期刊,Robotics & Machine Learning Daily News 2024年卷Feb.12期_国家学术搜索

期刊信息/Journal information

Robotics & Machine Learning Daily News

NewsRx

主办单位：NewsRx

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

正式出版

收录年代

Study Results from Department of CSE in the Area of Machine Learning Reported (Research On Iot-based Hybrid Electrical Vehicles Energy Management Systems Using Machine Learning-based Algorithm)

65-66页

查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Tamil Nadu, India, by NewsRx editors, the research stated, "Electric vehicles (EVs) are quickly becoming a staple of smart transportation in applications involving smart cities due to their ability to reduce carbon footprints. However, the widespread use of electric vehicles significantly strains the nation's electrical system." Our news editors obtained a quote from the research from the Department of CSE, "In-depth descriptions of the EV's energy management system (EMS) should highlight the vehicle's powertrain's vital role. The energy for propulsion in electric automobiles comes from a rechargeable battery. The safe and dependable operation of batteries in electric vehicles relies heavily on online surveillance and status estimations of charges. An energy management strategy (EMS) that considers the electric vehicle's battery and ultra-capacitor may lessen the vehicle's reliance on external power sources and extend the battery's lifespan. A machine learning-based mathematical dynamic programming algorithm is used in designing the energy management system to teach the system how to respond appropriately to various situations without resorting to predefined rules. Therefore, this research aims to use Machine Learning to create a Smart Energy Management System for Hybrid Electrical Vehicles (SEMS-HEV) with energy storage. Energy optimization techniques and algorithms are necessary in this setting to reduce expenses and length of charging and appropriately arrange the EV charging process to prevent bursts in the electrical supply that may impact the transmission network. To improve the performance of an energy management system, this study employs an IoTbased smart charging system for scheduling V2G connections for hybrid electrical vehicles."

原文链接:

NETL
NSTL
Elsevier

Research in the Area of Artificial Intelligence Reported from Yibin University (Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants)

66-67页

查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting originating from Sichuan, People's Republic of China, by NewsRx correspondents, research stated, "Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production." Our news journalists obtained a quote from the research from Yibin University: "Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses."

原文链接:

NETL
NSTL
Imr Press

Jianghan University Reports Findings in Machine Learning (Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation)

67-68页

查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting from Wuhan, People's Republic of China, by NewsRx journalists, research stated, "Over the past few decades, extensive research has indicated that exposure to bisphenol A (BPA) increases the health risks in humans. Toxicological studies have demonstrated that BPA can bind to the androgen receptor (AR), resulting in endocrine-disrupting effects." The news correspondents obtained a quote from the research from Jianghan University, "In recent investigations, many alternatives to BPA have been detected in various environmental media as major pollutants. However, related experimental evaluations of BPA alternatives have not been systematically implemented for the assessment of chemical safety and the effects of structural characteristics on the antagonistic activity of the AR. To promote the green development of BPA alternatives, high-throughput toxicological screening is fundamental for prioritizing chemical tests. Therefore, we proposed a hybrid deep learning architecture that combines molecular descriptors and molecular graphs to predict AR antagonistic activity. Compared to previous models, this hybrid architecture can extract substantial chemical information from various molecular representations to improve the model's generalization ability for BPA alternatives. Our predictions suggest that lignin-derivable bisguaiacols, as alternatives to BPA, are likely to be nonantagonist for AR compared to bisphenol analogues. Additionally, molecular dynamics (MD) simulations identified the dihydrotestosterone-bound pocket, rather than the surface, as the major binding site of bisphenol analogues. The conformational changes of key helix H12 from an agonistic to an antagonistic conformation can be evaluated qualitatively by accelerated MD simulations to explain the underlying mechanism."

原文链接:

NETL
NSTL

Study Data from University of Minho Update Knowledge of Robotics (Development and Implementation of a New Approach for Posture Control of a Hexapod Robot To Walk In Irregular Terrains)

68-69页

查看更多>>摘要：Data detailed on Robotics have been presented. According to news reporting originating from Guimaraes, Portugal, by NewsRx correspondents, research stated, "The adaptability of hexapods for various locomotion tasks, especially in rescue and exploration missions, drives their application. Unlike controlled environments, these robots need to navigate ever-changing terrains, where ground irregularities impact foothold positions and origin shifts in contact forces." Funders for this research include Fundacao para a Ciencia e a Tecnologia (FCT), Fundacao para a Ciencia e a Tecnologia (FCT), European Social Fund through the Programa Operacional Regional Norte, Fundacao para a Ciencia e a Tecnologia (FCT).

原文链接:

NETL
NSTL
Cambridge Univ Press

Findings in the Area of Machine Translation Reported from Shandong Management University (Automatic Recognition of Machine English Translation Errors Using Fuzzy Set Algorithm)

69-70页

查看更多>>摘要：Investigators discuss new findings in Machine Translation. According to news reporting from Shandong, People's Republic of China, by NewsRx editors, the research stated, "Fuzzy sets demonstrate remarkable efficacy in addressing a wide range of challenges in real-world domains, surpassing the capabilities of traditional approaches. These disciplines include data analysis, machine learning, decision theory, data mining, recognition tasks, intelligence, and hybrid systems." The news correspondents obtained a quote from the research from Shandong Management University, "As a result, the application of fuzzy sets extends to diverse areas, such as robotics, intelligent systems, medical and satellite systems, decision-making in consumer electronics, information processing, pattern recognition, and optimization. Nowadays, one of the applications, language recognition, is a particular issue-precisely, the error frequency in the machine language translator. The frequency of errors in simple machine English translation is increasing day by day. With modern information technology's continuous evolution and development, simple machine translation has yet to meet people's normal needs. This research paper presents a novel machine translation framework founded on automatic error detection. In the realm of machine translation, effectively incorporating user feedback alongside linguistic knowledge remains a challenge. To address this complexity, the study advocates employing a machine learning technique, specifically the fuzzy set algorithm, to extract valuable insights. These insights are instrumental in refining machine-generated translations into more standardized, accurate outputs. The application of this knowledge to other machine translations aims to rectify common errors, ultimately enhancing the overall usability of machine translation systems. Through iterative experiments, the study expanded its set of translation rules, extracting 50 and 100 rules by iteratively adjusting translations through addition, deletion, and modification. Interestingly, the research found that an excessive number of iterations did not necessarily lead to improved translation quality; instead, stabilization occurred after rule sequences. Additionally, the study delved into automatic error identification in machine-generated English translations, introducing automatic post-editing technology to significantly enhance translation quality."

原文链接:

NETL
NSTL
Springer Nature

New Robotics Study Findings Have Been Reported from University of Potsdam ('Ick bin een Berlina': dialect proficiency impacts a robot's trustworthiness and competence evaluation)

70-70页

查看更多>>摘要：Investigators publish new report on robotics. According to news originating from Potsdam, Germany, by NewsRx correspondents, research stated, "Robots are increasingly used as interaction partners with humans. Social robots are designed to follow expected behavioral norms when engaging with humans and are available with different voices and even accents." Our news editors obtained a quote from the research from University of Potsdam: "Some studies suggest that people prefer robots to speak in the user's dialect, while others indicate a preference for different dialects. Our study examined the impact of the Berlin dialect on perceived trustworthiness and competence of a robot. One hundred and twenty German native speakers (Mage = 32 years, SD = 12 years) watched an online video featuring a NAO robot speaking either in the Berlin dialect or standard German and assessed its trustworthiness and competence. We found a positive relationship between participants' self-reported Berlin dialect proficiency and trustworthiness in the dialect-speaking robot. Only when controlled for demographic factors, there was a positive association between participants' dialect proficiency, dialect performance and their assessment of robot's competence for the standard Germanspeaking robot. Participants' age, gender, length of residency in Berlin, and device used to respond also influenced assessments. Finally, the robot's competence positively predicted its trustworthiness."

原文链接:

NETL
NSTL
Frontiers Media Sa

China Academy of Engineering Physics Researchers Update Knowledge of Machine Learning (Machine learning-based prediction and interpretation of decomposition temperatures of energetic materials)

71-71页

查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting from Mianyang, People's Republic of China, by NewsRx journalists, research stated, "Exploring the application of machine learning (ML) in energetic materials (EMs) has been a hot research topic." Financial supporters for this research include National Natural Science Foundation of China. Our news journalists obtained a quote from the research from China Academy of Engineering Physics: "Accordingly, the prediction of the detonation properties of EMs using ML methods has attracted much attention. However, the predictive models for the thermal decomposition temperatures (Td) of EMs have been scarcely reported. Furthermore, the small datasets used in these reports lead to a weak generalization ability of the predictive models. This study created a dataset containing 1022 energetic molecules with Td values of 38-425 °C and determined an optimal predictive model through training. The gradient boost machine for regression (GBR) model yielded a coefficient of determination (R2) of 0.65 and a mean absolute error (MAE) of 27.7 for the test set. This study further explored critical features, determining that the prediction accuracy of the models was significantly influenced by descriptors representing molecular bond stability (i.e., the BCUT metrics) and atomic composition (i.e., the Molecular ID)."

原文链接:

NETL
NSTL
Keai Publishing Ltd

Leibniz-Institut fur Analytische Wissenschaften - ISAS - e.V. Reports Findings in Machine Learning (MMV_Im2Im: an open-source microscopy machine vision toolbox for image-to-image transformation)

71-72页

查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Dortmund, Germany, by NewsRx editors, research stated, "Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source Python package for image-to-image transformation in bioimaging applications." Financial support for this research came from Bundesministerium fur Bildung und Frauen.

原文链接:

NETL
NSTL

Studies from University of Bucharest in the Area of Artificial Intelligence Described (Mapping the conceptual structure of innovation in artificial intelligence research: A bibliometric analysis and systematic literature review)

72-73页

查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from Bucharest, Romania, by NewsRx correspondents, research stated, "This study uses bibliometric analysis and a systematic literature review to map the conceptual structure of artificial intelligence innovations (AI-I) in the social sciences between 2000 and 2023." Our news correspondents obtained a quote from the research from University of Bucharest: "It explicitly focuses on non-economic aspects conducive to AI-I, namely social, technological, cultural, sustainable, personal, moral, and ethical. Our analysis reveals that 1225 articles and proceeding papers have been published, and terms such as ‘technology,' ‘big data,' ‘management,' ‘performance,' ‘future,' and ‘impact' are the most frequently used when discussing innovation and AI. According to our time-zone analysis, the last two years have shown a significant emphasis on concepts such as ‘transformation,' ‘corporate social responsibility,' and ‘resource-based view.' In terms of citations, the countries that receive the highest number of references in the AI-I field are the United Kingdom, the United States, Germany, Australia, and China. The most prolific authors in terms of publications are David Teece, Erik Brynjolfsson, and Anjan Chatterjee. Given that most studies highlight the economic side of AI-I, we selected the most prolific 163 articles from all social science research areas. These studies legitimize the main non-economic aspects that highlight both certainties and uncertainties conducive to such innovations."

原文链接:

NETL
NSTL
Elsevier

Researchers at Shenzhen University Release New Data on Machine Learning (Analysis of the Relationship Between Metro Ridership and Built Environment: a Machine Learning Method Considering Combinational Features)

73-74页

查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting out of Shenzhen, People's Republic of China, by NewsRx editors, research stated, "Limited studies have examined the relationship between combinational features of the built environment and metro ridership. In this study, we applied the gradient boosting regression tree (GBRT) to explore the non-linearity effects for metro commuter ridership and non-commuter ridership, respectively." Financial support for this research came from National Natural Science Foundation of China (NSFC).

原文链接:

NETL
NSTL
Elsevier