM based on DBO optimization predicts each sub-series column and residual. The final forecast results are achieved after the preliminary forecast results have been stacked and corrected by the DBO-LSSVM prediction error. To verify the reliability of the proposed model, it is applied to the monthly runoff prediction of the Xiajiang hydrological station in the Ganjiang River Basin, the Hongshanhe hydrological station in the Heihe River Basin, and the Jiayugaun hydrological station in the Heihe River Basin. The proposed model is evaluated using four evaluation indicators: RMSE, MAPE, NSEC, and R, and is compared with SVM, LSSVM, PSOLSSVM, DBO-LSSVM, EEMD-LSSVM, CEEMDAN-LSSVM, DVMD-LSSVM, EEMD-DBO-LSSVM, CEEMDAN-DBOLSSVM, and DVMD-DBOLSSVM. Results show that the DVMD-DBO-LSSVM-EC model has the highest accuracy. During the test period, the NSEC of Xiajiang hydrological station is 0.9829, R is 0.9921, the NSEC of Hongshanhe hydrological station is 0.9981, R is 0.9991, and the NSEC of Jiayugaun hydrological station is 0.9772, R is 0.9897. The prediction effect of the model on the extreme value of the three stations after adding the error correction strategy has increased by 45.14%, 62.22%, and 29.49%, respectively, compared with the previous, which is closer to the actual value.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports Outline Robotics Study Findings from University of Rey Juan Carlos (Model Optimization In Deep Learning Based Robot Control for Autonomous Driving)

    29-30页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting originating from Madrid, Spain, by NewsRx correspondents, research stated, “Deep learning (DL) has been successfully used in robotics for perception tasks and end-to-end robot control. In the context of autonomous driving, this work explores and compares a variety of alternatives for model optimization to solve the visual lane-follow application in urban scenarios with an imitation learning approach.” Financial support for this research came from Google Incorporated. Our news editors obtained a quote from the research from the University of Rey Juan Carlos, “The optimization techniques include quantization, pruning, fine-tuning (retraining), and clustering, covering all the options available at the most common DL frameworks. TensorRT optimization for specific cuttingedge hardware devices has been also explored. For the comparison, offline metrics such as mean squared error and inference time are used. In addition, the optimized models have been evaluated in an online fashion using the autonomous driving state-of-the-art simulator CARLA and an assessment tool called Behavior Metrics, which provides holistic quantitative fine-grain data about robot performance. Typically the performance of robot applications depends both on the quality of the control decisions and also on their frequency. The studied optimized models significantly increase inference frequency without losing decision quality. The impact of each optimization alone has also been measured. This speed-up allows us to successfully run DL robot-control applications even in limited computing hardware.”
      原文链接:
    • NETL
    • NSTL
    • IEEE