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    New Support Vector Machines Findings Has Been Reported by Investigators at Jilin Agricultural University [Detection and Classification of Pesticide Residues In Dandelion (Taraxacum Officinale L.) By Electronic Nose Combined With Chemometric ...]

    30-31页
    查看更多>>摘要：A new study on Machine Learning - Support Vector Machines is now available. According to news reporting out of Changchun, People’s Republic of China, by NewsRx editors, research stated, “In this study, for the first time establish a suitable pesticide residue detection system for dandelion (Taraxacum officinale L.) based on electronic nose to determine and study the concentration of pesticide residue in dandelion. Dandelions were sprayed with different concentrations of pesticides (avermectin, trichlorfon, deltamethrin, and acetamiprid), respectively.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Science-Technology Development Plan Project of Jilin Province, The 13th Five-Year Plan Scientific Research Foundation of the Education Department of Jilin Province. Our news journalists obtained a quote from the research from Jilin Agricultural University, “Data collection was performed by application of an electronic nose equipped with 12 metal oxide semiconductor (MOS) sensors. Data analysis was conducted using different methods including BP neural network and random forest (RF) as well as the support vector machine (SVM). The results showed the superior effectiveness of SVM in discrimination and classification of non-exceeding maximum residue limits (MRLs) and exceeding MRLs standards. Moreover, the model trained by SVM has the best performance for the classification of pesticide categories in dandelion, and the classification accuracy was 91.7%.”
      原文链接:
    • NETL
    • NSTL
    • Chinese Acad Agricultural Engineering

    Findings from Polytechnic University of Madrid in Artificial Intelligence Reported (Testing Deep Learning Recommender Systems Models On Synthetic Gan-generated Datasets)

    31-32页
    查看更多>>摘要：Current study results on Machine Learning - Artificial Intelligence have been published. According to news reporting originating from Madrid, Spain, by NewsRx correspondents, research stated, “The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets.” Financial supporters for this research include Spanish Government, Comunidad de Madrid under Convenio Plurianual with the Universidad Politecnica de Madrid. Our news editors obtained a quote from the research from the Polytechnic University of Madrid, “We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data.”
      原文链接:
    • NETL
    • NSTL
    • Univ Int Rioja-Unir

    Study Data from Stanford University Provide New Insights into Robotics and Automation (Disentangled Neural Relational Inference for Interpretable Motion Prediction)

    32-32页
    查看更多>>摘要：Current study results on Robotics - Robotics and Automation have been published. According to news reporting out of Stanford, California, by NewsRx editors, research stated, “Effective interaction modeling and behavior prediction of dynamic agents play a significant role in interactive motion planning for autonomous robots. Although existing methods have improved prediction accuracy, few research efforts have been devoted to enhancing prediction model interpretability and out-of-distribution (OOD) generalizability.” Financial support for this research came from Honda Research Institute, USA, Inc. Our news journalists obtained a quote from the research from Stanford University, “This work addresses these two challenging aspects by designing a variational auto-encoder framework that integrates graphbased representations and time-sequence models to efficiently capture spatio-temporal relations between interactive agents and predict their dynamics. Our model infers dynamic interaction graphs in a latent space augmented with interpretable edge features that characterize the interactions. Moreover, we aim to enhance model interpretability and performance in OOD scenarios by disentangling the latent space of edge features, thereby strengthening model versatility and robustness. We validate our approach through extensive experiments on both simulated and real-world datasets.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Zhejiang Sci-Tech University Researcher Adds New Study Findings to Research in Machine Learning (Experimental study on the transition modes of falling film between horizontal 3D finned tubes and their transitional Reynolds number prediction ...)

    33-33页
    查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting out of Hangzhou, People’s Republic of China, by NewsRx editors, research stated, “To investigate the effect of different falling film modes on the heat transfer performance of three-dimensional (3D) finned tubes in a falling film heat exchanger, the falling film transition modes are experimentally investigated by observing the flow modes on 3D finned tubes and determining the Reynolds numbers of flow transition modes.” Our news correspondents obtained a quote from the research from Zhejiang Sci-Tech University: “A test facility, which contains an array of three horizontal test tubes, is constructed to study the effect of tube spacing and fin structure on the falling film Reynolds number (Re). The results show that tube spacing and fin structure significantly affect the Re and observed mode. With the increase in tube spacing, the Re overall shows an increasing trend for the four transition modes, especially for the transition between the column and the column-sheet mode. With the increase in the ratio for fin structure parameters, the Re overall shows a downward trend, and this phenomenon is more evident with the increase in the tube spacing.”
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Data from Ardhi University Update Knowledge in Machine Learning (Fine-scale mapping of residential land price using machine-learning: An experimental study in the city dominated by informal land markets)

    34-34页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Ardhi University by NewsRx correspondents, research stated, “Context and backgound Fine-scale mapping of residential land price (RLP) is essential to the understanding of residential land market dynamics and improving urban planning. However, such cartographic resources and experimental studies to map RLP at fine-scale in Sub-Saharan African cities are limited as a result of informal land market dominance in shaping the growth and expansion of most of the cities in the region.” Our news correspondents obtained a quote from the research from Ardhi University: “Goal and The study seeks to establish an optimized ensemble machine-learning method for mapping RLP at grid-level in Dar-es-Salaam City, Tanzania. The study utilizes RLPs collected at the sub-ward level via the survey method and uses open data such as Nighttime Lights (NTL), and amenities coordinates points from OpenStreetMap. This paper explores the ability of two (2) ensemble machine learning methods (ie. Random Forest Regression (RF-R) and XGBoost Regression) for mapping RLP at grid-level. Results found that RF-R was slightly superior to XGBoost Regression and was used to map RLP at fine-scale. The relative importance of explanatory variables in the RF-R model demonstrated that NTL was by far the most important determinant for the RLP spatial distribution in Dar-es-Salaam. NTL literature presents it as a proxy for socioeconomic variables such as Gross Domestic Product (GDP) and population, hence describing typical characteristics of informal land markets.”
      原文链接:
    • NETL
    • NSTL

    Studies from University of Perugia Add New Findings in the Area of Robotics (Vision-based Topological Localization for Mavs)

    34-35页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting originating in Perugia, Italy, by NewsRx journalists, research stated, “Vision-based topological localization is recently emerging as a promising alternative to metric pose estimation techniques in robotic navigation systems. Contrarily to the latter, which suffer from a quick degradation of their performance under non-ideal conditions (e.g., scenes with poor illumination and low amount of textures), topological localization trades off precise metric positioning with a more robust and higher-level location representation.” Financial support for this research came from HiPeRT s.r.l. The news reporters obtained a quote from the research from the University of Perugia, “State-ofthe- art works in this direction, however, often neglect the spatiotemporal relationships between poses that are naturally induced by robotic navigation. Furthermore, these techniques are nearly unexplored for autonomous flying platforms. Inspired by these considerations, in this work, we propose a vision-based topological localization approach designed for Micro Aerial Vehicles (MAVs) applications. Our strategy exploits the framework of graph recurrent neural networks to model the spatial and temporal dependencies and estimate the location of the robot with respect to a topological graph representing the environment.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Data from Nanyang Technological University Illuminate Findings in Robotics (Learning Relation In Crowd Using Gated Graph Convolutional Networks for Drl-based Robot Navigation)

    35-36页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Singapore, Singapore, by NewsRx correspondents, research stated, “Deep reinforcement learning (DRL) frameworks have shown their remarkable effectiveness in learning navigation policy for the mobile robot navigating in a human crowded environment. Moreover, attention mechanisms coupled with DRL allows the robot to identify neighbors with different level of influence and incorporate them into the robot’s decision.” Financial support for this research came from National Research Foundation, Singapore. Our news editors obtained a quote from the research from Nanyang Technological University, “However, as the crowd density increases, attention mechanisms may fail to identify critical neighbors which can lead to significant drops in navigation efficiency. In this work, we aim to address this limitation by encoding both human-human and human-robot interaction using a special class of Graph Convolutional Networks (GCN) known as Message-Passing GCN (MP-GCN). In contrast to existing methods, where attention between robot and humans are encoded uniformly, the proposed approach named MP-GatedGCN-RL encodes asymmetric interactions using the combination of novel message-passing function and edge-wise gating mechanisms. We evaluate our approach on the simulated environments of ETH/UCY pedestrians datasets consisting of different scenarios like collision avoidance, group forming, diverging, crossing, and so on. Experimental results demonstrate that our proposed method outperforms the conventional benchmark dynamic avoidance method ORCA with a 20.6% increase in success rate and a 9.1% reduction in navigation time.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Studies from Science University of Malaysia Provide New Data on Machine Learning (Improved Tropical Cyclone Wind Speed Estimation for Microwave Altimeter Using Machine Learning)

    36-37页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news originating from George Town, Malaysia, by NewsRx correspondents, research stated, “Satellite altimeters can provide excellent global wind speed at 10 m above the sea surface (U10), however, the U10 becomes inaccurate and difficult to measure in tropical cyclone conditions. The violent wind, rough waves and torrential rain manifest an exceptionally complex ocean-atmospheric environment for wind estimation.” Financial supporters for this research include Universiti Sains Malaysia, Ministry of Education, Malaysia. Our news journalists obtained a quote from the research from the Science University of Malaysia, “Although the backscatter signal is measured equally well in normal condition, the interpretation is not straightforward in tropical cyclones that requires complex associations with ocean-atmospheric geophysical variables. Typical U10 regression model developed in normal atmospheric conditions would inevitably reduce the estimation quality and encounter high modelling uncertainties from high dimensional input data that provide ill-posed solutions in extreme U10 estimation. However, other secondary parameters simultaneously measured by the altimeter have properties that convey additional atmospheric information to enhance U10 estimation accuracy in storm condition. Therefore, the present study proposes machine learning (ML) approaches based on artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR) to integrate the multi-dimensional parameters and provide accurate U10 estimates for different parameter combination. Results suggest that the GPR method, considering atmospheric and sea surface related parameters, can provide the highest accuracy of U10 up to 35 ms-1 with quality perseverance against rain contamination.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports Outline Robotics Findings from University of Cauca (Generador 3d De Trayectorias Libres De Colisiones Para Un Manipulador Ur3e Con Pinza Blanda)

    37-38页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting originating in Popayan, Colombia, by NewsRx journalists, research stated, “Pick-and-place operations are the most common in robotic applications, and often their design involves the presence of obstacles. This paper presents the development of a software platform that (allows-enables) the manipulation of a collaborative robot UR3e through the generation of 3D trajectories easily defined by the user, as well as a soft gripper capable of gripping objects with different geometries.” The news reporters obtained a quote from the research from the University of Cauca, “For this purpose, the development of a graphical interface in Unity is detailed, as well as the incorporation of the digital twin of the UR3e robot. In the same way, the different modules that allow the communication of the platform with the manipulator through ROS are exposed. The results show the creation of user-adapted paths for different cases in collision zones and the arrangement of the gripper for gripping different objects.”
      原文链接:
    • NETL
    • NSTL
    • Univ Politecnica Valencia, Editorial Upv

    Study Findings on Robotics Are Outlined in Reports from Chinese Academy of Sciences (Magnetic Tactile Sensor With Bionic Hair Array for Sliding Sensing and Object Recognition)

    38-39页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Due to the high application value in intelligent robots, tactile sensors with large sensing area and multi-dimensional sensing ability have attracted the attention of researchers in recent years. Inspired by bionics of hairs on human skin, a flexible tactile sensor based on magnetic cilia array is developed, showing extremely high sensitivity and stability.” Financial supporters for this research include National Key Research and Development Program of China, National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation. The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “The upper layers of the sensor are multiple magnetic cilia containing magnetic particles, while the lower layer is a serpentine flexible circuit board with a magnetic sensor array. When magnetic cilia are bent under force, the magnetic sensor array can detect changes in the magnetic field, thereby the magnitude and direction of external force can be obtained. The proposed sensor has a resolution of 0.2 mN with a working range of 0-19.5 mN and can distinguish the direction of external force. The large sensing area and short response time make this sensor suitable for sliding tactile detection, and experiments show that the sensor can be also applied in object recognition with a success accuracy of 97%. In addition to the shape of objects, the sensor can identify whether there is magnetism inside objects, making it of significant value in intelligent robots and modern medicine. A biomimetic magnetic tactile sensor with a magnetic hair array is developed, which can accurately measure the magnitude of external forces with a remarkable resolution of 0.2 mN within a range of 0-19.5 mN. It excels in sliding sensing and object recognition with an accuracy rate of 97%.”
      原文链接:
    • NETL
    • NSTL
    • Wiley