检索结果-内蒙古大学图书馆

Origin of the tribofilm from MoS_(2) nanoparticle oil additives: dependence of oil film thickness on particle aggregation in rolling point contact

引用

Friction 2021年第6期9卷 1436-1449页

作者： Hongxing WU Liping WANG Guangneng dONG State Key Laboratory of Solidification Processing Center of Advanced Lubrication and Seal MaterialsNorthwestern Polytechnical UniversityXi’an 710072China Petro China Lanzhou Lubricating Oil R&D Institute Lanzhou 730060China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi'an Jiaotong UniversityXi'an 710049China

The lubrication effectiveness of MoS_(2) nanoparticles as an oil additive remains unclear,restricting its application in industry to reduce *** goal of this work was to explore the lubrication mechanism of MoS_(2) nanoparticles as an oil *** this study,the oil film thickness behaviors of MoS_(2) nanoparticles in poly-alpha olefin(PAO4)base oil,PAO4 with 3 wt%dispersant(polyisobutyleneamine succinimide,PIBS),and 0W20 engine oil were investigated using an elastohydrodynamic lubrication(EHL)testing *** the EHL tests,the flow patterns around the contact area and the tribofilm covering rate on contact area were studied using optical microscopy to understand the lubrication *** results indicate that both the dispersant and nanoparticle aggregation significantly affected the oil film *** expected oil film thickness increase in the case of 0.1 wt%MoS_(2) in PAO4 base oil was obtained,with an increase from 30 to 60 nm over 15 min at a velocity of 50 mm/*** pattern analysis revealed the formation of particle aggregation on the rolling path when lubricated with 0.1 wt%MoS_(2),which is associated with a tribofilm coverage rate of 41.5%on the contact ***,an oil film thickness increase and particle aggregation were not observed during the tests with 0.1 wt%MoS_(2) blended with 3 wt%PIBS as the dispersant in PAO4 base oil,and for 0.75 wt%MoS_(2) in 0W20 engine *** results suggest that nanoparticles responsible for tribofilm formation originated from aggregates,but not the well-dispersed nanoparticles in point *** understanding should aid the advancement of novel lubricant additive design.

关键词： nanoparticle lubrication oil film thickness flow pattern particle aggregation

来源：评论

学校读者我要写书评

暂无评论

Measuring^(222)rn in aquatic environment via Pulsed Ionization Chamber radon detector

引用

Acta Oceanologica Sinica 2023年第8期42卷 185-189,I0001-I0006页

作者： Lijun Song Wen Liu Shibin Zhao Chunqian Li Jinjia Guo Natasha dimova Bochao Xu Frontiers Science Center for Deep Ocean Multispheres and Earth System/Key Laboratory of Marine Chemistry Theory and Technology of Ministry of Education Ocean University of ChinaQingdao 266100China Laboratory for Marine Ecology and Environmental Science Qingdao National Laboratory for Marine Science and TechnologyQingdao 266071China College of Chemistry and Chemical Engineering Ocean University of ChinaQingdao 266100China College of Information Science and Engineering Ocean University of ChinaQingdao 266100China R&D Center for Marine Instruments and Apparatuses Qingdao Marine Science and Technology CenterQingdao 266200China Department of Geological Sciences College of Arts and SciencesUniversity of AlabamaTuscaloosa 35487USA

radon(rn)is a naturally occurring radioactive inert gas in nature,and^(222)rn has been routinely used as a powerful tracer in various aquatic environmental research on timescales of hours to days,such as submarine groundwater *** we developed a new approach to measure^(222)rn in discrete water samples with a wide range of^(222)rn concentrations using a Pulsed Ionization Chamber(PIC)radon *** sensitivity of the new PIC system is evaluated at 6.06 counts per minute for 1 Bq/L when a 500 mL water sample volume is used.A robust logarithmic correlation between sample volumes,ranging from 250 mL to 5000 mL,and system sensitivity obtained in this study strongly suggests that this approach is suitable for measuring radon concentration levels in various natural *** to the currently available methods for measuring radon in grab samples,the PIC system is cheaper,easier to operate and does not require extra accessories(e.g.,drying tubes etc.)to maintain stable measurements throughout the counting procedure.

关键词： ^(222)rn radon measurement Pulsed Ionization Chamber radon detector radon in discrete water samples submarine groundwater discharge

来源：评论

学校读者我要写书评

暂无评论

Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT 17

Smart Soft Sensor for Froth flotation quality monitoring usi...

引用

17th International Conference on Signal-Image Technology and Internet-Based systems, SITIS 2023

作者： Hasidi, Oussama Bendaouia, Ahmed Abdelwahed, El Hassan Abdellah, El Alaoui-Chrifi Moulay Boussetta, Abdelmalek Benzakour, Intissar Bourzeix, Francois Oubouhss, Kaotar Mohammed Vi Polytechnic University Lisi Laboratory Computer Science Departement Uca Analytics Lab Marrakech Morocco Cadi Ayyad University Lisi Laboratory Computer Science Departement Marrakech Morocco Reminex Managem Group R&d and Engineering Center Marrakech Morocco Mohammed Vi Polytechnic University Analytics Lab Rabat Morocco National School of Applied Science Uae Department of Electrical and Industrial Engineering Tangier Morocco

ISBN: (纸本)9798350370911

Smart sensor technologies have gained prominence in the field of machine vision-based applications. Within this context, the utilization of machine vision systems for monitoring and controlling flotation plants has become increasingly prevalent. This paper addresses the critical need for accurate froth image classification in the development of an online machine vision-based control system. This paper explores the recent advancements in machine learning, with a focus on deep learning techniques. Specifically, it introduces the application of a Vision Transformer (VIT) model as an alternative to the traditional Convolutional Neural Network (CNN) for classifying froth images collected from an industrial lead flotation circuit. The results of this research demonstrate the remarkable performance of the ViT model, achieving an impressive overall accuracy rate of 96% when combined with pre-trained feature extractor models. This achievement surpasses the capabilities of conventional artificial neural network (CNN) classifiers of the state of art in terms of both accuracy and computational efficiency. These findings underscore the promising potential of deep learning neural networks in froth image analysis, highlighting their significance in the advancement of smart sensor-based machine vision systems within the field of flotation plant control. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

research on One-way data Transmission Technology Based on rdMA 7

Research on One-way Data Transmission Technology Based on RD...

引用

7th IEEE International Conference on data Science in Cyberspace, dSC 2022

作者： Yuanpeng, Yang Hongbo, Wang Xiaofeng, Wang Yuan, Liu Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Trs Topwalk Information Technology Co. Ltd. R&d Center Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

One-way data transmission technology is of great significance for data security. To address the problems of low throughput, high central processing unit (CPU) overhead and high delay in traditional one-way data transmission technology based on the User diagram Protocol (UdP), this paper proposes a novel one-way data transmission system, named OdTr, which is based on remote direct Memory Access (rdMA) technology. With the features of zero copy and kernel bypass of rdMA, OdTr significantly improves the performance of one-way data transfers. A remote memory management mechanism and transmission control mechanism are proposed to solve the problems of memory management and packet loss in rdMA unidirectional transmission. Experiments with the premise of accurate and complete data transmission show that applying rdMA technology in place of traditional UdP technology in a one-way data transmission system increases the average throughput by 50.4%, reduces the average delay by 69.7%, and decreases the average CPU overhead of file transmission by 40.7%. The effects of the transmission control mechanism on packet loss are verified by experiments. © 2022 IEEE.

关键词： data communication systems

来源：评论

学校读者我要写书评

暂无评论

Multi-Scales Hybrid Graph Attention Neural Network with Jaccard Correlation Embedding for Link Prediction 4

Multi-Scales Hybrid Graph Attention Neural Network with Jacc...

引用

4th International Conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Mai, Chenghai Lin, Jiayi Zheng, Hewen Zhang, Huanjia Bi, Jinghu Zhou, Jifeng Southern Power Grid Digital Grid Research Institute Co. Ltd Guangdong Provincial Key Laboratory of Digital Grid Technology Marketing Department Guangzhou China Southern Power Grid Digital Grid Research Institute Co. Ltd Guangdong Provincial Key Laboratory of Digital Grid Technology Hardware R&d Department Guangzhou China Yantai Dongfang Wisdom Electric Co. Ltd System Product Department Yantai China

ISBN: (纸本)9798331528850

By predicting the existence of a link in networks, link prediction finds widespread applications in power grid dispatching and security. recently, Graph Auto-Encoders (GAE) based link prediction methods effectively integrate deep learning into link prediction tasks and attract much attention. However, the existence of a link between two nodes may be closely related to their neighboring nodes in different scales, when GAE-based encoding processes only extract features from the adjacency matrix at a single scale, establishing node features that account for varying influences from different neighborhood sizes becomes challenging. To alleviate this situation, this paper proposes a Multi-scales Hybrid Graph Attention Neural Network with Jaccard Correlation Embedding (MHGAT) to address link prediction tasks. Specifically, MHGAT enhances graph node features by integrating Jaccard Correlation, which reflects the neighboring information, as heuristic information. Then, it designs a multi-scale hybrid attention method for extracting link features around multiple neighborhoods for building a graph encoder to model the importance of nodes. By incorporating a designed node correlation-based graph auto-encoding loss, MHGAT extracts multi-scale neighboring correlation features and leverages these features for link prediction. Experimental results demonstrate that compared to commonly used link prediction methods, the proposed MHGAT approach achieves superior performance. The experiments also validate the effectiveness of the designed multi-scale hybrid attention and node correlation embedding. © 2024 IEEE.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

MVd: A Novel Methodology and dataset for Acoustic Vehicle Type Classification

arXiv

引用

arXiv 2023年

作者： Ashhad, Mohd Ahmed, Omar Ambat, Sooraj K. Haq, Zeeshan Ali Alam, Mansaf Dept. of Computer Science Jamia Hamdard Delhi India Naval Physical & Oceanographic Laboratory Defence R&D Organization Kochi India

rising urban populations have led to a surge in vehicle use and made traffic monitoring and management indispensable. Acoustic traffic monitoring (ATM) offers a cost-effective and efficient alternative to more computationally expensive methods of monitoring traffic such as those involving computer vision technologies. In this paper, we present MVd and MVdA: two open datasets for the development of acoustic traffic monitoring and vehicle-type classification algorithms, which contain audio recordings of moving vehicles. The dataset contain four classes—Trucks, Cars, Motorbikes, and a No-vehicle class. Additionally, we propose a novel and efficient way to accurately classify these acoustic signals using cepstrum and spectrum based local and global audio features, and a multi-input neural network. Experimental results show that our methodology improves upon the established baselines of previous works and achieves an accuracy of 91.98% and 96.66% on MVd and MVdA datasets, respectively. Finally, the proposed model was deployed through an Android application to make it accessible for testing and demonstrate its efficacy. © 2023, CC BY.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Harnessing Machine Learning based sequence prediction for Industry 4.0 applications

Harnessing Machine Learning based sequence prediction for In...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： G r Kanagachidambaresan C.A Arun M Subashini S Maheswaran Dept of CSE VelTech Rangarajan Dr.Sagunthala R&D institute of Science and Technology Chennai India Dept of ECE VelTech Rangarajan Dr.Sagunthala R&D institute of Science and Technology Chennai India JRF IoT Experts System Laboratory VelTec Rangarajan Dr.Sagunthala R&D institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper focuses on the utilization of gyro sensor data, particularly from the MPU6050 sensor, for sequence prediction and positional analysis for many Industry 4.0 application and asset monitoring. Leveraging machine learning algorithms such as random Forest, decision Trees etc., a robust classification model is developed to predict sequences of positions with notable accuracy. data collection involves capturing gyro sensor readings across distinct positions, laying the foundation for subsequent analysis. Integration with the Thingsboard platform facilitates data processing and analysis, showcasing the effectiveness of this integrated approach. Additionally, a user-friendly interface is designed for streamlined data input and visualization of prediction outcomes, enhancing accessibility and usability. The model demonstrates high accuracy in predicting sequences of positions using random Forest and decision Trees algorithms. The random Forest and decision tree approach provides higher accuracy when compared with the other algorithms. A IoT based hardware is designed in Kicad and tested to validate the ML based prediction for shop floor applications.

关键词： Analytical models Machine learning algorithms Accuracy Predictive models robot sensing systems data models Gyroscopes decision trees Usability random forests

来源：评论

学校读者我要写书评

暂无评论

The HEFT Algorithm Based on Node Merging

The HEFT Algorithm Based on Node Merging

引用

Control Engineering and Advanced Algorithms (IWCEAA), International Workshop on

作者： Ying Huang Yuanhong Lu Jie Zhang Jingyue Zhang Haiping Guo National Energy Power Grid Technology R&D Centre Guangzhou 510663 State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou 510663 Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou 510663 Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou 510663 Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou 510663 CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou 510663 Guangzhou China

ISBN: (数字)9798331528881

ISBN: (纸本)9798331528898

The task scheduling problem in power systems is complex, especially when communication costs exceed computation costs. This paper proposes an improved HEFT algorithm with a node merging mechanism, merging nodes through hierarchical traversal to assign high-communication-cost tasks to the same processor, reducing communication overhead. Experiments with 499-dimensional and 1614-dimensional matrices from the power system show that the improved algorithm reduces completion time by 5ms and 2ms, respectively, compared to standard HEFT.

关键词： Costs Control engineering Processor scheduling Conferences Merging Power systems Standards

来源：评论

学校读者我要写书评

暂无评论

Leveraging data Analytics to Enhance Smart Home Efficiency and Security 2

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

引用

2nd IEEE International Conference on Emerging research in Computational Science, ICErCS 2024

作者： Vivekanandan, Karthi Kaleeswari, B. Natchiammai, P.L. Anthonisamy, Arun Vijayabharathi, r. Alagarsamy, Manjunathan Builders Engineering College Department of Electrical and Electronics Engineering Tirupur India Department of Electronics and Communication Engineering Chennai India Mookambigai College of Engineering Department of Electronics and Communication Engineering Pudukkottai India Panimalar Engineering College Department of Computer Science and Business System Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices' energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security. © 2024 IEEE.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

FP4-Quantization: Lossless 4bit Quantization for Large Language Models

FP4-Quantization: Lossless 4bit Quantization for Large Langu...

引用

IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jie Wang Huanxi Liu dawei Feng Jie ding Bo ding College of Computer National University of Defense Technology Changsha China State Key Laboratory of Complex & Critical Software Environment Changsha China R&D Group Iflytek Hefei China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Large language models(LLMs) have demonstrated exceptional performance across a wide range of tasks. However, their extensive computational and storage requirements hinder their widespread deployment. To address this, low-bit quantization has emerged as a highly effective approach to reducing the inference cost of LLMs. Nevertheless, the existing repertoire of 4-bit quantization techniques is plagued by a substantial decline in model precision. In this paper, we introduce a novel 4-bit weight quantization method, FP4-Quantization, which leverages a 4-bit floating-point(FP4) representation that aligns better with the weight distribution characteristics of LLMs. Furthermore, it incorporates a Low-rank Quantization Error Correction(LrEC), involving progressive fine-tuning of low-rank parameters to rectify quantization errors, thereby enabling the achievement of precision-preserving 4-bit weight-only quantization. Our Experimental results on multiple zero-shot tasks demonstrate that FP4-Quantization achieves 4-bit weight quantization with an accuracy degradation of less than 0.5%.

关键词： degradation Cloud computing Quantization (signal) Accuracy Costs Computational modeling Large language models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：