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    Data from Shanghai Ocean University Broaden Understanding of Robotics (A novel parallel ant colony optimization algorithm for mobile robot path planning)

    39-39页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in robotics. According to news reporting from Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research.” The news correspondents obtained a quote from the research from Shanghai Ocean University: “This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out.”
      原文链接:
    • NETL
    • NSTL

    Investigators at Fujian Agricultural and Forestry University Discuss Findings in Machine Learning (Machine Learning-enhanced Triboelectric Sensing Application)

    40-40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 Learning have been published. According to news reporting originating in Fuzhou, People’s Republic of China, by NewsRx journalists, research stated, “Tri- boelectric nanogenerator (TENG) has become a promising candidate for wearable energy harvesting and self-powered sensing systems. However, processing large amounts of data imposes a computing power barrier for practical application.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Sci- ence Foundation of Fujian Province, Fuzhou Institute of Oceanography project, Fuzhou City Science and Technology Cooperation Project. The news reporters obtained a quote from the research from Fujian Agricultural and Forestry University, “Machine learning-assisted self-powered sensors based on TENG have been widely used in data-driven applications due to their excellent characteristics such as no additional power supply, high sensing accuracy, low cost, and good biocompatibility. This work comprehensively reviews the latest progress in machine learning (ML)-assisted TENG-based sensors. The future challenges and opportunities are discussed. First, the fundamental principles including the working mode of ML-assisted TENG-based sensor and common algorithms are systematically and comprehensively illustrated, which emphasizes the algorithm definition and principle. Subsequently, the progress of ML methods in the field of TENG-based sensors is further reviewed, summarizing the advantages and disadvantages of various algorithms in practical examples, and providing guidance and suggestions on how to choose the appropriate methods. Finally, the prospects and challenges of ML-assisted TENG-based sensors is summarized.” According to the news reporters, the research concluded: “Directions and important insights for the future development of TENG and AI integration is provided.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Shenzhen Institute of Advanced Technology Reports Findings in Machine Learning (Computational drug development for membrane protein targets)

    41-41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 the subject of a report. According to news reporting originating in Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins.” The news reporters obtained a quote from the research from the Shenzhen Institute of Advanced Tech- nology, “Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dy- namic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development.” According to the news reporters, the research concluded: “Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL

    Findings on Machine Learning Reported by Investigators at Renmin University (Regularized Optimal Transport Layers for Generalized Global Pooling Operations)

    42-42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 Machine Learning. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Global pooling is one of the most significant operations in many machine learning models and tasks, which works for information fusion and structured data (like sets and graphs) representation. However, without solid mathematical fundamentals, its practical implementations often depend on empirical mechanisms and thus lead to sub-optimal, even unsatisfactory performance.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), CAAI-Huawei MindSpore Open Fund, Fundamental Research Funds for the Central Universities, Research Funds of Renmin University of China, Beijing Key Laboratory of Big Data Management, Major Innovation & Planning Interdisciplinary Platform for the “Double-First Class” Initiative.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Researchers from Chinese Academy of Sciences Detail New Studies and Findings in the Area of Robotics (Autonomous Dynamic Hitchhiking Control of a Bionic Robotic Remora)

    43-43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 Robotics. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Remora suckerfish and its hitch-hiking behavior bring enormous inspiration into the engineering field. In this article, a bioinspired hitch-hiking behavior is accomplished automatically by a robotic remora, which creates possibilities for prolonging its endurance and multirobot cooperation.” Funders for this research include National Natural Science Foundation of China (NSFC), Joint Fund of Ministry of Education for Equipment Pre-Research. Our news editors obtained a quote from the research from the Chinese Academy of Sciences, “First, the definition of the hitch-hiking task and the mechatronic design of robotic remora are introduced. Then, aiming at the hitch-hiking task, the LED-marker-based underwater visual localization method and planar state synchronization controller are developed. The localization method includes a complete framework from LED detection to marker pose optimization, which considers the refraction correction to improve the localization precision in water. The synchronization controller is decomposed into the lateral and longitudinal subcontrollers to overcome the challenges caused by underactuated dynamic. Besides, a finite state machine is designed to model the state and action transition during the hitch-hiking task. Extensive experimental results demonstrate the effectiveness of the proposed method. The autonomous hitch-hiking task toward a moving host is successfully implemented by a robotic fish for the first time.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Reports on Machine Learning Findings from Technical University of Denmark (DTU) Provide New Insights (Natural Language Processing of Student's Feedback To Instructors: a Systematic Review)

    44-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are presented in a new report. According to news orig- inating from Lyngby, Denmark, by NewsRx correspondents, research stated, “Course developers, providers and instructors gather feedback from students to gain insights into student satisfaction, success, and diffi- culties in the learning process. The traditional manual analysis is time-consuming and resource-intensive, resulting in decreased insights and pedagogical impact.” Financial support for this research came from Digital Research Centre Denmark. Our news journalists obtained a quote from the research from the Technical University of Denmark (DTU), “To address the problems, researchers use natural language processing techniques that apply the fields of machine learning, statistics and artificial intelligence to the feedback datasets for various purposes. These purposes include predicting sentiment, opinion research, insights into students’ views of the course, and so on. The aim of this study is to identify themes and categories in academic research reports that use natural language processing for student feedback. Previous review studies have focused exclusively on sentiment analysis and specific techniques, such as machine learning and deep learning. Our study put forward a comprehensive synthesis of various aspects, from the data to the methods used, to the data translation and labeling efforts, and to the categorization of prediction/analysis targets in the literature. The synthesis includes two tables that allow the reader to compare the studies themselves and present the identified themes and categorizations in one figure and text. The methods, tools and data of 28 peer- reviewed papers are synthesized in 20 categories under six themes: aim and categorization, methods and models, and tools and data (size and context, language, and labeling).”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Data on Machine Learning Described by Researchers at University of Toronto (Machine Learning Enabled Prediction of High Stiffness 2d Materials)

    45-45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 Learning have been published. According to news reporting originating from Toronto, Canada, by NewsRx correspondents, research stated, “Persistent ex- ploration of high stiffness two-dimensional (2D) materials is necessary for advancements in applications such as nanocomposites, flexible electronics, and resonant sensors, all of which demand elevated resistance to deformation. However, data-centric material models developed for this purpose remain in their early stages, often due to incomplete stiffness estimation or limited transferability to unseen 2D materials.” Funders for this research include University of Toronto, CGIAR, University of Toronto. Our news editors obtained a quote from the research from the University of Toronto, “In this context, we examined stiffness trends among different classes of 2D materials and identified the elastic constants pivotal for estimating the 2D material stiffness irrespective of their crystal symmetry. Subsequently, we developed Gaussian Process Regression machine learning models with the capability of relative stiffness comparison, which are used to predict high stiffness candidates across a broad spectrum of unseen 2D materials during model training. The probability of finding high stiffness 2D materials increased significantly, from a mere 1% in the training data set to a notable 47% in the set of machine learning-predicted 2D materials.” According to the news editors, the research concluded: “We also discussed potential stiffening mecha- nisms, competing stiffness characteristics, and complementary properties of these predicted high-stiffness 2D materials that are crucial for enhancing the effectiveness of the aforementioned applications.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL

    Reports on Support Vector Machines from Guangdong University of Technology Provide New Insights (Privileged Multi-view One-class Support Vector Machine)

    45-46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 Support Vector Machines have been published. According to news reporting originating from Guangzhou, People’s Republic of China, by NewsRx correspondents,research stated, “One -class support vector machine (OCSVM) is a typical one -class classification approach, which learns the classifier by using only the target samples. At present, most OCSVM works hypothesize that the samples have only one view, while multi -view OCSVM has not been taken into account.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangdong Province. Our news editors obtained a quote from the research from the Guangdong University of Technology, “In this paper, a novel multi -view one -class support vector machine method with privileged information learning (MOCPIL) is put forward. MOCPIL embodies both the consensus principle and complementarity principle in multi -view learning. Privileged information is additional data that is available only in the training process, but not in the testing process. By introducing the idea of privileged information learning, MOCPIL implements the complementarity principle by treating one view as the training data and the other view as the privileged data. Moreover, MOCPIL implements the consensus principle by requiring that different views of the same object should give similar predicting outputs. The learning problem of MOCPIL is a quadratic programming (QP) problem, which is able to be solved by off -the -shelf QP solvers. To the best of our knowledge, this is the first study to tackle the multi -view learning problem based on OCSVM. The performance of MOCPIL is evaluated through extensive experiments.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Netherlands Organization for Applied Scientific Research Researcher Describes Research in Machine Learning (Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models)

    46-47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 artificial intelligence. According to news originating from the Netherlands Organization for Applied Scientific Research by NewsRx correspondents, research stated, “Reducing greenhouse emissions can be done via the electrification of the transport industry.However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities.” Funders for this research include European Union. The news correspondents obtained a quote from the research from Netherlands Organization for Applied Scientific Research: “To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e. the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Data on Robotics Reported by Gurneet Brar and Colleagues (Robotic surgery: public perceptions and current misconceptions)

    47-48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 Robotics is the subject of a report. According to news reporting from London, United Kingdom, by NewsRx journalists, research stated, “Whilst surgeons and robotic companies are key stakeholders involved in the adoption of robotic assisted surgery (RS), the public’s role is overlooked. However, given that patients hold ultimate power over their healthcare decisions, public acceptance of RS is crucial.” The news correspondents obtained a quote from the research, “Therefore, this study aims to identify public understanding, opinions, and misconceptions about RS. An online questionnaire distributed between February and May 2021 ascertained the views of UK adults on RS. The themes of questions included familiarity, experience and comfort with RS, opinions on its ethical implications, and the impact of factual information provided to the participant. The data were evaluated using thematic and statistical analysis, including assessing for statistical differences in age, gender, education level, and presence in the medical field. Overall, 216 responses were analysed. Participants were relatively uninformed about RS, with a median knowledge score of 4.00(2.00-6.00) on a 10-point Likert scale. Fears surrounding increased risk, reduced precision and technological failure were identified, alongside misconceptions about its autonomous nature. However, providing factual information in the survey about RS statistically increased participant comfort (p =<0.0001). Most (61.8%) participants believed robot manufacturers were responsible for malfunctions, but doctors were held accountable more by older, less educated, and non-medical participants. Our findings suggest that there is limited public understanding of RS.”
      原文链接:
    • NETL
    • NSTL