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    Anhui Agricultural University Reports Findings in Machine Learning (High-through put prediction of stalk cellulose and hemicellulose content in maize using machi ne learning and Fourier transform infrared spectroscopy)

    86-87页
    查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting out of Anhui, People's Republic of China , by NewsRx editors, research stated, "Cellulose and hemicellulose are key cross -linked carbohydrates affecting bioethanol production in maize stalks. Tradition al wet chemical methods for their detection are labor-intensive, highlighting th e need for high-throughput techniques." Our news journalists obtained a quote from the research from Anhui Agricultural University, "This study used Fourier transform infrared (FTIR) spectroscopy comb ined with machine learning (ML) algorithms on 200 large-scale maize germplasms t o develop robust predictive models for stalk cellulose, hemicellulose and holoce llulose content. We identified several peak height features correlated with thre e contents, used them as input data for model building. Four ML algorithms demon strated higher predictive accuracy, achieving coefficient of determination ® ran ging from 0.83 to 0.97. Notably, the Categorical Boosting algorithm yielded opti mal models with coefficient of determination ® exceeding 0.91 for the training s et and over 0.81 for the test set."
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    Recent Findings from Indian Institute of Technology (IIT) Indore Has Provided Ne w Information about Support Vector Machines (Bell-shaped Fuzzy Least Square Twin Svm With Biomedical Applications)

    87-88页
    查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Support Vector Machines is the su bject of a report. According to news originating from Indore, India, by NewsRx c orrespondents, research stated, "In practical applications, datasets frequently encompass noise, outliers, and imbalanced classes, which can markedly affect a m odel's generalization performance. Support vector machine (SVM) and its twin var iant i.e., TWSVM tend to be biased towards the majority class samples, leading t o misclassification of the minority class samples." Funders for this research include Office of the Administrator (NIH), United Stat es Department of Defense, NIH National Institute on Aging (NIA), NIH National In stitute of Biomedical Imaging & Bioengineering (NIBIB), Alzheimer' s Association, Alzheimers Drug Discovery Foundation, Fujirebio, GE Healthcare, I XICO Ltd., Janssen Alzheimer Immunotherapy Research & Development, LLC., Johnson & Johnson USA, Lumosity, Lundbeck Corporation, Merc k & Company, Meso Scale Diagnostics, LLC., Pfizer, Piramal Imaging , Servier, Takeda Pharmaceutical Company Ltd, Transition Therapeutics, Canadian Institutes of Health Research (CIHR).
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    Central South University of Forestry and Technology Researcher Reports Research in Artificial Intelligence (Rapid Forest Change Detection Using Unmanned Aerial Vehicles and Artificial Intelligence)

    88-89页
    查看更多>>摘要：Investigators publish new report on ar tificial intelligence. According to news reporting from Changsha, People's Repub lic of China, by NewsRx journalists, research stated, "Forest inspection is a cr ucial component of forest monitoring in China. The current methods for detecting changes in forest patches primarily rely on remote sensing imagery and manual v isual interpretation, which are timeconsuming and labor-intensive approaches." Financial supporters for this research include Hainan Provincial Natural Science Foundation of China. Our news correspondents obtained a quote from the research from Central South Un iversity of Forestry and Technology: "This study aims to automate the extraction of changed forest patches using UAVs and artificial intelligence technologies, thereby saving time while ensuring detection accuracy. The research first utiliz es position and orientation system (POS) data to perform geometric correction on the acquired UAV imagery. Then, a convolutional neural network (CNN) is used to extract forest boundaries and compare them with the previous vector data of for est boundaries to initially detect patches of forest reduction. The average boun dary distance algorithm (ABDA) is applied to eliminate misclassified patches, ul timately generating precise maps of reduced forest patches. The results indicate that using POS data with RTK positioning for correcting UAV imagery results in a central area correction error of approximately 4 m and an edge area error of a pproximately 12 m. The TernausNet model achieved a maximum accuracy of 0.98 in i dentifying forest areas, effectively eliminating the influence of shrubs and gra sslands."
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    New Machine Learning Findings from South China University of Technology Discusse d (Downscaled High Spatial Resolution Images From Automated Machine Learning for Assessment of Urban Structure Effects On Land Surface Temperatures)

    89-90页
    查看更多>>摘要：Investigators publish new report on Ma chine Learning. According to news reporting out of Guangzhou, People's Republic of China, by NewsRx editors, research stated, "Urbanization has profoundly resha ped urban morphology and land cover while degrading the thermal environment. Des pite numerous studies exploring correlations between two-dimensional (2D) and th ree-dimensional (3D) urban features and land surface temperatures (LSTs), unders tanding the impact of urban structural effects on LSTs remains unclear due to li mited high-spatial-resolution satellite data." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of Guangdong Province, S tate Key Laboratory of Subtropical Building and Urban Science, Fundamental Resea rch Funds for the Central Universities.
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    Study Data from Xinjiang University Provide New Insights into Machine Learning ( Improved Pore Structure Prediction Based On a Stacking Machine Learning Model fo r Low-permeability Reservoir In Tazhong Area, Tarim Basin)

    90-91页
    查看更多>>摘要：New research on Machine Learning is th e subject of a report. According to news reporting originating from Xinjiang, Pe ople's Republic of China, by NewsRx correspondents, research stated, "Stacking i s an ensemble machine learning method designed to improve overall performance by combining the predictions of multiple base learners. The core idea of Stacking is to use the output of different base learners as input and make final predicti ons through a meta-learner, allowing the model to benefit from the strengths of various base learners and adapt better to complex data patterns." Financial support for this research came from Tianchi talent project. Our news editors obtained a quote from the research from Xinjiang University, "P redicting pore structures in low permeability is a sophisticated nonlinear model ing issue affected by multiple factors, including sedimentary and diagenetic eff ects on the structure. An individual model to predict pore structure is not sati sfactory. In this paper, we applied a fusion of random forest and Adaboost model s as base learners, which emerges as the cornerstone of this research, showcasin g remarkable versatility and integration of diverse learner strengths. The Silur ian system in Tazhong area features a vast expanse of lithological reservoirs wi th low abundance and high heterogeneity. Capillary pressure plays a crucial role in the distribution of these less permeable reservoirs by pore structure type i nfluence. Aiming to predict the pore structure for the low-permeability reservoi r in the field, which is characterized by low abundance and high heterogeneity, the study strategically integrates geological theory into specialized databases and harnesses machine learning methods. The resulting machine learning modeling dataset leverages geological factors and logging response characteristics to str engthen the ensemble machine learning stacking model. This sophisticated model n ot only achieves exceptional accuracy but also undergoes rigorous validation by analyzing predicted pore structure types alongside production data. The model's average accuracy was 79.4%, which practically hit the test set's ac curacy threshold, meaning that the model was in an optimal state."
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    Findings from Illinois State University Broaden Understanding of Androids (Presc riptive Multi-group Networks: Humanoid Service Robots' Value Co-creation and Co- destruction Potentials In Apparel Stores)

    91-92页
    查看更多>>摘要：Current study results on Robotics - An droids have been published. According to news reporting out of Normal, Illinois, by NewsRx editors, the research stated, "Drawing upon a theoretical foundation within service-dominant logic, this study analyzed multi-group networks of human oid service robots (HSRs) and investigated the differences in the structures and relations between groups that adopted and rejected HSRs. Moreover, it explored the most important and central predictors in each network among value co-creatio n and co-destruction potentials." Financial supporters for this research include College of Applied Science & Technology (CAST), Department of Family and Consumer Sciences (FCS), Office of R esearch and Graduate Studies (Research and Sponsored Programs) at the Illinois S tate University. Our news journalists obtained a quote from the research from Illinois State Univ ersity, "A pretest and the main data collection (n = 474) were conducted with a video-based stimulus in an apparel store. The results revealed that the structur e and the three edge-weights in the networks of the groups that adopted and reje cted HSRs differed significantly. Essentially, complexity and co-creation enjoym ent were centralpredictors within the networks. This study offers a comprehensi ve understanding of value co-creation and co-destruction between customers and t echnology actors, leading them to adopt and reject HSRs."
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    Findings on Machine Learning Reported by Investigators at University of Science and Technology Beijing (Multi-damage Indexbased Interfacial Debonding Predictio n for Steel-concrete Composite Structures With Percussion Method)

    92-93页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "The interfacial debonding as an invisi ble damage significantly undermines the bearing capacity and durability of steel -concrete composite structures (SCCS). Although the percussion method has been w idely utilized in practical applications, the single damage index (DI) extracted in the analysis process tends to be invalid to abnormalities and leads to misju dgment." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Beijing Municipal Science & Technology Commiss ion, Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities, Key Laboratory for Intelligent Infr astructure and Monitoring of Fujian Province (Huaqiao University).
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    Wuhan University of Science and Technology Researchers Yield New Data on Robotic s (Improved vision-only localization method for mobile robots in indoor environm ents)

    93-93页
    查看更多>>摘要：Research findings on robotics are disc ussed in a new report. According to news reporting out of Wuhan University of Sc ience and Technology by NewsRx editors, research stated, "To solve the problem o f mobile robots needing to adjust their pose for accurate operation after reachi ng the target point in the indoor environment, a localization method based on sc ene modeling and recognition has been designed." Funders for this research include Natural Science Foundation of Hubei Province; Hubei Key Laboratory of Power System Design And Test For Electrical Vehicle.
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    Researchers' Work from China Jiliang University Focuses on Robotics and Automati on (Enhancing Adaptability: Hierarchical Frontier-based Path Planning for Naviga tion In Challenging Environments)

    94-94页
    查看更多>>摘要：Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news reporting originatin g from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Current UAV path planning methods exhibit efficient performance in navi gating environments with small obstacles, such as indoor areas and outdoor fores ts. However, they often encounter challenges when dealing with environments char acterized by large obstacles, such as expansive walls and towering structures sc enario (ET scenario)." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from China Jiliang Universit y, "These challenges often cause problems of path redundancy or even complete fa ilure in path planning. In this letter, a hierarchical frontier-based path plann ing framework is proposed to enhance the adaptability of planners to diverse env ironments. The collected frontiers, processed by a point cloud voxel down-sampli ng method, are transformed into a spatially uniform point set. An analysis is co nducted to explore the relationship among the UAV's current position, the target , and the potential guide point. Following this, a decision function is designed to identify the optimal local guide point from the point set to guide the UAV i n path planning."
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    Research from University of Tehran Yields New Data on Robotics (Multi-Modal Robu st Geometry Primitive Shape Scene Abstraction for Grasp Detection)

    95-95页
    查看更多>>摘要：Fresh data on robotics are presented i n a new report. According to news reporting originating from Tehran, Iran, by Ne wsRx correspondents, research stated, "Scene understanding is essential for a wi de range of robotic tasks, such as grasping. Simplifying the scene into predefin ed forms makes the robot perform the robotic task more properly, especially in a n unknown environment." The news editors obtained a quote from the research from University of Tehran: " This paper proposes a combination of simulation-based and real-world datasets fo r domain adaptation purposes and grasping in practical settings. In order to com pensate for the weakness of depth images in previous studies reported in the lit erature for clearly representing boundaries, the RGB image has also been fed as input in RGB and RGB-D input modalities. The implemented architecture is based o n the Mask R-CNN network with a backbone of ResNet101. By using RGB and RGB-D im ages as input, the proposed approach has thus improved the segmentation Dice sco re over primitive shape abstraction by 3.73% and 6.19% , respectively. Moreover, in order to improve and evaluate the robustness of the model to occlusion and a variety of primitive shapes and colors that may occur in the scene, different versions of simulation-based datasets are generated usin g the Coppeliasim simulator. Additionally, a real-world primitive shape abstract ion dataset is created to make the model more robust in more complex objects and real-world experiments. To further generalize the model to apply to a wider ran ge of objects, new primitive shapes, such as cones, and both filled and hollow t ypes of each primitive shape, are considered. Subsequently, the point clouds of the segmented parts are generated, and the ICP algorithm is used to derive the 6 -DOF grasp parameters using reference primitive shapes and their predefined gras ps. Simulation experiments result in a 95% grasp success rate usin g the Coppeliamsim simulation environment on unseen objects. A Delta parallel ro bot and a 2-fingered fabricated gripper are used for practical experiments."
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