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    New Findings from University of Pompeu Fabra Describe Advances in Robotics ("plu g-and-play" Inventory Robots: Autonomous Itinerary Planning Through Autonomous W aypoint Generation)

    20-21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 Ro botics. According to news reporting out of Barcelona, Spain, by NewsRx editors, the research stated, "Current robotic inventory systems rely on human interactio n for installation, configuration, and reconfiguration tasks. However, this depe ndence on human involvement hampers the efficiency of the process chain in the i ndustry and can lead to bottlenecks in the supply chain and the overall system." Financial support for this research came from Spanish Government trough the Mini stry of Science, Innovation and Universities under RF-VOLUTION Project. Our news journalists obtained a quote from the research from the University of P ompeu Fabra, "In this study, we present a ‘plug-and-play' methodology that enabl es the deployment of inventory robots and the autonomous reconfiguration of the map and inventory itineraries. This work introduces, for the first time, an auto nomous waypoint generation method based on radiofrequency identification explora tion and the first fully autonomous solution for designing efficient itineraries for inventory robots. The proposed methodology is extensively detailed, and a s eries of experiments are conducted in a real environment with physical robots."
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    Studies from Beijing Academy of Agricultural and Forestry Sciences Update Curren t Data on Robotics (Peduncle Collision-free Grasping Based On Deep Reinforcement Learning for Tomato Harvesting Robot)

    21-22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 from Beijing, People's Republic of China, by NewsRx journalists, research stated, "Collision-fros grasping of the t hin, brief peduncies connecting sherry tomate clusters to the main stem was cruc ial for tomato harvesting robots. Recognising that the optimal operating posture for each individual poduncle was various, this study proposed a naval pedumele grasping posture decision model using deep rein forcement learning (DRL) for tom ato harvesting manipulators, to ove is the collision issue caused by Axed- postu re grasping." The news correspondents obtained a quote from the research from the Beijing Acad emy of Agricultural and Forestry Sciences, "This model could dynamically generat ed action sequences for the harvesting manipulator, ensuring that the end-effect ar approach to the peduncle along the collision-fros path with the optimal grasp ing posture. Building upon price research inte the multi-task identification of tomato clusters, peduncles, and the main stom, a keypoint-based spatial pose des cription model for tomate bunches was devised. Through this, the optimal operati ng pesture for the and effector on the peduncle was established. An improved HER -SAC (Soft Actor Critic with Hindsight Experience Replay) algorithm was subseque ntly established to guide the and-effector in collision-free grasping motions. T he reward function of this algorithm incorporated end-effccser posture constrain ts obtained from the optimal posture plans. In the training phase, a heuristic s trategy model, providing prior knowizdgs, was marged with a dynamic guin module to sidestep local optimal policies, collectiv enhancing the learning efficiency In the simulation, our method improved the success rate of the peduncle grasping by at least 14 %, compared with SAC, HER DOFC and HER-TD3. For the identical scenarios, improved HER-SAC reached the desired posture with a minimu m of 15.5 % fewer stops compared to ather algorithms. In Feld cxpe riments conducted in tomate greenhouses, the robot schieved a harvesting success rate of 35.5 which was an increase of 57.31% and 43.0 1% compared to traditional methods with food horizontal and parallel to-main-stem p ostures, respectively."
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    Researcher at University of Costa Rica Publishes New Data on Artificial Intellig ence (El Assessing artificial intelligence and professors' calibration in Englis h as a foreign language writing courses at a Costa Rican public university)

    22-23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 out of San Jose, Costa Rica, by Ne wsRx editors, research stated, "This article paper explores the evaluation of ar tificial intelligence (AI) in English as a Foreign Language (EFL) writing course s and the importance of calibration in writing evaluations. The role of calibrat ion has received little attention in language contexts, while the role of artifi cial intelligence has gained increased attention in the last couple of years." The news correspondents obtained a quote from the research from University of Co sta Rica: "This investigation, conducted from August 2022 to March 2023, involve d eight TESOL students enrolled in an English as a Foreign Language (EFL) major at a Costa Rican public university, ten TESOL university professors, and one AI piece of software. It used a quantitative, quasi-experimental design, and a lang uage elicitation data collection process. Data was collected by means of a rubri c-based writing assessment. Quantitative data were analyzed using descriptive st atistics. Data analyses indicate that: 1) humancreated paragraphs (X = 7,56) an d AI writing (X = 7,61) yield similar results when evaluated; 2) some criteria m ay favor human creativity or computer, rule-oriented writing; and 3) professors' ratings reveal inconsistencies when grading human writing in particular. These findings demonstrate that AI matches, at least to a basic level, human writing s kills." According to the news reporters, the research concluded: "Furthermore, data show that students may be falling behind in aspects such as grammar, vocabulary, and mechanics. Finally, the analysis indicates that professors' grading lacks consi stency, and a calibration model should be incorporated as part of regular traini ng workshops."
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    New Machine Learning Study Findings Have Been Reported by Researchers at Chinese Academy of Sciences (Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analy sis ...)

    23-24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 B eijing, People's Republic of China, by NewsRx correspondents, research stated, " Generating orchards spatial distribution maps within a heterogeneous landscape i s challenging and requires fine spatial and temporal resolution images." Financial supporters for this research include National Natural Science Foundati on of China. Our news correspondents obtained a quote from the research from Chinese Academy of Sciences: "This study examines the effectiveness of Sentinel-1 (S1) and Senti nel-2 (S2) satellite data of relatively high spatial and temporal resolutions fo r discriminating major orchards in the Khairpur district of the Sindh province, Pakistan using machine learning methods such as random forest (RF) and a support vector machine. A Multicollinearity test (MCT) was performed among the multi-te mporal S1 and S2 variables to remove those with high correlations. Six different feature combination schemes were tested, with the fusion of multi-temporal S1 a nd S2 (scheme-6) outperforming all other combination schemes. The spectral separ ability between orchards pairs was assessed using Jeffries-Matusita (JM) distanc e, revealing that orchard pairs were completely separable in the multi-temporal fusion of both sensors, especially the indistinguishable pair of dates-mango. Th e performance difference between RF and SVM was not significant, SVM showed a sl ightly higher accuracy, except for scheme-4 where RF performed better. This stud y concludes that multi-temporal fusion of S1 and S2 data, coupled with robust ML methods, offers a reliable approach for orchard classification."
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    Capital Medical University Reports Findings in Artificial Intelligence (Identify ing IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: M anual recognition followed by artificial intelligence recognition)

    24-25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 originating from Beijing, People 's Republic of China, by NewsRx correspondents, research stated, "The T2-FLAIR m ismatch sign (T2FM) has nearly 100% specificity for predicting IDH -mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2% -56.0% of astrocytomas demonstrate a positive T2FM." Funders for this research include National Natural Science Foundation of China, National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation. Our news journalists obtained a quote from the research from Capital Medical Uni versity, "Methods must be considered for distinguishing astrocytomas from negati ve T2FM gliomas. In this study, positive T2FM gliomas were manually distinguishe d from nonenhancing gliomas, and then a support vector machine (SVM) classificat ion model was used to distinguish astrocytomas from negative T2FM gliomas. Nonen hancing gliomas (regardless of pathological type or grade) diagnosed between Jan uary 2022 and October 2022 ( = 300) and November 2022 and March 2023 ( = 196) wi ll comprise the training and validation sets, respectively. Our method for disti nguishing astrocytomas from nonenhancing gliomas was examined and validated usin g the training set and validation set. The specificity of T2FM for predicting as trocytomas was 100% in both the training and validation sets, whil e the sensitivity was 42.75% and 67.22%, respectively . Using a classification model of SVM based on radiomics features, among negativ e T2FM gliomas, the accuracy was above 85% when the prediction sco re was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas."
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    New Intelligent Systems Study Findings Have Been Reported from School of Intelli gent Manufacturing (Association rules combined fuzzy decision quality control te chnology in intelligent manufacturing)

    25-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on intelligent systems are presented i n a new report. According to news originating from the School of Intelligent Man ufacturing by NewsRx editors, the research stated, "In the context of the rapid development of intelligent manufacturing, effective quality control has become t he key to improving manufacturing efficiency and product quality. Traditional qu ality control methods are often inadequate when faced with complex production da ta and changing manufacturing environments." Our news reporters obtained a quote from the research from School of Intelligent Manufacturing: "Therefore, exploring new intelligent quality control technologi es to cope with these challenges in intelligent manufacturing has become an impo rtant research direction. In view of this, the study proposed a quality control technology that combines association rules and fuzzy decision-making. Firstly, a ssociation rule mining methods are used to analyze production data and extract t he relationships between key quality factors. Secondly, based on these associati on rules, fuzzy decision technology is used to adjust and optimize the productio n process, ultimately achieving quality control of products in the intelligent m anufacturing production process. The data showed that when running on the traini ng set and validation set, the research method reached a stable state at 18 and 46 iterations of the system, respectively, with a minimum cost loss function val ue. In both batch production lines, the detection efficiency under the operation of the research method remained at 2200 units per minute. During the process of repeating the system for 6 times, the research method consistently achieved max imum control accuracy and minimum time consumption."
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    Researchers at Bauhaus-Universitat Weimar Release New Study Findings on Machine Learning (Utilizing advanced machine learning approaches to assess the seismic f ragility of non-engineered masonry structures)

    26-27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 Weimar, Germany, by NewsRx editors, the research stated, "Seismic fragility assessment provides a su bstantial tool for assessing the seismic resilience of these buildings. However, using traditional numerical methods to derive fragility curves poses significan t challenges." Funders for this research include Deutsche Forschungsgemeinschaft; Bauhaus-unive rsitat Weimar. Our news editors obtained a quote from the research from Bauhaus-Universitat Wei mar: "These methods often overlook the diverse range of buildings found in diffe rent regions, as they rely on standardized assumptions and parameters. Consequen tly, they may not accurately capture the seismic response of various building ty pes. Alternatively, extensive data collection becomes essential to address this knowledge gap by understanding local construction techniques and identifying the relevant parameters. This data is crucial for developing reliable analytical ap proaches that can accurately derive fragility curves. To overcome these challeng es, this research employs four Machine Learning (ML) techniques, namely Support Vector Regression (SVR), Stochastic Gradient Descent (SGD), Random Forest (RF), and Linear Regression (LR), to derive fragility curves for probability of collap se in terms of Peak Ground Acceleration (PGA). To achieve the research objective, a comprehensive input/output dataset consisting of on-site data collected from 646 masonry walls in Malawi is used. Adopted ML models are trained and tested u sing the entire dataset and then again using only the most highly correlated fea tures. The study includes a comparative analysis of the efficiency and accuracy of each ML approach and the influence of the data used in the analyses. Random F orest (RF) technique emerges as the most efficient ML approach for deriving frag ility curves for the surveyed dataset in terms of achieved lowest values for eva luation metrics of the ML methods."
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    University Hospital Reports Findings in Hyperkalemia (Severe Hyperkalemia During a Robot-Assisted Total Radical Prostatectomy in a Patient with Stage 3a Chronic Kidney Disease: A Case Report)

    26-26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 Nutritional and Metabo lic Diseases and Conditions-Hyperkalemia is the subject of a report. According to news reporting from Yvoir, Belgium, by NewsRx journalists, research stated, "A 63-year-old man with stage 3a chronic kidney disease (CKD) and mild hyperkale mia was scheduled for a robot-assisted prostatectomy. He was being treated with lisinopril." The news correspondents obtained a quote from the research from University Hospi tal, "Owing to mild hyperkalemia (6.2 mmol/L), lisinopril was discontinued, and sodium polystyrene sulfonate was administered on the day before surgery. Three h ours after incision, electrocardiographic signs of hyperkalemia manifested with the serum potassium concentration rising to 8 mmol/L." According to the news reporters, the research concluded: "Although hyperkalemia is a common and well-documented side effect of angiotensin-converting enzyme inh ibitors in patients with CKD, we report an extreme increase in potassium within a very short time period despite prior drug discontinuation." This research has been peer-reviewed.
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    University of Malaysia Sabah Researchers Add New Data to Research in Machine Lea rning (A Survey on Vehicular Traffic Flow Anomaly Detection Using Machine Learni ng)

    27-28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 reporting from the University of Malaysia S abah by NewsRx journalists, research stated, "Vehicular traffic flow anomaly det ection is crucial for traffic management, public safety, and transportation effi ciency." The news journalists obtained a quote from the research from University of Malay sia Sabah: "It assists experts in responding promptly to abnormal traffic condit ions and making decisions to improve the traffic flow. This survey paper offers an overview of the application of machine learning to detect anomalies in the tr affic flow. Through an extensive review of the literature from the Scopus databa se, this paper explores the technical aspects of traffic flow anomaly detection using machine learning, including data sources, data processing approaches, mach ine learning algorithms, and evaluation metrics." According to the news reporters, the research concluded: "Additionally, the pape r highlights the emerging research opportunities for researchers in enhancing tr affic flow anomaly detection using machine learning."
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    Researcher at Shanghai Jiao Tong University Discusses Research in Robotics (Opti mization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dy namic Response)

    28-29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 subjec t of a new report. According to news reporting out of Shanghai, People's Republi c of China, by NewsRx editors, research stated, "With the advantages of large wo rking space, low cost and more flexibility, industrial robots have become an imp ortant carrier in intelligent manufacturing." Financial supporters for this research include China Postdoctoral Science Founda tion; National Key Research And Development Program of China For Robotics Serial ized Harmonic Reducer Fatigue Performance Analysis And Prediction And Life Enhan cement Technology Research. Our news editors obtained a quote from the research from Shanghai Jiao Tong Univ ersity: "Due to the low rigidity of robotic milling systems, cutting vibrations are inevitable and have a significant impact on surface quality and machining ac curacy. To improve the machining performance of the robot, a posture optimizatio n approach based on the dynamic response index is proposed, which combines postu redependent dynamic characteristics with surface quality for robotic milling. F irst, modal tests are conducted at sampled points to estimate the posture-depend ent dynamic parameters of the robotic milling system. The modal parameters at th e unsampled points are further predicted using the inverse distance weighted met hod. By combining posture-independent modal parameters with calibrating the cutt ing forces, a dynamic model of a robotic milling system is established and solve d with a semi-discretization method."
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