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    Researchers from University of Stuttgart Report Findings in Robotics and Automation (Hi-slam: Monocular Real-time Dense Mapping With Hybrid Implicit Fields)

    48-49页
    查看更多>>摘要:Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news reporting originating in Stuttgart, Germany, by NewsRx journalists, research stated, “In this letter, we present a neural field-based real-time monocular mapping framework for accurate and dense Simultaneous Localization and Mapping (SLAM). Recent neural mapping frameworks show promising results, but rely on RGB-D or pose inputs, or cannot run in real-time.” The news reporters obtained a quote from the research from the University of Stuttgart, “To address these limitations, our approach integrates dense-SLAM with neural implicit fields. Specifically, our dense SLAM approach runs parallel tracking and global optimization, while a neural field-based map is constructed incrementally based on the latest SLAM estimates. For the efficient construction of neural fields, we employ multi-resolution grid encoding and signed distance function (SDF) representation. This allows us to keep the map always up-to-date and adapt instantly to global updates via loop closing. For global consistency, we propose an efficient Sim(3)-based pose graph bundle adjustment (PGBA) approach to run online loop closing and mitigate the pose and scale drift. To enhance depth accuracy further, we incorporate learned monocular depth priors. We propose a novel joint depth and scale adjustment (JDSA) module to solve the scale ambiguity inherent in depth priors.”

    New Robotics and Automation Data Have Been Reported by Investigators at Shandong University (Rp-sg: Relation Prediction In 3d Scene Graphs for Unobserved Objects Localization)

    49-50页
    查看更多>>摘要:Research findings on Robotics - Robotics and Automation are discussed in a new report. According to news reporting out of Weihai, People’s Republic of China, by NewsRx editors, research stated, “The ability to search for objects is a fundamental prerequisite for mobile robots when addressing a wide range of automation tasks. However, how to effectively estimate the positions of unobserved objects in a continuously changing environment remains an open challenge.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Shandong University, “Previous works have utilized probabilistic models to estimate the co-occurrence property between the target object and the observed landmark objects in a scene. However, few approaches can predict the precise spatial relations between objects based on a specific scene configuration. In this letter, we propose a novel unobserved object localization framework that achieves context-specific relation prediction based on the particular configuration of a scene. First, we leverage a 3D scene graph as a compact representation of the environment and propose a relation prediction model based on graph neural networks. This model can effectively interpret the information provided by the 3D scene graph and make accurate relation predictions. Second, to address the challenge of a high number of non-existent links between objects in the scene graph, we introduce a novel loss function that can better address imbalanced training data. Additionally, we propose an evaluation framework to comprehensively assess whether the relation prediction model benefits object search tasks.”

    Research from Technological Center in the Area of Robotics Described (Development of a Human-Robot Interface for Cobot Trajectory Planning Using Mixed Reality)

    49-49页
    查看更多>>摘要:Current study results on robotics have been published. According to news originating from Valladolid, Spain, by NewsRx correspondents, research stated, “The growing demand for projects with collaborative robots, known as ‘cobots’, underlines the need to efficiently address the execution of tasks with speed and flexibility, without neglecting safety in human-robot interaction.” Our news correspondents obtained a quote from the research from Technological Center: “In general terms, this practice requires knowledge of robotics programming and skill in the use of hardware. The proposed solution consists of a mixed reality (MR) application integrated into a mixed reality head-mounted device (HMD) that accelerates the process of programming the complex manoeuvres of a cobot. This advancement is achieved through voice and gesture recognition, in addition to the use of digital panels. This allows any user, regardless of his or her robotics experience, to work more efficiently. The Robot Operating System (ROS) platform monitors the cobot and manages the transfer of data between the two. The system uses QR (Quick Response) codes to establish a precise frame of reference.”

    Investigators at University of Cassino and Southern Lazio Detail Findings in Machine Learning (How Word Semantics and Phonology Affect Handwriting of Alzheimer’s Patients: a Machine Learning Based Analysis)

    50-51页
    查看更多>>摘要:A new study on Machine Learning is now available. According to news originating from Cassino, Italy, by NewsRx correspondents, research stated, “Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements.” Our news journalists obtained a quote from the research from the University of Cassino and Southern Lazio, “In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer’s disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type.”

    Research Study Findings from University of London Update Understanding of Robotics (Who’s in charge here? A survey on Trustworthy AI in Variable Autonomy Robotic Systems)

    51-52页
    查看更多>>摘要:Investigators discuss new findings in robotics. According to news reporting from London, United Kingdom, by NewsRx journalists, research stated, “This paper surveys the Variable Autonomy (VA) robotics literature that considers two contributory elements to Trustworthy AI: transparency and explainability.” Our news reporters obtained a quote from the research from University of London: “These elements should play a crucial role when designing and adopting robotic systems, especially in VA where poor or untimely adjustments of the system’s level of autonomy can lead to errors, control conflicts, user frustration and ultimate disuse of the system. Despite this need, transparency and explainability is, to the best of our knowledge, mostly overlooked in VA robotics literature or is not considered explicitly. In this paper, we aim to present and examine the most recent contributions to the VA literature concerning transparency and explainability. In addition, we propose a way of thinking about VA by breaking these two concepts down based on: the mission of the human-robot team; who the stakeholder is; what needs to be made transparent or explained; why they need it; and how it can be achieved.”

    Findings from Yanshan University in Field Robotics Reported (Design and Motion Principle Analysis of New Parallel Mechanisms With Fewer Active Inputs Than the Degrees of Freedom)

    52-53页
    查看更多>>摘要:Fresh data on Robotics - Field Robotics are presented in a new report. According to news originating from Qinhuangdao, People’s Republic of China, by NewsRx correspondents, research stated, “In this paper, two new parallel mechanisms (PMs) with fewer active inputs than the degrees of freedom (DOFs)are proposed: (i) an nSPS (n = 7, 8, 9) six-DOF PM with n-6 active inputs and six lockable joints. and (ⅱ) a 3RPS-SPS 3-DOF PM with one active input and three lockable joints. Compared with the traditional PMs, the difference is that the proposed PMs can achieve the same mobility by using a minimal number of active joints.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Yanshan University, “Moreover, the loadcarrying capacity is also improved compared with the original standard mechanisms, since the new PMs become statically redundant when all the branches are locked. For this purpose, a sequential motion control principle is introduced that requires both inverse and forward kinematics of PMs. Kinematic modeling, dimensional optimization, and structural design are carried out for the 7SPS and 3RPS-SPS mechanisms, and the two prototypes are constructed for experimental validation.”

    Findings in Artificial Intelligence Reported from Bucharest University of Economic Studies (The Artificial Intelligence, Challenges for Accounting Profession. The Case of ChatGPT)

    53-54页
    查看更多>>摘要:New study results on artificial intelligence have been published. According to news reporting originating from Bucharest University of Economic Studies by NewsRx correspondents, research stated, “The implementation of Artificial Intelligence (AI) in the accounting field represents a hot topic. ChatGPT, an AI tool, became very popular recently, due to its conversational voice and abilities. The study is motivated less by the evolution of this Large Language Model (LLM), and more by its capabilities.” Our news journalists obtained a quote from the research from Bucharest University of Economic Studies: “This paper explores the impact of AI on accounting and accountants, in a dynamic world, with a focus on financial reporting. The research discusses about using AI technologies, more exactly ChatGPT 4, as tools available for accountants, and how they are changing the way financial data is processed, analyzed, and reported. The objectives of the author are to examine the potential advantages, benefits, limits, and risks associated with AI implementation in accounting, including increased accuracy and efficiency, as well as concerns around data privacy and security. In this regard, a quantitative method of research was used. It was realized an experiment with testing ChatGPT and its capabilities. Furthermore, the author argued that accountants need to develop new skills and competencies. This includes a deep understanding of AI algorithms and their limitations, as well as the ability to interpret and communicate the results of AI-driven analysis to non-technical stakeholders. By embracing AI technologies and developing new skills and competencies, accounting professionals can contribute to the long-term success of organizations in a dynamic and rapidly changing world. The paper also considers the challenges of detecting and preventing dishonesty and suggests strategies that accountants can implement to ensure integrity to use of these tools. These strategies refer to policies and procedures, providing training and support. The added value of this paper is the fact that provides an understanding of the implications of AI on accounting. The paper concludes that while the use of AI for accounting in a dynamic world presents benefits and opportunities, there are also some challenges to face.”

    Researchers from North China Electric Power University Report on Findings in Machine Learning (Prediction On the Seismic Performance Limits of Reinforced Concrete Columns Based On Machine Learning Method)

    54-55页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “The drift ratio or lateral deformation is typically applied as the indicator in order to evaluate the earthquakeinduced damage, one of the most important issues is to determine the seismic performance level limits. Therefore, this study presents to predict the seismic performance level limits of RC columns by using the machine learning method.” Financial supporters for this research include National Key Research and Development Program, National Natural Science Foundation of China (NSFC), Beijing Natural Science Foundation, Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture, BUCEA Post Graduate Innovation Project. Our news journalists obtained a quote from the research from North China Electric Power University, “Firstly, a test database of the backbone curves of RC columns was established after collecting 754 specimens under axial and lateral loads. Then the seismic performance level limits of all the collected columns were taken out as the input values of machine learning. The correlations among the geometric, mechanical parameters and the performance limits of RC columns were analyzed based on Pearson correlation analysis and mutual information method. Afterward, regression models of seven machine learning methods were established to predict the performance level limits of RC columns, while the hyperparameters of the machine learning models were optimized by the grid search and cross-validation methods. The generalization ability of the prediction models was verified and evaluated by using mean square error, mean absolute error, maximum error and R square, meanwhile, the accuracy of the applied methods was also analyzed. The seismic performance level limits of RC columns determined by the machine learning method can comprehensively consider the influence of geometric and mechanical parameters of RC columns. Combined with the earthquake-induced deformation of RC columns, the seismic damage of RC columns can be evaluated reasonably, which is of great significance for evaluating the seismic damage of building structures.”

    Second Hospital of Dalian Medical University Reports Findings in Heart Disease (Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization)

    55-56页
    查看更多>>摘要:New research on Heart Disorders and Diseases - Heart Disease is the subject of a report. According to news reporting out of Liaoning, People’s Republic of China, by NewsRx editors, research stated, “To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods. In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care Ⅳ (MIMIC-IV) were included in this cohort study.” Financial support for this research came from Dalian Medical Science research project. Our news journalists obtained a quote from the research from the Second Hospital of Dalian Medical University, “All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient <9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI). The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811-0.851) in the training set, and 0.760 (95%CI: 0.722- 0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764-0.814) in the training set and 0.731 (95%CI: 0.686-0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P <0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization.”

    Research Conducted at Guangxi Medical University Has Updated Our Knowledge about Support Vector Machines (Determining the Geographical Origin and Glycogen Content of Oysters Using Portable Near-infrared Spectroscopy: Comparison of ...)

    56-57页
    查看更多>>摘要:Current study results on Support Vector Machines have been published. According to news reporting out of Nanning, People’s Republic of China, by NewsRx editors, research stated, “Oysters are extensively cultivated worldwide. However, significant variations in chemical composition, quality, and price exist between oysters from different geographical origins.” Funders for this research include Guangxi First-class Discipline Project for Pharmaceutical Sciences, Fangchenggang Science and Technology Program, Major Program. Our news journalists obtained a quote from the research from Guangxi Medical University, “This study employed portable near-infrared spectroscopy in conjunction with chemometric analysis to determine the geographical origin and glycogen content of oysters. Pretreatment methods (multiplicative scattering correction, first derivative, and second derivative) were used to preprocess the raw spectra. Partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and support vector machine (SVM) were then adopted to establish the qualitative models. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were compared for predicting the glycogen content. The results revealed that the PLS-DA, OPLS-DA, and SVM models classified the geographical origin of oysters with 100% accuracy. For quantitative analysis, the regression equations displayed high predictive ability. The SVMR model was superior to the PLSR model for glycogen content prediction, with a coefficient of determination of prediction (R2P) of 0.9253 and a residual prediction deviation (RPD) of 3.62.”