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 reporting from Lisbon, P ortugal, by NewsRx journalists, research stated, "Accounting for aeroelastic phe nomena, such as flutter, in the conceptual design phase is becoming more importa nt as the trend toward increasing the wing aspect ratio forges ahead." Financial supporters for this research include Fundacao Para A Ciencia E A Tecno logia. The news journalists obtained a quote from the research from University of Lisbo n: "However, this task is computationally expensive, especially when utilizing h igh-fidelity simulations and numerical optimization. Thus, the development of ef ficient computational strategies is necessary. With this goal in mind, this work proposes a surrogate-based optimization (SBO) methodology for wing design using a predefined machine learning model. For this purpose, a custom-made Python fra mework was built based on different opensource codes. The test subject was the classical Goland wing, parameterized to allow for SBO. The process consists of e mploying a Latin Hypercube Sampling plan and subsequently simulating the resulti ng wing on SHARPy to generate a dataset."
查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Technology is the subj ect of a report. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "In the quest for enhanced precis ion in near-infrared spectroscopy (NIRS), in this study, the application of a no vel BEST-1DConvNet model for quantitative analysis is investigated against conve ntional support vector machine (SVM) approaches with preprocessing such as multi plicative scatter correction (MSC) and standard normal variate (SNV). We assesse d the performance of these methods on NIRS datasets of diesel, gasoline, and mil k using a Fourier Transform Near-Infrared (FT-NIR) spectrometer having a wavelen gth range of 900-1700 nm for diesel and gasoline and 4000-10,000 nm for milk, en suring comprehensive spectral capture." The news correspondents obtained a quote from the research from the Beijing Inst itute of Petrochemical Technology, "The BEST-1DConvNet's effectiveness in chemom etric predictions was quantitatively gauged by improvements in the coefficient o f determination (R2) and reductions in the root mean square error (RMSE). The BE ST-1DConvNet model achieved significant performance enhancements compared to the MSC + SNV + 1D + SVM model. Notably, the R2 value for diesel increased by appro ximately 48.85% despite a marginal RMSE decrease of 0.92% . R2 increased by 11.30% with a 3.32% RMSE reduction for gasoline, and it increased by 8.71%, accompanied by a 3.51% RMSE decrease for milk."
查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on Machine Learning have been prese nted. According to news reporting from Guangdong, People's Republic of China, by NewsRx journalists, research stated, "Machine learning algorithms have been wid ely used to establish online portfolio selection strategies. Meta-algorithm, one of the machine learning algorithms, has the advantage of combining different ba se expert algorithm, which can greatly reduce the risk of choosing wrong experts , especially in the case of the base expert algorithm being very sensitive to th e expert selection." Financial supporters for this research include Ministry of Education, China, Gua ngdong Basic and Applied Basic Research Foundation, Philosophy and Social Scienc es Planning Project of Guangdong Province.
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 - Ro botics and Automation have been published. According to news reporting originati ng in Nanjing, People's Republic of China, by NewsRx journalists, research state d, "Point cloud registration is a fundamental task in various intelligence appli cations, including simultaneous localization and mapping as well as scene recons truction. However, in large-scale scenes, the majority of point clouds exhibit p artial overlap, posing a significant challenge to the registration process." Financial support for this research came from Science Fund for Creative Research Groups. The news reporters obtained a quote from the research from Southeast University, "This study introduces a registration network, named OKR-Net, specifically desi gned to efficiently align partially overlapping point clouds. The OKR-Net compri ses two innovative modules: a joint estimation module adept at identifying the k eypoints within the overlapping region; and a coarse-to-fine registration module designed to aggregate the overlap and descriptor information, thereby reducing the outliers and yielding robust corresponding point pairs. In addition, an over lap labeling method for generated keypoints is introduced. The efficiency of the proposed registration network is validated utilizing two large-scale outdoor da tasets: KITTI and NuScenes."
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 Robotics is now availab le. According to news reporting out of Yangzhou, People's Republic of China, by NewsRx editors, research stated, "In the dynamic landscape of artificial intelli gence and robotics, the pursuit of accurate positioning in mobile robots has int ensified. This research addresses the limitations of single -sensor SLAM (Simult aneous Localization and Mapping) techniques in complex settings by harnessing th e collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Iner tial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors." Financial supporters for this research include National Project of Foreign Exper ts, Bagui Scholars Program of Guangxi Zhuang Autonomous Region, Postgraduate Res earch & Practice Innovation Program of Jiangsu Province (Yangzhou University), National Natural Science Foundation of China (NSFC), Yangzhou Scien ce and Technology, Special Innovation Fund for Medical Innovation and Transforma tion - Clinical Translational Research Project of Yangzhou University, Science a nd Technology on Near-Surface Detection Laboratory.
查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting from Nanning, People's Republic of China, by Ne wsRx journalists, research stated, "Accurately estimating the state -of -charge (SOC) and state -of -health (SOH) of lithium batteries used in electric vehicles is critical but challenging. Machine learning advances aid battery health monit oring, but optimizing model performance often requires adjusting hyperparameters which can lead to local optimization issues." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Liuzhou Science Research and Planning Development Project, I nnovation Project of Guangxi Graduate Education. The news correspondents obtained a quote from the research from Guangxi Universi ty, "Gaussian process regression (GPR), one of the commonly used methods, typica lly uses the conjugate gradient method to search for the optimal hyperparameters in lithium -ion battery state estimation, which often results in local optimiza tion. In this paper, the improved firefly algorithm (IFA) is proposed to improve the predictive performance of the GPR model from the internal predictive proces s perspective. To be specific, the four swarm intelligence algorithms are compar ed for hyperparameter optimization and finally a novel IFA-GPR model is proposed . Compared with the traditional conjugate gradient method, the proposed model im proves the accuracy by 6.75 % and 3.12 % in two curr ent conditions for SOC estimation, and by 91.64 % and 78.12 % in two schemes for SOH prediction, respectively. Moreover, compared with other e xisting algorithms, the statistical results again verify the high precision and adaptability of the proposed method in battery diagnosis."
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 Tubingen, Ge rmany, by NewsRx correspondents, research stated, "Digital neuropsychological to ols for diagnosing neurodegenerative diseases in the older population are becomi ng more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined." Our news editors obtained a quote from the research, "The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the s erial reaction time task (SRTT). This study aims to develop and empirically test a digital tablet-based SRTT in older participants with cognitive impairment (Co I) and healthy control (HC) participants. On the basis of the parameters of resp onse accuracy, reaction time, and learning curve, we measure implicit learning a nd compare the HC and CoI groups. A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI-diagnosed by an interdisci plinary team) completed a tablet-based SRTT. They were presented with 4 blocks o f stimuli in sequence and a fifth block that consisted of stimuli appearing in r andom order. Statistical and machine learning modeling approaches were used to i nvestigate how healthy individuals and individuals with CoI differed in their ta sk performance and implicit learning. Linear mixed-effects models showed that in dividuals with CoI had significantly higher error rates (b=-3.64, SE 0.86; z=-4. 25; P<.001); higher reaction times (F=22.32; P<.001); and lower implicit learning, measured via the response increase between s equence blocks and the random block (b=-0.34; SE 0.12; t=-2.81; P=.007). Further more, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an ave rage prediction accuracy of 77.13% (95% CI 74.67% -81.33%). Our results showed that the HC and CoI groups differed su bstantially in their performance in the SRTT. This highlights the promising pote ntial of implicit learning paradigms in the detection of CoI."
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 originating from Suzhou, People's Republic of China , by NewsRx correspondents, research stated, "Machine learning has demonstrated remarkable success in the field of intelligent fault diagnosis. In current machi ne learning systems, models tend to be fixed after training, which makes them ca n only generalize to classes that appear in the training set, and cannot continu ously learn newly emerging classes." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Jiangsu Province. Our news journalists obtained a quote from the research from Soochow University, "However, in real industrial scenarios, industrial data is constantly growing, which requires models to be able to acquire new knowledge while retaining existi ng knowledge. Furthermore, a substantial proportion of the acquired samples in i ndustry are unlabeled and only few samples are labeled. To tackle the above two challenges, this article proposes a novel semi-supervised class incremental broa d network (SSCIBN) for incremental intelligent diagnosis of rotating machinery f aults under limited labeled samples. Specifically, a semisupervised graph embed ding loss function is designed. On the one hand, this loss function can learn th e structural information of a large amount of unlabeled data, which overcomes th e limitation that the scarcity of labeled samples leads to poor model performanc e. On the other hand, local structure information within old and new classes is considered in the continuous learning process to fully learn the unique manifold structure information of different classes, which in turn enhances the discrimi native performance between old and new classes. Furthermore, a novel semisupervi sed class incremental learning mechanism is proposed, which does not need to uti lize the old class data during the incremental learning process, but can effecti vely retain the old class knowledge. The effectiveness of the proposed method is evaluated through multiple mechanical failure increment cases."
查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics are presented i n a new report. According to news reporting from Hannover, Germany, by NewsRx jo urnalists, research stated, "Soft material robotic systems offer inherent safety and flexibility due to their low material stiffness. Therefore, soft material r obots are prone to operate in unknown environments and fulfill tasks that involv e and even exploit contact with the environment." Financial support for this research came from German Research Foundation (DFG). The news correspondents obtained a quote from the research from Leibniz Universi ty Hannover, "Moving to the application of soft robots, incorporating validated contact models in modeling frameworks can be crucial for simulation tasks in, e. g. design optimization, motion planning or control. Cosserat rod models have pro ven themselves not only to be accurate but also computationally efficient for sl ender soft continuum robots (SCRs). However, only recently the topic of contact modeling has been introduced to Cosserat rod frameworks for SCRs. In this letter , for the first time we present and analyze an approach to include contact model ing in a widely used shooting-based Cosserat rod implementation. Evaluation agai nst detailed finite element (FE) simulations indicate comparable accuracy, while the computational time remains a small fraction. Simulated data for the conside red contact scenarios reveal a consistent level of agreement to experimental dat a, with minor discrepancies."
查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Mental Health Diseases and Conditions - Obsessive-Compulsive Disorder is the subject of a report. Acco rding to news originating from Heilongjiang, People's Republic of China, by News Rx correspondents, research stated, "Obsessive-compulsive disorder (OCD) is a hi ghly heterogeneous mental condition with a diverse symptom. Existing studies cla ssified OCD on the basis of conventional phenomenology-based taxonomy ignoring t he fact that the same subtype identified in accordance with clinical symptom may have different mechanisms and treatment responses." Our news journalists obtained a quote from the research from Qiqihar Medical Uni versity, "This research involved 50 medicine-free patients with OCD and 50 match ed healthy controls (HCs). All the participants were subjected to structural and functional magnetic resonance imaging (MRI). Voxel-based morphometry (VBM) and amplitude of low frequency fluctuation (ALFF) were used to evaluate gray matter volume (GMV) and spontaneous neuronal activities at rest respectively. Similarit y network fusion (SNF) was utilized to integrate GMVs and spontaneous neuronal a ctivities, and heterogeneity by discriminant analysis was applied to characteris e OCD subtypes. Two OCD subtypes were identified: Subtype 1 exhibited decreased GMVs (i.e., left inferior temporal gyrus, right supplementary motor area and rig ht lingual gyrus) and increased ALFF value (i.e., right orbitofrontal cortex), w hereas subtype 2 exhibited increased GMVs (i.e., left cuneus, right precentral g yrus, left postcentral gyrus and left hippocampus) and decreased ALFF value (i.e ., right caudate nucleus). Furthermore, the altered GMVs was negatively correlat ed with abnormal ALFF values in both subtype 1 and 2. This study requires furthe r validation via a larger, independent dataset and should consider the potential influences of psychotropic medication on OCD patients' brain activities."