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    New Findings in Machine Learning Described from Shanghai Jiao Tong University (A Context-aware Smart Product-service System Development Approach and Application Case)

    67-68页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “With advancements in information and communication technologies (ICTs), one of the epitomes of ICTs are smart product-service systems (SPSSs), which have attracted much research attention in recent years. The capture and analysis of users’ dynamic situations are crucial for generating personalized services.” Financial supporters for this research include National Key R & D Program of China, National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities. Our news editors obtained a quote from the research from Shanghai Jiao Tong University, “Thus the context awareness ability with better self-adaptation and capability to mine information on implicit needs appears to be promising to cope with this issue. However, approaches for the development of a model that integrates context analysis and SPSS have not been fully explored. Therefore, this study presents a new perspective with the incorporation of context awareness, and then proposes a conceptual framework for an SPSS with context awareness (CA-SPSS) and the approach to develop this system. Specifically, the approach consists of four steps. The first step is context acquisition to create a wireless sensor network with wearable devices to acquire the physiological data of users. Second, context modeling procedures are set up on a cloud platform, such as for data collection, storage, and analysis. Moreover, machine learning datasets with information obtained through multiple channels, such as user, physical, sensed and inferred contexts, are generated. Afterwards, neural network models are used for context reasoning to determine the modalities for the users. To produce the appropriate service contents for each user modality level, a multi-criteria decision making process is employed to form customized service strategies for context distribution. To implement the proposed approach, an SPSS case for supporting non-professional sports competitions is developed and assessed in terms of the model performance and user satisfaction. The results show that the model deployed in the system has a better performance than traditional machine learning models. According to the findings from the user experiments, the subjects are very satisfied with the personalized service bundles and intelligence, but find that the reliability is suffering.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports Outline Robotics Study Findings from University College Cork (Deep-learning-assisted Robust Detection Techniques for a Chipless Rfid Sensor Tag)

    68-69页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting originating from Cork, Ireland, by NewsRx correspondents, research stated, “In this article, we present a new approach for robust reading of identification (ID) and sensor data from chipless radio frequency ID (CRFID) sensor tags. For the first time, machine-learning (ML) and deep-learning (DL) regression modeling techniques are applied to a dataset of measured radar cross Section (RCS) data that have been derived from large-scale robotic measurements of custom-designed, 3-bit CRFID sensor tags.” Funders for this research include Science Foundation Ireland, CONNECT Centre for Future Networks and Communications, Insight Centre for Data Analytics, Enterprise Ireland funded Holistics Disruptive Technologies Innovation Fund (DTIF), European Union (EU). Our news editors obtained a quote from the research from University College Cork, “The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industrystandard robot. A dataset of 9600 electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-D convolutional neural network (1-D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of ID and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalize well on the given data. However, the 1-D CNN models outperformed the conventional ML models in the detection of ID and sensing values.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Investigators at Leuphana University Luneburg Detail Findings in Machine Learning (Case Study On Delivery Time Determination Using a Machine Learning Approach In Small Batch Production Companies)

    69-70页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting out of Luneburg, Germany, by NewsRx editors, research stated, “Delivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades.” Financial supporters for this research include Lower Saxony Ministry of Science and Culture, Lower Saxony “Vorab” of the Volkswagen Foundation, Center for Digital Innovations (ZDIN), German Federal Ministry of Economics and Technology (BMWi), through the Working Group of Industrial Research Associations (AIF). Our news journalists obtained a quote from the research from Leuphana University Luneburg, “In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    University of Waterloo Researcher Adds New Data to Research in Artificial Intelligence (Human-centric artificial intelligence)

    70-71页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news originating from the University of Waterloo by NewsRx editors, the research stated, “The essay explores the influence of artificial intelligence (AI) on society and its potential to take over jobs from humans.” Our news journalists obtained a quote from the research from University of Waterloo: “With the ongoing acceleration of technology and the increasing independence of machines, a reduced number of workers will be required. The significant progress of artificial intelligence indicates that numerous jobs such as those of paralegals, journalists, office workers, and even computer programmers are at the brink of becoming obsolete as robots and intelligent software are set to replace them. It examines the possibility of augmented intelligence and concentrates on machine learning and deep learning as possible approaches. The study indicates variables that determine how likely an occupation is to be automated and highlights the advantages of using AI to boost work productivity. The application of AI and the concerned problem associated with it has a huge impact on human society. Machine learning and deep learning are implemented to discuss the feasibility of augmented intelligence.”
      原文链接:
    • NETL
    • NSTL

    Study Findings from Worcester Polytechnic Institute Provide New Insights into Robotics (Perception and Action Augmentation for Teleoperation Assistance in Freeform Tele-manipulation)

    71-71页
    查看更多>>摘要：Investigators publish new report on robotics. According to news originating from Worcester, United States, by NewsRx editors, the research stated, “Teleoperation enables controlling complex robot systems remotely, providing the ability to impart human expertise from a distance.” The news correspondents obtained a quote from the research from Worcester Polytechnic Institute: “However, these interfaces can be complicated to use as it is difficult to contextualize information about robot motion in the workspace from the limited camera feedback. Thus, it is required to study the best manner in which assistance can be provided to the operator that reduces interface complexity and effort required for teleoperation. Some techniques that provide assistance to the operator while freeform teleoperating include: 1) perception augmentation, like augmented reality visual cues and additional camera angles, increasing the information available to the operator; 2) action augmentation, like assistive autonomy and control augmentation, optimized to reduce the effort required by the operator while teleoperating.”
      原文链接:
    • NETL
    • NSTL

    University of Toronto Researcher Details Research in Robotics (UTIL: An ultra-wideband time-difference-of-arrival indoor localization dataset)

    72-72页
    查看更多>>摘要：New research on robotics is the subject of a new report. According to news reporting from Toronto, Canada, by NewsRx journalists, research stated, “Ultra-wideband (UWB) time-differenceof- arrival (TDOA)-based localization has emerged as a promising, low-cost, and scalable indoor localization solution, which is especially suited for multi-robot applications.” Our news correspondents obtained a quote from the research from University of Toronto: “However, there is a lack of public datasets to study and benchmark UWB TDOA positioning technology in cluttered indoor environments. We fill in this gap by presenting a comprehensive dataset using Decawave’s DWM1000 UWB modules. To characterize the UWB TDOA measurement performance under various line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, we collected signal-to-noise ratio (SNR), power difference values, and raw UWB TDOA measurements during the identification experiments. We also conducted a cumulative total of around 150 min of real-world flight experiments on a customized quadrotor platform to benchmark the UWB TDOA localization performance for mobile robots. The quadrotor was commanded to fly with an average speed of 0.45 m/s in both obstacle-free and cluttered environments using four different UWB anchor constellations.”
      原文链接:
    • NETL
    • NSTL
    • Sage

    Badji Mokhtar University Researcher Reports Research in Pattern Recognition and Artificial Intelligence (Boosting Multi-Label Classification Performance Through Meta-Model)

    73-73页
    查看更多>>摘要：Investigators publish new report on pattern recognition and artificial intelligence. According to news originating from Annaba, Algeria, by NewsRx correspondents, research stated, “Multilabel classification problem, where each instance can be associated with multiple labels, has received considerable attention from machine learning community.” The news journalists obtained a quote from the research from Badji Mokhtar University: “To address the inherent challenges of multi-label classification including data imbalance, label dependence, and high dimensionality, ensemble approaches have been developed, gaining popularity across various real-world applications. This paper proposes a novel ensemble method called ConfBoost that addresses these challenges and enhances the generalization ability of learning systems. ConfBoost which is a meta-model based on a weighted stacking paradigm using local confidence, combines heterogeneous and complementary ensembles of multi-label classifiers. The proposed approach achieves two main objectives: Firstly, by focusing on label weights based on their confidence scores, the model can generate more relevant predictions and enhance the accuracy at the base-level by mitigating the impact of irrelevant labels during the stacking process. Moreover, assigning higher weights to certain labels exhibits better discrimination and adaptability to capture complex label relationships. Second, applying adjusted thresholds enables the model to generate predictions adapted to the specific characteristics of each label, effectively addressing imbalanced label distributions.”
      原文链接:
    • NETL
    • NSTL
    • World Scientific

    Studies from Beijing Institute of Technology Update Current Data on Robotics (Learning Robust Locomotion for Bipedal Robot Via Embedded Mechanics Properties)

    74-74页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Reinforcement learning (RL) provides much potential for locomotion of legged robot. Due to the gap between simulation and the real world, achieving sim-to-real for legged robots is challenging.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Ministry of Education, China - 111 Project. Our news editors obtained a quote from the research from the Beijing Institute of Technology, “However, the support polygon of legged robots can help to overcome some of these challenges. Quadruped robot has a considerable support polygon, followed by bipedal robot with actuated feet, and point-footed bipedal robot has the smallest support polygon. Therefore, despite the existing sim-to-real gap, most of the recent RL approaches are deployed to the real quadruped robots that are inherently more stable, while the RL-based locomotion of bipedal robot is challenged by zero-shot sim-to-real task. Especially for the point-footed one that gets better dynamic performance, the inevitable tumble brings extra barriers to sim-to-real task. Actually, the crux of this type of problem is the difference of mechanics properties between the physical robot and the simulated one, making it difficult to play the learned skills well on the physical bipedal robot. In this paper, we introduce the embedded mechanics properties (EMP) based on the optimization with Gaussian processes to RL training, making it possible to perform sim-to-real transfer on the BRS1-P robot used in this work, hence the trained policy can be deployed on the BRS1-P without any struggle.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Peking University Reports Findings in Machine Learning (Interpretable Machine Learning To Accelerate the Analysis of Doping Effect on Li/Ni Exchange in Ni-Rich Layered Oxide Cathodes)

    75-75页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “In Ni-rich layered oxide cathodes, one effective way to adjust the performance is by introducing dopants to change the degree of Li/Ni exchange. We calculated the formation energy of Li/Ni exchange defects in LiNiMnXO with different doping elements X, using first-principles calculations.” Our news journalists obtained a quote from the research from Peking University, “We then proposed an interpretable machine learning method combining the Random Forest (RF) model and the Shapley Additive Explanation (SHAP) analysis to accelerate identification of the key factors influencing the formation energy among the complex variables introduced by doping. The valence state of the doping element effectively regulates Li/Ni exchange defects through changing the valence state of Ni and the strength of the superexchange interaction, and COOP and Mag were proposed as two indicators to assess superexchange interaction.”
      原文链接:
    • NETL
    • NSTL

    Researchers’ from Lanzhou Jiaotong University Report Details of New Studies and Findings in the Area of Support Vector Machines (Landslide hazard assessment based on improved Stacking model)

    75-76页
    查看更多>>摘要：Current study results on have been published. According to news reporting from Lanzhou, People’s Republic of China, by NewsRx journalists, research stated, “The early warning of landslides is crucial in mitigating the losses caused by frequent and abrupt landslide disasters along the railway.” Our news editors obtained a quote from the research from Lanzhou Jiaotong University: “The scientific construction of an evaluation model is pivotal in conducting a comprehensive landslide hazard assessment. Using a railway section in Ya’an City as a case study, an improved Stacking model was developed to assess landslide hazard by selecting eight evaluation factors and employing support vector machines, random forests, K-neighborhood, and naive Bayesian learning. Logical regression was utilized as a meta learning tool to evaluate the model’s performance. To address the issue of a limited number of input samples for the meta learner, the proposed approach incorporates reduced dimensionality data from the original dataset as input for the meta learner. This is based on the output of the base learner, resulting in the establishment of an improved Stacking model. The ROC curve is used to verify the accuracy of the model, compare the accuracy of the Stacking model and the single model before and after the improvement, and generate the risk zoning map of the study area.”
      原文链接:
    • NETL
    • NSTL
    • Tamkang Univ