查看更多>>摘要：2024 MAY 29 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Urogenital Diseases an d Conditions - Hypospadias is the subject of a report. According to news reporti ng originating from Chengdu, People's Republic of China, by NewsRx correspondent s, research stated, "To describe the anatomical abnormalities of hypospadias bef ore puberty using current commonly used anthropometric index data and predict po stoperative diagnostic classification. Children with hypospadias before puberty who were initially treated at Sichuan Provincial People's Hospital from April 20 21 to September 2022 were selected."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Artificial Intelligen ce have been presented. According to news originating from Guangzhou, People's R epublic of China, by NewsRx correspondents, research stated, "The combination of artificial intelligence methods and multisensory is crucial for future intellig ent battery management systems (BMSs). Among multisensing technologies in batter ies, simultaneously monitoring the strain and temperature is essential to determ ine the batteries' safety and state of charge (SoC)." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the University of J inan, "However, the combination still faces a few challenges, such as obtaining multisensing parameters with only one simple and easy-to-fabricate sensor, and h ow to use artificial intelligence and measurement parameters such as strain and temperature for effective modeling. To address these, we propose a novel sensing technique based on a compact dual-diameter fiber Bragg gratings (FBGs) sensor c apable of being attached to the surface of a working lithium-ion pouch cell to s imultaneously monitor the battery's surface strain and temperature. Then, based on the collected data of strain and temperature, we have constructed deep neural network (DNN) models with different inputs to realize accurate battery SoC esti mation with high resistance to electromagnetic interference. Based on our DNN mo dels, the experimental results show that strain and temperature information can be used as supplementary parameters for improved SoC estimation (accuracy increa sed from 97.40% to 99.94%). Meanwhile, we also find t hat by just using the strain and temperature information obtained by the optical fiber sensor, the SoC estimation can be achieved without the voltage and curren t inputs."
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 Ma chine Learning. According to news originating from Dubendorf, Switzerland, by Ne wsRx correspondents, research stated, "Protection of groundwater resources is es sential to ensure quality and sustainable use. However, predicting vulnerability to anthropogenic pollution can be difficult where data are limited." Funders for this research include Swiss Federal Office of Energy (SFOE), Norwegi an Agency for Development Cooperation - NORAD. Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Aquatic Science and Technology (EAWAG), "This is particularly true i n the Sahel region of Africa, which has a rapidly growing population and increas ing water demands. Here we use groundwater measurements of tritium (3H) with mac hine learning to create an aquifer vulnerability map (of the western Sahel), whi ch forms an important basis for sustainable groundwater management. Modelling sh ows that arid areas with greater precipitation seasonality, higher permeability and deeper wells or water table generally have older groundwater and less vulner ability to pollution. About half of the modelled area was classified as vulnerab le. Groundwater vulnerability is based on recent recharge, implying a sensitivit y also to a changing climate, for example, through altered precipitation or evap otranspiration. This study showcases the efficacy of using tritium to assess aqu ifer vulnerability and the value of tritium analyses in groundwater, particularl y towards improving the spatial and temporal resolution. Assessing the resilienc e of groundwater resources can be challenging in data-sparse regions."
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 ar tificial intelligence. According to news reporting from Xiangtan, People's Repub lic of China, by NewsRx journalists, research stated, "To investigate the impact of structural damages on the comfort level of suspension footbridges under huma n-induced vibrations, this study addresses the limitations of traditional manual testing, which often entails significant manpower and material resources. The a im is to achieve rapid estimation and health monitoring of comfort levels during bridge operation." Financial supporters for this research include Construction of Innovative Provin ces in Hunan Province. The news journalists obtained a quote from the research from Xiangtan University : "To accomplish this, the study combines finite-element simulation results to e stablish a data-driven library and introduces three distinct machine learning al gorithms. Through comparative analysis, a machine learning-based method is propo sed for quick evaluation of bridge comfort levels. Focusing on the Yangjiadong S uspension Bridge, the study evaluates and researches the comfort level of the st ructure under the influence of human-induced vibrations. The findings revealed a relatively low base frequency and high flexibility. Additionally, when consider ing the mass of individuals, peak acceleration decreased. The predictive perform ance of the Artificial Neural Network (ANN) model was found to be superior when accounting for multi-parameter damages, yielding root mean square error (RMSE), mean absolute percentage error (MAPE), and Rsquared (R2) values of 0.03, 0.02, and 0.98, respectively. Moreover, the error ratio of the generalization performa nce analysis was below 5%."
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 Ma chine Learning. According to news reporting originating in Montreal, Canada, by NewsRx journalists, research stated, "Upcoming experiments will map the spatial distribution of the 21-cm signal over three-dimensional volumes of space during the Epoch of Reionization (EoR). Several methods have been proposed to mitigate the issue of astrophysical foreground contamination in tomographic images of the 21-cm signal, one of which involves the excision of a wedgeshaped region in cy lindrical Fourier space." Funders for this research include Canadian Institute for Advanced Research (CIFA R), Trottier Space Institute, New Frontiers in Research Fund Exploration grant p rogram, Canadian Institute for Advanced Research (CIFAR), Natural Sciences and E ngineering Research Council of Canada (NSERC), Alfred P. Sloan Foundation, Willi am Dawson Scholarship at McGill.
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 out of Beijing, People's Repu blic of China, by NewsRx editors, research stated, "With the widespread applicat ion of machine learning in various fields, enhancing its accuracy in hydrologica l forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale strea mflow and explores the application of the Lasso feature selection method alongsi de three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction." Funders for this research include National Key R&D Program of China , National Natural Science Foundation of China. Our news journalists obtained a quote from the research from Capital Normal Univ ersity, "Through comparative experiments, we found that the Lasso method signifi cantly enhances the model's performance, with a respective increase in the gener alization capabilities of the three models by 21, 12, and 14%. Amon g the selected features, lagged streamflow and precipitation play dominant roles , with streamflow closest to the prediction date consistently being the most cru cial feature. In comparison to the TTS and RF models, the LSTM model demonstrate s superior performance and generalization capabilities in streamflow prediction for 1-7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery - Adrenalectom y is the subject of a report. According to news reporting originating from Bialy stok, Poland, by NewsRx correspondents, research stated, "Adrenal gland incident aloma is frequently identified through computed tomography and poses a common cl inical challenge. Only selected cases require surgical intervention." Our news editors obtained a quote from the research from the Medical University of Bialystok, "The primary aim of this study was to compare the effectiveness of selected machine learning (ML) techniques in proper qualifying patients for adr enalectomy and to identify the most accurate algorithm, providing a valuable too l for doctors to simplify their therapeutic decisions. The secondary aim was to assess the significance of attributes for classification accuracy. In total, cli nical data were collected from 33 patients who underwent adrenalectomy. Histopat hological assessments confirmed the proper selection of 21 patients for surgical intervention according to the guidelines, with accuracy reaching 64% . Statistical analysis showed that Supported Vector Machines (linear) were signi ficantly better than the baseline (p <0.05), with accuracy reaching 91%, and imaging features of the tumour were found to be the most crucial attributes."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on robotics have been presented. Ac cording to news reporting from Chongqing, People's Republic of China, by NewsRx journalists, research stated, "The complexity of the environment limits the accu racy of the traditional Adaptive Monte Carlo Localization(AMCL) algorithm, which also suffers from high computational effort and particle degradation due to las er model limitations. To address these issues, an optimized AMCL algorithm with a bounding box is proposed." Funders for this research include Wanzhou District Science And Technology Bureau ; General Project of The Natural Science Foundation of Chongqing Science And Tec hnology Commission. Our news reporters obtained a quote from the research from Chongqing Three Gorge s University: "The AMCL algorithm is first parameterized and initialized to the particle swarm. During the particle iteration process, collision detection is pe rformed on the bounding box. If a collision occurs, the particle filter is not u pdated and its particle weight is set to 1. If there is no collision, the partic le filter is updated normally and the particle weight is set to 0. Then, the par ticles are resampled and updated based on the measurement data and motion model. After experimental verification, this method's self-localization trajectory is closer to the actual path, and the measurement error fluctuation is smaller."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in artificial intelli gence. According to news originating from Cracow University of Technology by New sRx editors, the research stated, "The article describes problems related to the construction of an automatic system for teaching human motor activities. Teachi ng these activities in rehabilitation, sports, and professional work is of great importance in both social and individual dimensions." The news editors obtained a quote from the research from Cracow University of Te chnology: "The prospect of using automated systems is therefore highly significa nt. The system can use signals from any motion sensors, e.g., cameras or MEMS (M icro-Electro-Mechanical Systems) inertial sensors. A significant problem is real -time signal analysis. In the system presented, this analysis involves a classif ication process. It enables the selection of an optimal motor learning algorithm for a given situation. The learner is provided with information about required movement corrections through haptic devices. The primary aim of the research des cribed in the article is to identify key features of classification methods that ensure the construction of an effective teaching system. To achieve this goal, three classification methods were statistically tested, namely: a method using C NN (Convolutional Neural Network), a minimum distance method, and a method based on hidden Markov models. The main result of the study is the statement that the key feature of the methods is their interpretability. This property enables the efficient transfer of knowledge from experts to the system and facilitates its improvement."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting from Santiago, Chile, by NewsRx journalists, research stated, "Electric motors play a fundamental rol e in various industries, and their relevance is strengthened in the context of t he energy transition." Funders for this research include Espol; Universidad Tecnica Federico Santa Mari a. The news journalists obtained a quote from the research from Technical Universit y Federico Santa Maria: "Having efficient tools and techniques to detect and dia gnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the m agnitude of the space vector stator current for detecting and diagnosing incipie nt inter-turn short circuits (ITSCs) in induction motors (IMs). The effectivenes s of these indicators was evaluated using four machine learning methods previous ly documented in the literature: random forests (RFs), support vector machines ( SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networ ks (FNNs and RNNs). This assessment was conducted using experimental data."
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