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    Research from University of Southampton Yields New Study Findings on Machine Learning (Calibration of a Low-Cost Methane Sensor Using Machine Learning)

    39-39页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting out of Southampton, United Kingdom, by NewsRx editors, research stated, "In order to combat greenhouse gas emissions, the sources of these emissions must be understood." Funders for this research include Uk Research And Innovation. The news journalists obtained a quote from the research from University of Southampton: "Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0-200 ppm methane, 5-30 ℃ and 40-80% relative humidity."
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Findings from University of Badji Mokhtar Update Understanding of Machine Translation (Low Resource Arabic Dialects Transformer Neural Machine Translation Improvement Through Incremental Transfer of Shared Linguistic Features)

    40-40页
    查看更多>>摘要：New research on Machine Translation is the subject of a report. According to news originating from Annaba, Algeria, by NewsRx correspondents, research stated, "Neural machine translation (NMT) is a complex process that deals with many grammatical complexities. Today, transfer learning (TL) has emerged as a leading method in machine translation, enhancing accuracy with ample source data for limited target data." Financial supporters for this research include Direction Generale de la Recherche Scientifique et du Developpement Technologique (DGRSDT), Laboratoire de Recherche Informatique (LRI). Our news journalists obtained a quote from the research from the University of Badji Mokhtar, "Yet, low-resource languages such as Arabic dialects lack substantial source data. This study aims to enable an NMT model, trained on a sparse Arabic dialect corpus, to translate a precise dialect with a limited corpus, addressing this gap. This paper introduces an incremental transfer learning approach tailored for translating low-resource language. The method utilizes various related language corpora, employing an incremental fine-tuning strategy to transfer linguistic features from a grand-parent model to a child model. In our case, Knowledge is transferred from a broad set of Arabic dialects to the Maghrebi dialects subset and then to specific low-resource dialects such as Algerian, Tunisian, and Moroccan, employing Transformer and attentional sequence-to-sequence models. The evaluation of the proposed strategy on Algerian, Tunisian, and Moroccan dialects demonstrates superior translation performance compared to traditional TL methods. Using the Transformer model, it shows improvements of 80%, 62%, and 58% for Algerian, Tunisian, and Moroccan dialects, respectively."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Reports Summarize Machine Learning Study Results from Chennai Institute of Technology (Prediction of Dragon King Extreme Events Using Machine Learning Approaches and Its Characterizations)

    41-41页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Chennai, India, by NewsRx journalists, research stated, "In this study, we employ a machine learning approach to infer the complex dynamics of dragon king extreme events. Specifically, we utilize two distinct machine learning techniques: Echo State Network and Gated Recurrent Unit." Financial support for this research came from Center for Nonlinear Systems, Chennai Institute of Technology (CIT) , India. The news reporters obtained a quote from the research from the Chennai Institute of Technology, "To do so, we consider three distinct systems for predicting dragon kings behavior: a pair of electronic circuits, coupled logistic maps, and Hindmarsh-Rose neurons. We discover that a few actual time series data points, accompanied by their corresponding system parameters, are adequate to capture dragon kings nature. Initially, we demonstrate that systems under consideration possess characteristics of extreme events, with signal amplitudes greater than the critical amplitude threshold. The presence of dragon kings within these observed extreme events is discerned by the emergence of hump-like behavior in the tail distribution of the probability density function and the statistical measures."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Lorraine Reports Findings in Gliomas (Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma)

    41-42页
    查看更多>>摘要：New research on Oncology - Gliomas is the subject of a report. According to news reporting originating from Nancy, France, by NewsRx correspondents, research stated, "This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation." Our news editors obtained a quote from the research from the University of Lorraine, "Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716-0.784], p = 0.06)."
      原文链接:
    • NETL
    • NSTL

    North University of China Researcher Releases New Data on Robotics (Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning)

    42-43页
    查看更多>>摘要：Fresh data on robotics are presented in a new report. According to news originating from Taiyuan, People's Republic of China, by NewsRx correspondents, research stated, "In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger." Financial supporters for this research include Natural Science Foundation of Shanxi Province, China. The news journalists obtained a quote from the research from North University of China: "At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high."
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Study Data from Northwest A&F University Update Knowledge of Machine Learning (A Cooperative Regulation Method for Greenhouse Soil Moisture and Light Using Gaussian Curvature and Machine Learning Algorithms)

    43-44页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating from Shaanxi, People's Republic of China, by NewsRx correspondents, research stated, "Soil moisture (SM) exerts a significant impact on crop growth, interacting with environmental factors such as temperature, photosynthetic photon flux density (PPFD), and CO2, ultimately affecting crop photosynthesis (Pn). This study employs a nested experimental design to investigate the photosynthetic activity of cucumber seedlings under diverse environmental conditions and establishes a support vector regression (SVR) model for Pn prediction." Financial support for this research came from Shaanxi Provincial Key Research and Development Project on Research and Demonstration of Intelligent Management Information System for Agricultural Irrigation (CN).
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Robotics Research Reported from Kyonggi University (Applying Quantitative Model Checking to Analyze Safety in Reinforcement Learning)

    44-45页
    查看更多>>摘要：Investigators publish new report on robotics. According to news reporting originating from Gyeonggi Do, South Korea, by NewsRx correspondents, research stated, "Reinforcement learning (RL) is rapidly used in safety-centric applications. However, many studies focus on generating optimal policy that achieves maximum rewards." Funders for this research include Institute of Information & Communications Technology Planning & Evaluation. Our news journalists obtained a quote from the research from Kyonggi University: "While maximum rewards are beneficial, safety constraints and non-functional requirements must also be considered in safety-centric applications to avoid dangerous situations. For example, in the case of food delivery robots in restaurants, RL should be used not only to find optimal policy that response to all customer requests through maximum rewards but also to consider safety constraints such as collision avoidance and nonfunctional requirements such as battery saving. In this paper, we investigated the fulfillment of safety constraints and non-functional requirements of learning models generated through RL with quantitative model checking. We experimented with various time steps and learning rates required for RL, targeting restaurant delivery robots. The functional requirement of these robots is to process all customer order requests, and the non-functional requirements are the number of steps and battery consumption to complete the task. Safety constraints include the amount of collision and the probability of collision. Through these experiments, we made three important findings. First, learning models that obtain maximum rewards may have a low degree of achievement of non-functional requirements and safety constraints. Second, as safety constraints are met, the degree of achievement of non-functional requirements may be low. Third, even if the maximum reward is not obtained, sacrificing non-functional requirements can maximize the achievement of safety constraints."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Investigators from Department of Electronic Computer & Biomedical Engineering Report New Data on Machine Learning (Interpretability of Machine Learning: Recent Advances and Future Prospects)

    45-46页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Toronto, Canada, by NewsRx journalists, research stated, "The proliferation of machine learning (ML) has drawn unprecedented interest in the study of various multimedia contents such as text, image, audio, and video, among others. Consequently, understanding and learning MLbased representations have taken center stage in knowledge discovery in intelligent multimedia research and applications." The news reporters obtained a quote from the research from the Department of Electronic Computer & Biomedical Engineering, "Nevertheless, the black-box nature of contemporary ML, especially in deep neural networks, has posed a primary challenge for ML-based representation learning. To address this black-box problem, studies on the interpretability of ML have attracted tremendous interest in recent years. This article presents a survey on recent advances in and future prospects for the interpretability of ML, with several application examples pertinent to multimedia computing, including text-image cross-modal representation learning, face recognition, and the recognition of objects."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Northwestern Polytechnic University Details Findings in Robotics (Adaptive Detumbling Control of Dual-arm Space Robot After Capturing Non-cooperative Target)

    46-47页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Xi'an, People's Republic of China, by NewsRx correspondents, research stated, "This research focuses on the detumbling planning and control method for the dual-arm space robot postcapturing a non-cooperative tumbling target, which takes the uncertainty of target's inertial parameters and generalized input constraints of robotic system into consideration. Firstly, based on the concept of task compatibility, an efficient detumbling strategy without optimization algorithm is pro-posed, where the target's desired acceleration is in the opposite direction of its velocity with the magnitude determined by scaling factor." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Science and Technology on Aerospace Flight Dynamics Laboratory, Fundamental Research Funds for the Central Universities, Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Findings on Machine Learning Described by Investigators at University of Manitoba (Short-term Voltage Instability Prediction Using Pre-identified Voltage Templates and Machine Learning Classifiers)

    47-48页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news originating from Winnipeg, Canada, by NewsRx correspondents, research stated, "System operators use different procedures to detect disturbances which can lead to short-term voltage instability. These schemes are designed using the domain knowledge and generally specific for the corresponding system." Our news journalists obtained a quote from the research from the University of Manitoba, "The design procedure of such schemes is heuristic and cannot be directly applied to any other power system. This paper proposes a data-driven approach to predict short-term voltage stability using set of post-disturbance voltage templates and a machine learning-based classifier. In this scheme, a set of voltage templates corresponding to stable/unstable events is identified for selected buses using a suitably generated training data set. The proximity of real-time voltage trajectory to each of the template is computed and input to a trained machine learning classifier to predict the short-term voltage stability status. The proposed approach is validated using IEEE Nordic test system. The investigations show that this method provides accurate short-term voltage stability predictions."
      原文链接:
    • NETL
    • NSTL
    • Elsevier