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 Beijing, People's Republic of China, by NewsRx journalists, research stated, "Quantum coh erence is a crucial resource in numerous quantum processing tasks." Funders for this research include Nsfc; Fundamental Research Funds For The Centr al Universities. Our news editors obtained a quote from the research from North China Electric Po wer University: "The robustness of coherence provides an operational measure of quantum coherence, which can be calculated for various states using semidefinite programming. However, this method depends on convex optimization and can be tim e-intensive, especially as the dimensionality of the space increases. In this st udy, we employ machine learning techniques to quantify quantum coherence, focusi ng on the robustness of coherence. By leveraging artificial neural networks, we developed and trained models for systems with different dimensionalities."
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 from Hunan, People's Republic of China, by NewsRx journalists, research stated, "The imperative to preserve e nvironmental resources has transcended traditional conservation efforts, becomin g a crucial element for sustaining life. Our deep interconnectedness with the natural environment, which directly impacts our well-being, emphasizes this urgenc y." The news correspondents obtained a quote from the research from Central South Un iversity, "Contaminants such as leachate from landfills are increasingly threate ning groundwater, a vital resource that provides drinking water for nearly half of the global population. This critical environmental threat requires advanced d etection and monitoring solutions to effectively safeguard our groundwater resou rces. To address this pressing need, we introduce the Multifaceted Anomaly Detec tion Framework (MADF), which integrates Electrical Resistivity Tomography (ERT) with advanced machine learning models-Isolation Forest (IF), One-Class Support V ector Machines (OC-SVM), and Local Outlier Factor (LOF). MADF processes and anal yzes ERT data, employing these hybrid machine learning models to identify and qu antify anomaly signals accurately viathe majority vote strategy. Applied to the Chaling landfill site in Zhuzhou, China, MADF demonstrated significant improvem ents in detection capability. The framework enhanced the precision of anomaly de tection, evidenced by higher Youden Index values ( 6.216%), with a 30% increase in sensitivity and a 25% reduction in f alse positives compared to traditional ERT inversion methods. Indeed, these enha ncements are crucial for effective environmental monitoring, where the cost of m issing a leak could be catastrophic, and for reducing unnecessary interventions that can be resource-intensive."
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 Machine Learning-Intelligent Systems. According to news reporting originating in Shanghai, People's Republic of China, by NewsRx journalists, research stated, "The safe and reliable operation of the pitch system is essential for the stabl e and efficient operation of a wind turbine (WT). The pitch fault data collected by supervisory control and data acquisition systems (SCADA) often contain a wid e variety of variables, leading to redundant features that interfere with the ac curacy of final diagnosis results, making it difficult to meet requirements." Financial supporters for this research include European Union (EU), Royal Societ y, Alexander von Humboldt Foundation, BRIEF Award of Brunel University London in the UK, Capacity Building Project of Shanghai Local Colleges and Universities o f China.
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 Salt Lake City, Utah, by N ewsRx correspondents, research stated, "Lithium-ion battery development necessit ates predicting capacity fading using early cycle datato minimize testing time and costs." Funders for this research include National Natural Science Foundation of China; Tsinghua-toyota Joint Research Fund. The news editors obtained a quote from the research from University of Utah: "Th is study introduces a hybrid physics-guided data-driven approach to address this challenge by accurately determining the dominant fading mechanism and predictin g the average capacity fading rate. Physics-guided features, derived from the el ectrochemical properties and behaviors within the battery, are extracted from th e first five cycles to provide meaningful, interpretable, and predictive data. U nlike previous models that rely on a single regression approach, our method util izes two separate regression models tailored to the identified dominant fading m echanisms. Our model achieves 95.6% accuracy in determining the do minant fading mechanism using data from the second cycle and a mean absolute per centage error of 17.09% in predicting lifetime capacity fade from the first five cycles. This represents a substantial improvement over state-of-t he-art models, which have an error rate approximately three times higher."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting originating from Greenvi lle, North Carolina, by NewsRx correspondents, research stated, "Metamorphic tes ting is a valuable approach to verifying machine learning programs where traditi onal oracles are unavailable or difficult to apply." Our news journalists obtained a quote from the research from East Carolina Unive rsity: "This paper proposes atechnique to prioritize metamorphic relations (MRs ) in metamorphic testing for machine learning and deep learning systems, aiming to enhance early fault detection. We introduce five metrics based on diversity i n source and follow-up test cases to prioritize MRs. The effectiveness of our pr oposed prioritization methods is evaluated on three machine learning and one dee p learning algorithm implementation. We compare our approach against random-base d, fault-based, and neuron activation coverage-based MR ordering. The results sh ow that our data diversity-based prioritization performs comparably to fault-bas ed prioritization, reducing fault detection time by up to 62% comp ared to random MR execution. Our proposed metrics outperformed neuron activation coverage-based prioritization, providing 5-550% higher fault dete ction effectiveness."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on robotics have been pr esented. According to news reporting out of Guilin, People's Republic of China, by NewsRx editors, research stated, "A pipeline robot suitable for miniature pip eline detection, namely p-II, was proposed in this paper." Funders for this research include Specific Research Project of Guangxi For Resea rch Bases And Talents. The news journalists obtained a quote from the research from Guilin University o f Electronic Technology: "It features six wheel-leg mobile mechanisms arranged i n a staggered manner, with a monocular fisheye camera located at the center of t he front end. The proposed robot can be used to capture images during detection in miniature pipes with an inner diameter of 120 mm. To efficiently identify the robot's status within the pipeline, such as navigating in straight pipes, curve d pipes, or T-shaped pipes, it is necessary to recognize and classify these spec ific pipeline landmarks accurately. For this purpose, the residual network model ResNet18 was employed to learn from the images of various pipeline landmarks ca ptured by the fisheye camera. A detailed analysis of image characteristics of so me common pipeline landmarks was provided, and a dataset of approximately 908 im ages was created in this paper."
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-An droids have been published. According to news reporting originating from Rome, I taly, by NewsRx correspondents, research stated, "Developing a robotic torso mec hanism is crucial in replicating human mobility in humanoid robots. Previous res earch has presented the LARMbot humanoid's torso as a potential solution, which has now been improved with a novel design proposed in this paper." Financial support for this research came from China Scholarship Council.
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 originating from Ya'an, People's Re public of China, by NewsRx correspondents, research stated, "Apples are suscepti ble to various types of damage during the production process. Such damage not on ly affects the appearance and edibility of the apples but also can result in the infection of healthy apples, leading to secondary economic losses." Financial supporters for this research include China Scholarship Council, Sichua n Agricultural University Double Support Project. Our news editors obtained a quote from the research from Sichuan Agricultural Un iversity, "Therefore, it is crucial to properly handle damaged apples and re-sor t them to enhance their utilization value and optimize resource use. To quickly and accurately identify apple damage and perform sorting in real time, addressin g the resource limitations of mobile devices and the difficulty of extracting de ep network image features, this study proposes a lightweight real-time apple dam age classification network, Fast Fourier ransform Channel Attention (FFTCA)-YOL Ov8n-cls. The FFTCA module focuses on the frequency domain feature information o f images in deep networks, enhancing the network's feature extraction capabiliti es. Additionally, it integrates Convolutional Block Attention Module (CBAM) and Distribution Shifting Convolution to capture channel and spatial information of images in shallow networks and accelerate network inference. Finally, FFTCA-YOLO v8n-cls is compared with typical lightweight classification networks. Experiment al results show that this network has better classification accuracy and faster inference speed. Specifically, the FFTCA-YOLOv8n-cls network is only 0.601 MB in size, achieving a classification accuracy of 96.03%, a recall of 9 6.08%, and an F1-score of 96.05%, demonstrating its fe asibility in real-time apple damage sorting."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Although there is considerabl e research interest in high-entropy alloys (HEAs), only a small fraction of the potential compositional space was explored hitherto. While additive manufacturin g techniques such as the laser powder bed fusion (LPBF) can be gainfully employe d to accelerate discovery of new HEAs with promising properties, the printabilit y of such alloys could restrict such an exploration." Financial supporters for this research include National Key Research & Development Program of China, National Natural Science Foundation of China (NSFC ), Agency for Science Technology & Research (A*STAR), Advanced Mod els for Additive Manufacturing, Instrumental Analysis Center of Shanghai Jiao To ng University and Instrument and equipment sharing platform of School of Materia ls Science and Engineering, SJTU.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on intelligent systems is the subject of a new report. According to news reporting out of Shijiazhuang, People's Republic of China, by NewsRx editors, research stated, "In multimodal se ntiment analysis, achieving effective fusion among text, acoustic, and visual mo dalities for enhanced sentiment prediction is a crucial research topic. Recent s tudies typically employ tensor-based or attention-based mechanisms for multimoda l fusion." Funders for this research include Hebei Province Department of Education; Hebei Provincial Natural Science Foundation.
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