检索结果-内蒙古大学图书馆

High Speed Machining for Linear Paths Blended with G3 Continuous Pythagorean-Hodograph Curves

Journal of Systems science & Complexity 2022年第4期35卷 1586-1607页

作者： ZHAO Kai LI Shurong College of Control Science and Engineering China University of Petroleum(East China)Qingdao266580China College of Computer Science and Information Engineering Anyang Institute of TechnologyAnyang455000China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

Previously,many studies have illustrated corner blend problem with different parameter *** a few of them take a Pythagorean-hodograph(PH)curve as the transition arc,let alone corresponding real-time interpolation *** this paper,an integrated corner-transition mixing-interpolation-based scheme(ICMS)is proposed,considering transition error and machine tool ***,the ICMS smooths the sharp corners in a linear path through blending the linear path with G3 continuous PH transition *** obtain optimal PH transition curves globally,the problem of corner smoothing is formulated as an optimization problem with *** order to improve optimization efficiency,the transition error constraint is deduced analytically,so is the curvature extreme of each transition *** being blended with PH transition curves,a linear path has become a blend ***,the ICMS adopts a novel mixed interpolator to process this kind of blend curves by considering machine tool *** mixed interpolator can not only implement jerk-limited feedrate scheduling with critical points detection,but also realize self-switching of two interpolation ***,two patterns are machined with a carving platform based on *** l results show the effectiveness of ICMS.

关键词： Bézier control polygon corner transition jerk-limited scheduling mixed interpolation pythagorean-hodograph curve

来源：评论

学校读者我要写书评

暂无评论

Hyper Triglycerides Prognosis Using Machine Learning and Data science

Hyper Triglycerides Prognosis Using Machine Learning and Dat...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sunitha, T. Sankaradass, Veeramalai Anna University P B College of Engineering Dept. Computer Science and Engineering Chennai India Chennai Institute of Technology Dept. Computer Science and Engineering Tamil Nadu Chennai600069 India

ISBN: (纸本)9798350348910

This study investigates the prognostic potential of machine learning and data science in evaluating hypertriglyceridemia, a condition linked to various health risks. Triglycerides, a substantial component of body fat derived from dietary intake, are intricately associated with these risks. The research explores the use of data science methodologies, especially machine learning algorithms, for precise prognoses. The integration of artificial neural networks and diverse data sources contributes to advancing predictive models, aiming to enhance understanding and improve early detection and preventive interventions for hypertriglyceridemia. This study also emphasizes the role of triglycerides in the body, originating from dietary sources such as butter and oils. Excess calories, not immediately utilized, transform into stored triglycerides, released by the body for energy distribution through VLDL cholesterol particles. Elevated triglyceride levels correlate with increased risks of heart disease, coronary artery disease (CAD), stroke, and chronic conditions affecting the liver and kidneys. The rapid progress in data science, driven by diverse data sources, positions the field among the fastest-evolving. Integration with machine learning technologies significantly improves healthcare solutions, particularly in disease prediction through artificial neural networks. These advancements have a substantial impact on addressing heart disease (4.4 million annual deaths according to the WHF dossier) and cholesterol-related fatalities (estimated at nearly 2.6 million by the WHO). © 2023 IEEE.

关键词： Cholesterol

来源：评论

学校读者我要写书评

暂无评论

Light field salient object detection:A review and benchmark

引用

Computational Visual Media 2022年第4期8卷 509-534页

作者： Keren Fu Yao Jiang Ge-Peng Ji Tao Zhou Qijun Zhao Deng-Ping Fan College of Computer Science Sichuan Universityand National Key Laboratory of Fundamental Science on Synthetic VisionSichuan UniversityChengdu 610065China School of Computer Science Wuhan UniversityWuhan 430072China PCA Lab School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Computer Vision Lab ETH ZürichZürichSwitzerland

Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past *** light fields record comprehensive information of natural scenes that benefit SOD in a number of ways,using light field inputs to improve saliency detection over conventional RGB inputs is an emerging *** paper provides the first comprehensive review and a benchmark for light field SOD,which has long been lacking in the saliency ***,we introduce light fields,including theory and data forms,and then review existing studies on light field SOD,covering ten traditional models,seven deep learning-based models,a comparative study,and a brief *** datasets for light field SOD are also ***,we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets,providing insightful discussions and analyses,including a comparison between light field SOD and RGB-D SOD *** to the inconsistency of current datasets,we further generate complete data and supplement focal stacks,depth maps,and multi-view images for them,making them consistent and *** supplemental data make a universal benchmark ***,light field SOD is a specialised problem,because of its diverse data representations and high dependency on acquisition hardware,so it differs greatly from other saliency detection *** provide nine observations on challenges and future directions,and outline several open *** the materials including models,datasets,benchmarking results,and supplemented light field datasets are publicly available at https://***/kerenfu/LFSOD-Survey.

关键词： light field salient object detection(SOD) deep learning benchmarking

来源：评论

学校读者我要写书评

暂无评论

Group Teaching Optimization Algorithm with Machine Learning Model for Big Data Analytics

Group Teaching Optimization Algorithm with Machine Learning ...

引用

2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Prabaharan, G. Sesha Vidhya, S. Venkatesh, K. Mary Sundararajan, Suma Christal Shobana, G. Srimathi, S. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Veltech University Avadi Chennai India Rmk College of Engineering and Technology Department of Ece India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India College of Science and Humanities Srm Institute of Science and Technology Department of Computer Applications SRM Nagar Kattankulathur603 203 India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350313987

Big Data An analytics (BDA) plays a significant role in the process of decision-making over several domains. Effective evaluation and processing of enormous data amounts need influential techniques and Machine Learning (ML) methods. BDA by implementing ML and Feature Selection (FS) is an influential model in extracting valued patterns and vision from huge and convolutional datasets. The process encompasses implementing modern ML methods and models for interpreting, processing, and analyzing data on a scale that cannot be handled effectively by conventional models. This research study proposes a design of Group Teaching Optimization Algorithm with Machine Learning model for Big Data Analytics (GTOA-MLBDA) technique. The GTOA-MLBDA technique classifies the big data and it can be managed by Map Reduce tool. The GTOA-MLBDA technique uses GTOA to determine the important features, reduce dimensionality and enhance classifier results. In addition, the classification of big data takes place using functional link neural network (FLNN) model. Finally, the parameter tuning of the FLNN model takes place using Adam optimizer. Experimental analysis has been carried out by using big data datasets and the results highlight that the combination of the GTOA with the FLNN classifier of the GTOA-MLBDA technique resulted in improved results over classical approaches. © 2023 IEEE.

关键词： Big Data Classification Data Analytics Feature Selection Machine Learning Map Reduce

来源：评论

学校读者我要写书评

暂无评论

Adaptive Differential Privacy Budget Allocation Algorithm Based on Random Forest 16th

Adaptive Differential Privacy Budget Allocation Algorithm Ba...

引用

16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Wang, Chong-Yang Chen, Si-Yang Li, Xin-Cheng College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9789811912559

In the context of big data, privacy protection in the process of data mining has become a hot issue in the field of security. The commonly used privacy protection method is to add differential privacy in the process of data mining, but the unreasonable privacy budget allocation leads to unsatisfactory classification results. To solve this problem, an adaptive differential privacy budget allocation algorithm RFDPP-weight based on random forest is proposed. Construct multiple decision trees with balanced features, calculate the Balance Error Rate (BER) of the data outside the bag in order to calculate the feature weight and then reconstruct the feature set. And the random forest with higher classification performance was constructed based on the new feature set. The decision tree weight was calculated according to the feature weight, and the privacy protection budget was adaptively allocated according to the decision tree weight. In the final prediction stage, the classification result with the largest weight of the corresponding decision tree is taken as the result of the random forest algorithm, so as to further improve the classification accuracy of the random forest algorithm. The experimental results show that the F1 score of our algorithm reaches 0.977 and 0.893 with a small number of decision trees in the Mushroom and Adult data sets, which proves that the algorithm can reasonably allocate the privacy budget and further improve the availability of data while protecting data privacy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Reconstructing the image stitching pipeline: integrating fusion and rectangling into a unified inpainting model 24

Reconstructing the image stitching pipeline: integrating fus...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ziqi Xie Weidong Zhao Xianhui Liu Jian Zhao Ning Jia College of Computer Science and Technology Tongji University College of Computer Science and Technology and College of Electronics and Information Engineering Tongji University

ISBN: (纸本)9798331314385

Deep learning-based image stitching pipelines are typically divided into three cascading stages: registration, fusion, and rectangling. Each stage requires its own network training and is tightly coupled to the others, leading to error propagation and posing significant challenges to parameter tuning and system stability. This paper proposes the Simple and Robust Stitcher (SRStitcher), which revolutionizes the image stitching pipeline by simplifying the fusion and rectangling stages into a unified inpainting model, requiring no model training or fine-tuning. We reformulate the problem definitions of the fusion and rectangling stages and demonstrate that they can be effectively integrated into an inpainting task. Furthermore, we design the weighted masks to guide the reverse process in a pre-trained large-scale diffusion model, implementing this integrated inpainting task in a single inference. Through extensive experimentation, we verify the interpretability and generalization capabilities of this unified model, demonstrating that SRStitcher outperforms state-of-the-art methods in both performance and stability. Code: https ://***/yayoyo66/SRStitcher

关键词：

来源：评论

学校读者我要写书评

暂无评论

Application of Data Analysis and Visualization Tools for U.S. Renewable Solar Energy Generation, Its Sustainability Benefits, and Teaching In engineering Curriculum

Application of Data Analysis and Visualization Tools for U.S...

引用

2024 ASEE Annual Conference and Exposition

作者： Radhakrishnan, Ben D. Altamirano, Nelson Jaurez, James J. Department of Engineering Data and Computer Sciences College of Business Engineering and Technology National University San DiegoCA United States Department of Finance Economics Marketing and Accounting College of Business Engineering and Technology National University San DiegoCA United States Department of Computer Science & Cyber Security College of Business Engineering and Technology National University San DiegoCA United States

The advancement of Data Analysis technologies with visualization has gained significant ground in the industries and they are also gaining ground in higher education curriculum. This research will focus on the application of these techniques to the energy industry - in particular, solar renewable energy generation in all the States in the United States (US). One strong opposing action against the progression of climate change is the use of renewable energy. The objective of the research is to develop a case study on renewable solar energy and its impact on abating or preventing CO2 emissions to help reduce the severe impacts of climate change in the United States and demonstrate how it supports the 3Es of sustainability at the same time. This research paper will specifically explore the past production of solar energy in all the states in the US, and with the use of data analysis tools will predict the production to the year 2030. The reduction of CO2 emissions with the use of renewable solar energy is in direct support of the three elements of sustainability, namely the 3Es: Environment, Economics, and Equity (or social justice). This research will quantify the past benefits already realized in all these three areas for solar energy, and project them up to 2030. Cluster analysis technique will be applied to solar generation across all US States to identify group(s) at distinct levels of production. This can help States to follow the leading State(s) policy and process to increase their solar generation and thus help manage climate change. Solar energy generation challenges and recycling issues of solar equipment will also be addressed. This case study approach will fill a gap that currently exists in engineering education when it comes to exploring renewable energy and its sustainability benefits with modern data analysis tools. This research will use publicly available data sources (e.g., NREL, EIA), ubiquitous Excel, and open-source data analytics and prediction

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

An Overview on Methods for Random Sampling of Social Media Interaction Data 1

An Overview on Methods for Random Sampling of Social Media I...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Paturkar, Yogini Prasanna Bhagat, Amol P. Khodke, Priti A. Prof Ram Meghe College of Engineering & Management Research Centre Department of Computer Science and Engineering Badnera Amravati India Prof Ram Meghe College of Engineering & Management Department of Information Technology Badnera Amravati India

ISBN: (纸本)9798350367720

The explosive growth of social media platforms has generated vast amounts of interaction data, presenting both opportunities and challenges for researchers and analysts. Effective random sampling of this data is essential for manageable analysis, ensuring representativeness and reducing computational burdens. This paper reviews various methods for random sampling of social media interaction data, focusing on their applications, advantages, and limitations. Selecting an appropriate sampling method is crucial for the integrity and validity of social media research. Each method has distinct advantages and trade-offs, necessitating careful consideration of the research objectives, data characteristics, and available resources. Future research should explore hybrid approaches and the integration of machine learning techniques to enhance the efficiency and accuracy of sampling processes in the evolving landscape of social media data analysis. © 2024 IEEE.

关键词： Data integrity

来源：评论

学校读者我要写书评

暂无评论

An Effective Approach Using Machine Learning for Fall Prediction 9

An Effective Approach Using Machine Learning for Fall Predic...

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Vinora, A. Maheswari, P. Uma Deborah, R.Nancy Ajitha, E. Srinivasan, A. Sivakarthi, G. Velammal College of Engineering and Technology Department of Information Technology Madurai India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314090

The ability to fall has now increased due to the usage of phones as well as a variety of distractions on the streets that has led to a major public concern. Fall is an activity that can be caused to carelessness or incoordination. It is also attributed to monitoring senior citizens who tend to have a higher fall rate due to age-related illness, diseases, or poor visibility. It can be predicted for also soldiers for an unknown battle field based on their momentum. A dataset of 40000 rows have been obtained from Kaggle which was procured using a three-axis gyroscope that was deployed on a smartphone to collect data on falls during activities such as walking, jogging, standing, and sitting. A set of Machine Learning (ML) algorithms such as Scaled Conjugate Gradient, Bayesian Regularization, Levenberg-Marquardt, BFGS Quasi-Newton Backpropagation, Resilient Backpropagation, Conjugate Gradient Backpropagation, Conjugate Gradient Backpropagation With Polak-Ribiére Updates, One-Step Secant Backpropagation, Gradient Descent With Momentum And Adaptive Learning Rate Backpropagation and Gradient Descent With Momentum Backpropagation were deployed on the built model to check for fall prediction. Among them, Bayesian Regularization provided high accuracy rates considering cross entropy as a performance metric. This technology helps to prevent the fall of a person that can lead to an uneventful situation. © 2023 IEEE.

关键词： Gyroscopes

来源：评论

学校读者我要写书评

暂无评论

Strengthening Authentication: The Role of Multi-Level Hashing in OSPA Protocol 5

Strengthening Authentication: The Role of Multi-Level Hashin...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

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：