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 Support Vector Machines is now available. According to news reporting from Liaoning, People’s Republic of China, by NewsRx journalists, research stated, “This paper studies the proble m of constructing a robust nonlinear classifier when the data set involves uncer tainty and only the first- and second -order moments are known a priori. A distr ibutionally robust chanceconstrained kernel -free quadratic surface support vect or machine (SVM) model is proposed using the moment information of the uncertain data.” Funders for this research include National Science Foundation (NSF), National Na tural Science Foundation of China (NSFC), Hainan Provincial Natural Science Foun dation of China, Foundation of Yunnan Key Laboratory of Service Computing Grant.
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 out of Zhenjiang, People’s Republic of China, by NewsRx editors, research stated, “During the ferm entation process of Oolong tea, significant changes occur in both its external c haracteristics and its internal components.” Funders for this research include Scientific And Technological Projects of Fujia n Province. The news reporters obtained a quote from the research from Jiangsu University: “ This study aims to determine the fermentation degree of Oolong tea using visible -near-infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projec tion algorithm (SPA) feature selection. Subsequently, traditional machine learni ng and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highes t prediction rates among traditional machine learning models and deep learning m odels with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing p ossesses the capability for rapid nondestructive online determination of the fe rmentation degree of Oolong tea. Additionally, the predictive rate of traditiona l machine learning models exceeds that of deep learning models in this study.”
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 Gulf University for Scienc e and Technology by NewsRx correspondents, research stated, “To better design AI processors, it is critical to characterize artificial intelligence (AI) workloa ds and contrast them to normal personal computer (PC) workloads.” The news journalists obtained a quote from the research from Gulf University for Science and Technology: “In this work, we profiled the AIBench and PassMark Per formanceTest benchmarks with the Intel oneAPI VTune Profiler on a multi-core com puter. We captured and contrasted the various CPU and platform metrics and event counts for these two distinct benchmarks. Using the Orange 3.0 data mining tool , and based on the captured profile metrics and event counts, we then trained an d tested 9 machine learning (ML) models to classify the CPIs and elapsed times o f the various tests of these two benchmarks, including inference and training te sts in AIBench, and CPU, memory, graphics, and disk tests in PassMark. The linea r regression machine learning model emerged as the best clocks per instruction ( CPI) classifier, while the neural network model with 4 hidden layers was the bes t elapsed time classifier. This machine learning classification can help in pred icting the CPI and elapsed time and distinguish between AI and standard PC workl oads based on the profiled application(s) and captured profile metrics and event counts.”
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 Learn ing have been published. According to news reporting originating from Volos, Gre ece, by NewsRx correspondents, research stated, “Walleye ( Sander vitreus ) is a freshwater perciform native to Northern America and Canada with high commercial value. Stocking programs for the species are unable to supply the significant d emand, creating stock management problems and an ecosystemic decline.”
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 reporting from Hefei, People’s Republic of China, by NewsRx journalists, research stated, “This article investigates the impact of H all errors on the speed, rotor position, and current of joint motors in robots, focusing on brushless dc torque motors. On introducing the sensing principle of the Hall sensor in the torque motor system, the influence of circumferential ins tallation errors and axial offset errors on the rotor position and current of th e motor is derived analytically on the basis of the Hall vector signals.” Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Anhui Province, Key Project of Natural Scie nce Research Project of Colleges and Universities in Anhui Province.
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 originating from Changsha, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterb orne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odoran t identification and odor evaluation.” Our news journalists obtained a quote from the research from Hunan University, “ In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS spectra as input features. We demonstrate that model performanc e using MS spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model’s robustness in predic ting odor sensory attributes of unidentified chemicals by using the experimental ly obtained MS spectra from nontarget analysis on authentic water samples. Inter preting the developed models, we identify the intricate interaction of functiona l groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., i n the inherent olfactory mechanisms.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning are discuss ed in a new report. According to news originating from Harbin, People’s Republic of China, by NewsRx correspondents, research stated, “The temperature of a moto r significantly affects its control and lifespan. However, due to the influence of motor structure and operating environment, precise temperature measurement of the motor is challenging with temperature sensors.” Our news journalists obtained a quote from the research from Harbin University, “Therefore, machine learning algorithms are often employed to predict the temper ature more accurately. To enhance motor control, integrating machine learning al gorithm models with the actual motor control terminal is highly beneficial. This paper proposes a Short and Long Term Memory (LSTM) algorithm model based on Har ris’s hawk optimization to predict the temperature of the motor stator, which is applied in actual motor control. Furthermore, it evaluates the tracking perform ance of motor control current. Firstly, an experimental platform for temperature measurement is established to acquire the temperature at different positions of the motor as raw data. Subsequently, the raw data is inputted into three algori thms: LSTM, PSO-LSTM, and HHO-LSTM, for comparison. By comparing evaluation metr ics, it is demonstrated that HHO-LSTM exhibits excellent predictive performance. Furthermore, utilizing diverse segments of the motor as model input sets enhanc es the generalization capability and predictive accuracy of the model.”
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 originating from Guangdong, People’s Re public of China, by NewsRx correspondents, research stated, “Accurately estimati ng the net ecosystem exchange of CO (NEE) in cropland ecosystems is essential fo r understanding the impacts of agricultural practices and climate conditions. Ho wever, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling m odels.” Our news journalists obtained a quote from the research from Sun Yat-sen Univers ity, “Here, we applied an integrated approach that combined object-based image a nalysis (OBIA) techniques with advanced machine learning (ML) approaches to upsc ale regional cropland NEE. We conducted a thorough evaluation of the upscaling a pproach across four distinct cropland areas characterized by diverse climate con ditions. Our study confirmed that OBIA techniques can efficiently segment cropla nd objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorit hm, among the three methods used for ML model integration, demonstrated exceptio nal performance in predicting NEE, with an R value exceeding 0.80 across all stu dy areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorestperforming ML model an d a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing crop land NEE patterns compared to pixel-based products. Additionally, we utilized th e SHapley Additive exPlanations (SHAP) to assess driver importance, revealing th at phenology and radiation had the greatest influence on prediction accuracy, fo llowed by temperature and soil moisture.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on artificial intelligence have bee n presented. According to news reporting from Budapest, Hungary, by NewsRx journ alists, research stated, “Introduction/purpose: The brain wave application is wi despread in recent years, especially in the applications that aid the impaired p eople suffered from amputation or paralysis. The objective of this research is t o assess how well different supervised machine learning algorithms classify brai n signals, with an emphasis on improving the precision and effectiveness of brai n-computer interface applications.” Our news editors obtained a quote from the research from Obuda University: “In t his work, brain signal data was analyzed using a number of well-known supervised learning models, such as Support Vector Machines (SVM) and Neural Networks (NN) . The data set was taken from a previous study. Twenty five participants imagine d moving their right arm (elbow and wrist) while the brain signals were recorded during that process. The dataset was prepared for the analysis by the applicati on of meticulous preprocessing and feature extraction procedures. Then the resul ting data were subjected to classification. The study highlights how crucial fea ture selection and model modification are for maximizing classification results. Supervised machine learning methods have great potential for classifying brain signals, particularly SVM and NN.”
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 originating from Wuhan, People’s Republic of C hina, by NewsRx correspondents, research stated, “The low -frequency chatter (LF C) of the robot body during milling severely constrains the machining efficiency . Taking the classical mode coupling mechanism as the underlying foundation, pre vious studies have developed various stability models to predict the LFC in robo tic milling.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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