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Robotics & Machine Learning Daily News

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    New Machine Learning Study Results Reported from University of Stuttgart (A Nove l Long Short-Term Memory Approach for Online State-of-Health Identification in L ithium-Ion Battery Cells)

    85-86页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Stuttgart, Germany, by NewsRx correspondents, research stated, "Revolutionary and cost-effective sta te estimation techniques are crucial for advancing lithium-ion battery technolog y, especially in mobile applications." Financial supporters for this research include Bundesministerium Fur Wirtschaft Und Energie. The news reporters obtained a quote from the research from University of Stuttga rt: "Accurate prediction of battery state-of-health (SoH) enhances state-of-char ge estimation while providing valuable insights into performance, second-life ut ility, and safety. While recent machine learning developments show promise in So H estimation, this paper addresses two challenges. First, many existing approach es depend on predefined charge/discharge cycles with constant current/constant v oltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery's lifespan in order to formulate predictions within the time series. Our novel hyb rid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing curr ent pulses filtered from authentic drive cycles."
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    Zhengzhou University Details Findings in Machine Learning (Research On Urban Sto rm Flood Simulation By Coupling K-means Machine Learning Algorithm and Gis Spati al Analysis Technology Into Swmm Model)

    86-87页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on Machine Learning are discuss ed in a new report. According to news reporting originating in Zhengzhou, People 's Republic of China, by NewsRx journalists, research stated, "Accurate flood si mulation has significant practical implications for urban flood management. The focus of this study is to develop a new flood model (K-SWMMG) based on the Storm Water Management Model (SWMM), which innovatively couples the K-means clusterin g machine learning algorithm and GIS spatial analysis techniques." Financial support for this research came from National Key Research and Developm ent Program of China.
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    Zhengzhou University Reports Findings in Machine Learning (Predicting and analyz ing the algal population dynamics of a grass-type lake with explainable machine learning)

    87-87页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Henan, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Algal blooms, exacerbated by climate change and eutrophication, have emerged as a global conc ern. In this study, we introduce a novel interpretable machine learning (ML) wor kflow tailored for investigating the dynamics of algal populations in grass-type lakes, Liangzi lake." Our news editors obtained a quote from the research from Zhengzhou University, " Utilizing seven ML methods and incorporating the covariance matrix adaptation ev olution strategy (CMA-ES), we predict algal density across three distinct time p eriods, resulting in the construction of a total of 30 ML models. The CMA-ES-Cat Boost model consistently demonstrates superior predictive accuracy and generaliz ation capability across these periods. Through the collective validation of vari ous interpretable tools, we identify water temperature and permanganate index as the two most critical water quality parameters (WQIs) influencing algal density in Liangzi Lake. Additionally, we quantify the independent and interactive effe cts of WQIs on algal density, pinpointing key thresholds and trends. Furthermore, we determine the minimum combination of WQIs that achieves near-optimal predic tive performance, striking a balance between accuracy and cost-effectiveness."
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    Study Findings from University of Taipei Update Knowledge in Pattern Recognition and Artificial Intelligence (A Novel Multi-Data-Augmentation and Multi-Deep-Lea rning Framework for Counting Small Vehicles and Crowds)

    88-88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 pa ttern recognition and artificial intelligence. According to news reporting origi nating from Taipei, Taiwan, by NewsRx correspondents, research stated, "Counting small pixel-sized vehicles and crowds in unmanned aerial vehicles (UAV) images is crucial across diverse fields, including geographic information collection, t raffic monitoring, item delivery, communication network relay stations, as well as target segmentation, detection, and tracking." Financial supporters for this research include Ministry of Science And Technolog y, Taiwan. Our news reporters obtained a quote from the research from University of Taipei: "This task poses significant challenges due to factors such as varying view ang les, non-fixed drone cameras, small object sizes, changing illumination, object occlusion, and image jitter. In this paper, we introduce a novel multidata- augm entation and multi-deep-learning framework designed for counting small vehicles and crowds in UAV images. The framework harnesses the strengths of specific deep -learning detection models, coupled with the convolutional block attention modul e and data augmentation techniques. Additionally, we present a new method for de tecting cars, motorcycles, and persons with small pixel sizes. Our proposed meth od undergoes evaluation on the test dataset v2 of the 2022 AI Cup competition, w here we secured the first place on the private leaderboard by achieving the high est harmonic mean."
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    New Intelligent Systems Study Findings Recently Were Reported by Researchers at National University of Defense Technology (Exploring Better Image Captioning Wit h Grid Features)

    89-89页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning-Intelligent Systems are discussed in a new report. According to news reporting originating from Changsha, People's Republic of China, by NewsRx correspondents, research stated, "Nowadays, Artificial Intelligence Generated Content (AIGC) h as shown promising prospects in both computer vision and natural language proces sing communities. Meanwhile, as an essential aspect of AIGC, image to captions h as received much more attention." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Nationa l Key Research and Development Program of China, Ministry of Science and Technol ogy, China. Our news editors obtained a quote from the research from the National University of Defense Technology, "Recent vision-language research is developing from the bulky region visual representations based on object detectors toward more conven ient and flexible grid ones. However, this kind of research typically concentrat es on image understanding tasks like image classification, with less attention p aid to content generation tasks. In this paper, we explore how to capitalize on the expressive features embedded in the grid visual representations for better i mage captioning. To this end, we present a Transformer-based image captioning mo del, dubbed FeiM, with two straightforward yet effective designs. We first desig n the feature queries that consist of a limited set of learnable vectors, which act as the local signals to capture specific visual information from global grid features. Then, taking augmented global grid features and the local feature que ries as inputs, we develop a feature interaction module to query relevant visual concepts from grid features, and to enable interaction between the local signal and overall context. Finally, the refined grid visual representations and the l inguistic features pass through a Transformer architecture for multi-modal fusio n. With the two novel and simple designs, FeiM can fully leverage meaningful vis ual knowledge to improve image captioning performance."
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    New Robotics Research from National Institute of Advanced Industrial Science and Technology (NIAIST) Described (A Path to Industry 5.0 Digital Twins for Human-R obot Collaboration by Bridging NEP+ and ROS)

    90-90页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in robotic s. According to news reporting from Tokyo, Japan, by NewsRx journalists, researc h stated, "The integration of heterogeneous hardware and software components to construct human-centered systems for Industry 5.0, particularly human digital tw ins, presents considerable complexity." Our news reporters obtained a quote from the research from National Institute of Advanced Industrial Science and Technology (NIAIST): "Our research addresses th is challenge by pioneering a novel approach that harmonizes the techno-centered focus of the Robot Operating System (ROS) with the cross-platform advantages inh erent in NEP+ (a human-centered development framework intended to assist users a nd developers with diverse backgrounds and resources in constructing interactive human-machine systems). We introduce the nep2ros ROS package, aiming to bridge these frameworks and foster a more interconnected and adaptable approach. This i nitiative can be used to facilitate diverse development scenarios beyond convent ional robotics, underpinning a transformative shift in Industry 5.0 applications . Our assessment of NEP+ capabilities includes an evaluation of communication pe rformance utilizing serialization formats like JavaScript Object Notation (JSON) and MessagePack. Additionally, we present a comparative analysis between the ne p2ros package and existing solutions, illustrating its efficacy in linking the s imulation environment (Unity) and ROS. Moreover, our research demonstrates NEP+' s applicability through an immersive human-in-the-loop collaborative assembly."
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    Researcher at Robert Gordon University Publishes Research in Evolutionary Comput ation (A Multi-Objective Evolutionary Approach to Discover Explainability Tradeo ffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behavi our ...)

    91-91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 ev olutionary computation. According to news originating from Aberdeen, United King dom, by NewsRx correspondents, research stated, "Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the des ign of assemblies (e.g., motors) that are efficient and durable under multiple o perational scenarios." Funders for this research include Comet-k2; Center For Symbiotic Mechatronics; L inz Center of Mechatronics. The news editors obtained a quote from the research from Robert Gordon Universit y: "To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multiobjective strategy for creating Linear Re gression (LR) models that integrate optimised synthetic features. The main stren gth of our approach is that it provides decision makers with a clear overview of the optimal tradeoffs between data collection costs, the expected modelling err ors and the overall explainability of the generated thermal models."
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    New Findings from NASA Goddard Space Flight Center Update Understanding of Machi ne Learning (A Novel Approach To Impact Crater Mapping and Analysis On Enceladus, Using Machine Learning)

    92-92页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting out of Greenbelt, Maryland, by NewsRx editors, research stated, "Impact cratering is one of the most import ant processes shaping planetary surfaces, offering valuable clues about the targ et body's geologic history and composition. However, crater mapping has historic ally been done manually, a process that has proven to be both arduous and time c onsuming." Funders for this research include Data Science Group, NASA Pathways Program at N ASA Goddard Space Flight Center, NASA High-End Computing (HEC) Program through t he NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. Our news journalists obtained a quote from the research from NASA Goddard Space Flight Center, "This paper outlines a machine learning crater mapping approach f or bodies with limited elevation data available (Digital Elevation Models). We a pplied a Convolutional Neural Network for the detection and morphometry of impac t craters on Saturn's moon Enceladus using light-shadow labels trained on data f rom the Cassini Imaging Science Subsystem. Our algorithm identified a total of 5 ,240 features which were used to quantify crater distribution; this included the highest number of small craters (<1-2 km in diameter) reco rded on Enceladus by any previous published study. The pool of features was late r down-selected to craters between 0 and 30 degrees N (latitude) imaged at high incidence (>60 degrees) and phase angles (> 26 degrees). The down selection was necessary to accurately perform diameter mea surements and derive depths from shadow estimation techniques to calculate depth -diameter ratios (d/D); a well-studied relationship used to constrain planetary surface properties. Results show that the d/D ratio of craters in the equatorial region of Enceladus range from similar to 0.06 to 0.37, with a median of 0.19. Our results will inform efforts to constrain the surface properties of this regi on of Enceladus, potentially also supporting future mission concept design for t he Saturnian moon."
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    Reports Outline Artificial Intelligence Study Findings from Kangwon National Uni versity Hospital (Influence of artificial intelligence and chatbots on research integrity and publication ethics)

    93-93页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting originating from Chunche on, South Korea, by NewsRx correspondents, research stated, "Artificial intellig ence (AI)-powered chatbots are rapidly supplanting human-derived scholarly work in the fast-paced digital age. This necessitates a re-evaluation of our traditio nal research and publication ethics, which is the focus of this article." Financial supporters for this research include National Research Foundation of K orea. Our news correspondents obtained a quote from the research from Kangwon National University Hospital: "We explore the ethical issues that arise when AI chatbots are employed in research and publication. We critically examine the attribution of academic work, strategies for preventing plagiarism, the trustworthiness of AI-generated content, and the integration of empathy into these systems. Current approaches to ethical education, in our opinion, fall short of appropriately ad dressing these problems. We propose comprehensive initiatives to tackle these em erging ethical concerns. This review also examines the limitations of current ch atbot detectors, underscoring the necessity for more sophisticated technology to safeguard academic integrity. The incorporation of AI and chatbots into the res earch environment is set to transform the way we approach scholarly inquiries. H owever, our study emphasizes the importance of employing these tools ethically w ithin research and academia."
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    University of Zielona Gora Researchers Update Knowledge of Machine Learning (Wor king toward Solving Safety Issues in Human-Robot Collaboration: A Case Study for Recognising Collisions Using Machine Learning Algorithms)

    94-94页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting out of Zielona Gora, Poland, by New sRx editors, research stated, "The monitoring and early avoidance of collisions in a workspace shared by collaborative robots (cobots) and human operators is cr ucial for assessing the quality of operations and tasks completed within manufac turing." Financial supporters for this research include Polish Ministry of Science. The news correspondents obtained a quote from the research from University of Zi elona Gora: "A gap in the research has been observed regarding effective methods to automatically assess the safety of such collaboration, so that employees can work alongside robots, with trust. The main goal of the study is to build a new method for recognising collisions in workspaces shared by the cobot and human o perator. For the purposes of the research, a research unit was built with two UR 10e cobots and seven series of subsequent of the operator activities, specifical ly: (1) entering the cobot's workspace facing forward, (2) turning around in the cobot's workspace and (3) crouching in the cobot's workspace, taken as video re cordings from three cameras, totalling 484 images, were analysed. This innovativ e method involves, firstly, isolating the objects using a Convolutional Neutral Network (CNN), namely the Region-Based CNN (YOLOv8 Tiny) for recognising the obj ects (stage 1). Next, the Non-Maximum Suppression (NMS) algorithm was used for f iltering the objects isolated in previous stage, the * * k* * -means clustering method and Simple Online Real-Time Tracking (SORT) approach were used for separa ting and tracking cobots and human operators (stage 2) and the Convolutional Neu tral Network (CNN) was used to predict possible collisions (stage 3). The method developed yields 90% accuracy in recognising the object and 96.4% accuracy in predicting collisions accuracy, respectively."
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