期刊,Robotics & Machine Learning Daily News 2024年卷Jun.20期_国家学术搜索

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Robotics & Machine Learning Daily News

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Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News

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Federal University Goias Reports Findings in Artificial Intelligence (Diagnostic capability of artificial intelligence tools for detecting and classifying odont ogenic cysts and tumors: a systematic review and meta-analysis)

76-77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 Artificial Intelligenc e is the subject of a report. According to news reporting originating in Goiania , Brazil, by NewsRx journalists, research stated, "To evaluate the diagnostic ca pability of artificial intelligence (AI) for detecting and classifying odontogen ic cysts and tumors, with special emphasis on odontogenic keratocyst (OKC) and a meloblastoma. Nine electronic databases and the gray literature were examined." The news reporters obtained a quote from the research from Federal University Go ias, "Humanbased studies using AI algorithms to detect or classify odontogenic cysts and tumors by using panoramic radiographs or CBCT were included. Diagnosti c tests were evaluated, and a meta-analysis was performed for classifying OKCs a nd ameloblastomas. Heterogeneity, risk of bias, and certainty of evidence were e valuated. Twelve studies concluded that AI is a promising tool for the detection and/or classification of lesions, producing high diagnostic test values. Three articles assessed the sensitivity of convolutional neural networks in classifyin g similar lesions using panoramic radiographs, specifically OKC and ameloblastom a. The accuracy was 0.893 (95% CI 0.832-0.954). AI applied to cone beam computed tomography produced superior accuracy based on only 4 studies. Th e results revealed heterogeneity in the models used, variations in imaging exami nations, and discrepancies in the presentation of metrics. AI tools exhibited a relatively high level of accuracy in detecting and classifying OKC and ameloblas toma. Panoramic radiography appears to be an accurate method for AI-based classi fication of these lesions, albeit with a low level of certainty."

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Researcher at Zhejiang University Has Published New Data on Robotics (Motion Con trol of a Hybrid Quadruped-Quadrotor Robot)

76-76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 robotics are disc ussed in a new report. According to news reporting out of Hangzhou, People's Rep ublic of China, by NewsRx editors, research stated, "Multimodal motion capabilit y is an emerging topic in the robotics field, and this paper presents a hybrid r obot system maneuvering in both terrestrial and aerial environments." The news journalists obtained a quote from the research from Zhejiang University : "Firstly, a micro quadruped-quadrotor robot with onboard sensing and computing is developed. This robot incorporates both the high mobility of unmanned aerial vehicles and the long endurance of mobile robots on the ground. A coordinated m otion control scheme is then exploited for adaptive terrestrial-aerial motion tr ansition. In this scheme, a bio-inspired terrestrial locomotion controller is pr oposed to generate various quadruped locomotions, and a model-based aerial locom otion controller is proposed to generate various quadrotor configurations."

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Research Conducted at Beijing Institute of Technology Has Provided New Informati on about Machine Learning (Inverse Machine Learning Framework for Optimizing Gra dient Honeycomb Structure Under Impact Loading)

77-78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 Machine Learning. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "In this study, an inverse design frame work was constructed to explore gradient honeycomb structures (HCS) with high im pact resistance. By establishing the relationship between the height of the cell and the cell-wall angle, HCS with different gradient modes were designed." Financial supporters for this research include National Key R & D Program for Young Scientists of China, China, National Natural Science Foundatio n of China (NSFC), Science and Technology Innovation Program of Beijing institut e of technology.

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New Findings Reported from Punjab Remote Sensing Centre Describe Advances in Mac hine Learning (Monitoring vegetation degradation using remote sensing and machin e learning over India-a multi-sensor, multi-temporal and multi-scale approach)

78-79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 artificial intell igence are discussed in a new report. According to news originating from Punjab, India, by NewsRx correspondents, research stated, "Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic vari ation and anthropogenic actions. Conservation of biodiversity is important becau se millions of people are directly and indirectly dependent on vegetation (fores t and crop) and its associated secondary products." The news journalists obtained a quote from the research from Punjab Remote Sensi ng Centre: "United Nations Sustainable Development Goals (SDGs) propose to quant ify the proportion of vegetation as a proportion of total land area of all count ries. Satellite images form as one of the main sources of accurate information t o capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degr adation. Open source Cloud computing system Google Earth Engine (GEE) was used t o systematically monitor vegetation degradation and evaluate the potential of mu ltiple satellite data with variable spatial resolutions. Hotspots were demarcate d using machine learning techniques to identify the greening and the browning ef fect of vegetation using coarse resolution Normalized Difference Vegetation Inde x (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipita tion with Station data (CHIRPS) for the period 2000-2022 were also used to find rainfall anomaly in the region."

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New Machine Learning Study Findings Reported from University of Connecticut (Non -Parametric Machine Learning Modeling of Tree- Caused Power Outage Risk to Overhe ad Distribution Powerlines)

79-80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 artificial intelligenc e is the subject of a new report. According to news reporting originating from S torrs, Connecticut, by NewsRx correspondents, research stated, "Trees in proximi ty to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs." The news correspondents obtained a quote from the research from University of Co nnecticut: "This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM's performance using decision tree (DT), random forest (RF), k-Nearest Neigh bor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM ) techniques. The RF algorithm demonstrated the highest performance with an accu racy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693 . The SVM achieved the highest recall value of 0.727. Based on the overall perfo rmance, the RF emerged as the best machine learning algorithm, whereas the DT wa s the least suitable."

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Research from Boston University Broadens Understanding of Machine Learning (DREA MER: a computational framework to evaluate readiness of datasets for machine lea rning)

80-81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 artific ial intelligence. According to news reporting from Boston University by NewsRx j ournalists, research stated, "Machine learning (ML) has emerged as the predomina nt computational paradigm for analyzing large-scale datasets across diverse doma ins." Funders for this research include National Institutes of Health. The news correspondents obtained a quote from the research from Boston Universit y: "The assessment of dataset quality stands as a pivotal precursor to the succe ssful deployment of ML models. In this study, we introduce DREAMER (Data REAdine ss for MachinE learning Research), an algorithmic framework leveraging supervise d and unsupervised machine learning techniques to autonomously evaluate the suit ability of tabular datasets for ML model development. DREAMER is openly accessib le as a tool on GitHub and Docker, facilitating its adoption and further refinem ent within the research community.. The proposed model in this study was applied to three distinct tabular datasets, resulting in notable enhancements in their quality with respect to readiness for ML tasks, as assessed through established data quality metrics. Our findings demonstrate the efficacy of the framework in substantially augmenting the original dataset quality, achieved through the elim ination of extraneous features and rows. This refinement yielded improved accura cy across both supervised and unsupervised learning methodologies."

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New Robotics Study Findings Have Been Reported by Investigators at Chinese Acade my of Sciences (Vision-based Docking System for an Aromatic-hydrocarbon-inspired Reconfigurable Robot)

81-82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 Robotics have been published. According to news reporting originating from Shenyang, People's Republic of China, by NewsRx correspondents, research stated, "Aromatic hydroca rbons generally refer to compounds containing benzene rings. Many types of isome rs can be formed by replacing hydrogen atoms on the benzene ring." Financial supporters for this research include National Key R&D Pro gram of China, CAS Interdisciplinary Innovation Team, National Natural Science F oundation of China (NSFC). Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "In this paper, an aromatic-hydrocarbon-inspired modular robot (AHIMR) is proposed. The robot can be reassembled into different configurations suitabl e for various task requirements. A vision-based docking system is designed for t he AHIMR. The system primarily consists of two stages: a remote guidance stage a nd a precise docking stage. During the remote guidance stage, an object module i s identified using an illumination adaptive target recognition algorithm, and th en the active module moves to the docking area through communication with ZigBee . In the precise docking stage, the active module calculates the relative pose w ith the object module using a perspective-n-point method and dynamically adjusts its posture to dock. In this process, a Kalman filter is used to reduce target occlusion and jitter interference. In addition, the docking system feasibility i s verified via several simulation experiments. The module docking accuracy is co ntrolled within 0.01 m, which meets the reconfiguration task requirements of the AHIMR."

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Studies from Henan University of Science and Technology Provide New Data on Robo tics (Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scene s Based on Deep Learning)

82-83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 robotics. According to news originating from Luoyang, People's Republic of China , by NewsRx correspondents, research stated, "Traditional automatic gardening pr uning robots generally employ electronic fences for the delineation of working b oundaries." Financial supporters for this research include Longmen Laboratory "trendy Indust ry Projects". Our news correspondents obtained a quote from the research from Henan University of Science and Technology: "In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a si multaneous localization and mapping (SLAM) system to construct a three-dimension al (3D) semantic map. To reduce the computational cost of its future deployment in resource-constrained mobile robots, we replaced the backbone network of DeepL abv3+, ResNet50, with MobileNetV2 to decrease the number of network parameters a nd improve recognition speed. In addition, we introduced an efficient channel at tention network attention mechanism to enhance the accuracy of the neural networ k, forming an improved Multiclass MobileNetV2 ECA DeepLabv3+ (MM-ED) network mod el. Through the integration of this model with the SLAM system, the entire frame work was able to generate a 3D semantic point cloud map of a lawn working area a nd convert it into octree and occupancy grid maps, providing technical support f or future autonomous robot operation and navigation. We created a lawn dataset c ontaining 7500 images, using our own annotated images as ground truth. This data set was employed for experimental purposes."

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Research from University of Alberta Has Provided New Study Findings on Machine L earning (Application of the Machine Learning Method to Determine Spring Load Lim its and Winter Weight Premium)

83-84页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news originating from Edmonton, Canada, by NewsR x correspondents, research stated, "Freight transportation plays a crucial role in sustaining the Canadian economy." Our news journalists obtained a quote from the research from University of Alber ta: "However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Pre mium (WWP) is used to reduce the impact of SLR on trucking operations by allowin g higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of th e pavement. Different methods have been proposed to improve the application of S LR and WWP; however, they rely mainly on indirect indices, such as the cumulativ e thawing index and cumulative freezing index, which pose challenges in their ca lculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a n ovel approach, machine learning models directly derive the start and end dates o f SLR and WWP from frost and thaw depths in the pavement structure which are det ermined by pavement temperature and moisture content."

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Researchers Submit Patent Application, 'Protocol Simulation In A Virtualized Rob otic Lab Environment', for Approval (USPTO 20240181642)

84-87页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-From Washington, D.C., NewsRx journali sts report that a patent application by the inventors Budd, Geoffrey J. (Redwood City, CA, US); Honda, Alexander Li (Sunnyvale, CA, US); Sganga, Jake (Los Angel es, CA, US); Singh, Nikhita (Palo Alto, CA, US); Washington, Jeff (Round Rock, T X, US), filed on January 18, 2024, was made available online on June 6, 2024. No assignee for this patent application has been made. News editors obtained the following quote from the background information suppli ed by the inventors: "In traditional lab environments, human operators work thro ughout the lab to perform protocols with equipment and reagents. For example, a human operator may mix reagents together, manually calibrate a robot arm, and op erate a pipettor robot to handle liquids. However, in some instances, a lab may include components (e.g., equipment, robots, etc.) that can be automated to perf orm protocols.

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