检索结果-内蒙古大学图书馆

21st International Conference on Networking, Sensing and control, ICNSC 2024

作者： Hu, Xinbang Liu, Guanjun Li, Xiaofeng Zhang, Kaiwen Guan, Jian Gu, Bin Tongji University Department of Computer Science Shanghai China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350365221

Estimating the Worst-Case Execution Time (WCET) of programs in an embedded multi-core environment is fundamental for schedulability analysis. In this paper, we propose a framework for calculating the WCET of programs originally designed for single-core systems when migrated to and executed in parallel on multi-core environments. First, we decompose single-core tasks and convert them into parallel programs using OpenMP. Our approach extends the task dependency graph to describe relevant information for parallel tasks (such as execution time estimation, core allocation, and inter-task dependencies). Finally, we present an iterative algorithm based on abstract interpretation. This algorithm takes the parallel task dependency graph as input and uses fixed-point analysis to determine the WCET of parallel programs. We conducted experiments on a physical machine to verify the feasibility of our solution. © 2024 IEEE.

关键词： Embedded software

来源：评论

学校读者我要写书评

暂无评论

Machine learning-driven optimization of plasma-catalytic dry reforming of methane

引用

Journal of Energy Chemistry 2024年第9期96卷 153-163页

作者： Yuxiang Cai Danhua Mei Yanzhen Chen Annemie Bogaerts Xin Tu Department of Electrical Engineering and Electronics University of LiverpoolLiverpool L693GJUK Research Group PLASMANT Department of ChemistryUniversity of AntwerpUniversiteitsplein 1BE-2610 Wilrijk-AntwerpBelgium College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816JiangsuChina

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental *** address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML *** ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM *** model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM *** found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/***,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM *** results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction *** hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

关键词： Plasma catalysis Machine learning Process optimization Dry reforming of methane Syngas production

来源：评论

学校读者我要写书评

暂无评论

Diabetic retinopathy detection using a well-calibrated uncertainty aware convolutional neural network

引用

Multimedia Tools and Applications 2024年 1-23页

作者： Verma, Preeti Elango, Sivasankar Singh, Kunwar Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli620015 India KIET Group of Institution Uttar Pradesh Delhi-NCR Ghaziabad201206 India

The early detection of diabetic retinopathy is crucial in preventing irreversible vision loss, making it a critical concern in healthcare. While deep learning models have shown advancements in categorization tasks, they can sometimes exhibit overconfidence in incorrect outcomes, posing significant risks in the medical field. To tackle this challenge, a novel model is proposed, which leverages a CNN (Convolutional neural network) architecture to predict softmax probabilities. The model incorporates train time augmentation to enhance performance and subsequently calibrates the projected probabilities using multiple calibration techniques, including Platt scaling, temperature scaling, isotonic regression, histogram binning, and Platt histogram. Furthermore, Test Time Data Augmentation (TTAUG) is employed to improve prediction accuracy by presenting the same data in multiple variations. The proposed technique is rigorously evaluated on benchmark datasets, namely Kaggle and Messidor. Notably, TTAUG yields remarkable results, achieving 98% accuracy on the Kaggle dataset, a significant improvement compared to the 93% achieved without augmentation. The calibration of probabilities further enhances the performance, as indicated by the reduction of Expected Calibration Error (ECE) from 0.52 to an impressive 0.019. The results underscore the significance of quantifying uncertainty in predictions, which enhances the interpretability of the proposed method as a diagnostic aid tool. By effectively addressing overconfidence and providing calibrated probabilities, the model proves its potential in improving both accuracy and reliability in diabetic retinopathy detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

引用

CAAI Transactions on Intelligence Technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

来源：评论

学校读者我要写书评

暂无评论

NR-Router+: Enhanced Non-Regular Electrode Routing With Optimal Pin Selection for Electrowetting-on-Dielectric Chips

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第9期43卷 2606-2619页

作者： Pan, Youlin Liu, Genggeng Huang, Xing Li, Zipeng Huang, Hsin-Chuan Liang, Chi-Chun Wang, Qining Kim, Chang-Jin Ho, Tsung-Yi Northwestern Polytechnical University School of Computer Science Xi'an710072 China Fuzhou University College of Computer and Data Science Fuzhou350116 China Apple Inc. Silicon Engineering Group CupertinoCA95014 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan University of California at Los Angeles Mechanical and Aerospace Engineering Department Los AngelesCA90095 United States The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are proposed, which allow more reliable droplet operations and facilitate the integration of optical sensors for many biochemical applications. Furthermore, nonregular electrode designs are utilized in EWOD chips, e.g., interdigitated electrodes for more reliable droplet manipulation, custom shaped electrodes for specific applications like concentric heating, etc. However, due to the technical challenges of fabricating multilayer interconnection on the glass substrate, e.g., unreliable process and high cost, both control electrodes and wires are fabricated with a single-layer configuration, which poses significant challenges to pin selection for nonregular electrodes. In this article, we propose a minimum-cost flow-based routing algorithm called NR-Router+ that features efficient and robust routing for single-layer EWOD chips with nonregular electrodes. To the best of our knowledge, this is the first work that overcomes the aforementioned challenges. We construct a minimum-cost flow algorithm to generate optimal routing paths followed by a light-weight model to handle flow capacity. A grid reduction strategy is proposed to reduce the computational overhead. Additionally, a flow collocation algorithm based on integer linear programming is presented to efficiently prevent wire overlapping. Experimental results show that NR-Router+ achieves 100% routability while minimizing wirelength with shorter run time. Moreover, NR-Router+ can generate mask files feasible for manufacturing via adjustments of design parameters, thus demonstrating its robustness and efficiency. © 1982-2012 IEEE.

关键词： Electrodes

来源：评论

学校读者我要写书评

暂无评论

Mitigating Cyberbullying in Social Media: A Deep Contextual Learning Approach for Severity Level Classification in Textual Data 5

Mitigating Cyberbullying in Social Media: A Deep Contextual ...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Agrawal, Prashant Kumar, Awanit Tripathi, Arun Kr. Sangam University Computer Science & Engineering Rajasthan India Sangam University Bhilwara Computer Science & Engineering Rajasthan India Kiet Group of Institution Department of Computer Applications Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350379945

This research introduces a novel approach that integrates Deep Contextual Learning (DCL), specifically the DCL-256-32 model with an embedding model to accurately classify offense levels within the textual data. The DCL-256-32 model employs a SoftMax function to assign probabilities to distinct severity classes, ranging from critical to negligible. The proposed model incorporates two endpoints: an embedding model for generating semantic representations of input text and a set of pre-trained DCL-256-32 models for predicting offense levels. By averaging these predictions and associating them with humanreadable labels, this study proposes a robust and scalable framework for real-time text analysis. The proposed model demonstrates high performance compared to existing methods, contributing to the advancement of Natural Language Processing (NLP) and classification. This research study offers a practical solution for enhancing digital safety and combating online harassment. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Simulating Hybrid Deep Learning Mechanism For Security Enhancement During Blockchain Based Wireless Sensor Network (WSN) Authentication 2

Simulating Hybrid Deep Learning Mechanism For Security Enhan...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Singh, Tejbir Vaid, Rohit Rana, Sanjeev Kumar Department of Computer Science & Engineering Maharishi MarkandeshwarEngineering College Mullana Haryana Ambala India Department of Computer Science & Engineering GNIOT Group of Institutions Uttar Pradesh India Mullana Haryana Ambala India

ISBN: (纸本)9798331541217

In WSN, the processing power, memory, and battery life of sensor nodes are limited. Due to their widespread usage in untrusted situations, WSNs are vulnerable to a variety of threats. The trustworthiness of the data it gathers is also called into doubt due to the challenges in getting a WSN. The authentication process of WSNs allows for verification of the authenticity of data and resources. Authentication examines the data's provenance and permits only approved modifications, protecting data in WSNs from manipulation. On the other hand, ID spoofing attacks and other vulnerabilities exist in existing authentication techniques. Blockchain is just one more exciting new development in the realm of cyber defense. A new blockchain-based authentication method for WSNs was developed in this study. Sensor nodes and the blockchain were integral parts of the study's system design, with users and a private blockchain playing vital roles. A comprehensive security review was conducted on the study's data. The classification of safe and insecure records via blockchain-based WSN has been accomplished using a deep learning model. Following optimization-based filtering, data stored on blockchain is categorized. After optimization, the performance of the attack classification system much improves;general accuracy increases to 96.95%, precision, recall, and F1-score rises to about 97%. Successful optimization is shown by these changes. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Malware Analysis Using Machine Learning and Deep Learning 3

Malware Analysis Using Machine Learning and Deep Learning

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Gupta, Riddhi Kumar, Puru Rani, Sonia Gupta, Aastha Kommuru, Anusha Ramkumar Dronacharya Group of Institutions Department of Computer Science &engineering Greater Noida India Galgotias University Department of Computer Science &engineering Greater Noida India

ISBN: (纸本)9798350342338

Malware is a severe danger to everyone from home users to huge corporations. As a result, it's a popular research topic. Malware fingerprints and activity patterns are analyzed both statically and dynamically to detect it. Analysis of malware on a daily basis by humans is a tedious task and still we are not ready for zero-day attack malware. Many machine learning methods are currently being utilized to find and analyze malware. The detection and classification accuracy varies based on algorithm used and the dataset taken to perform the testing. New and improved dataset will provide us with more accurate data and then new algorithm can be constructed based on it. The result will be more malware analysis data which can be used for malware detection by antivirus software. Given that Android smartphones are currently the target of the majority of attacks, the field of android malware analysis is expanding. Finding the best method to employ for a generic dataset is the research's ultimate objective, and the tested algorithm should surpass all prior tests on that dataset. © 2023 IEEE.

关键词： Android Malware Analysis Deep Learning Machine Learning Malware Analysis Static and Dynamic Analysis

来源：评论

学校读者我要写书评

暂无评论

BiFrost: A Blockchain-Based Decentralized Messaging Application

BiFrost: A Blockchain-Based Decentralized Messaging Applicat...

引用

International Conference on Data science and Network engineering, ICDSNE 2023

作者： Pandey, Himanshu Siraswal, Akhil Kaushik, Ekta Pandey, Dilkeshwar Kapoor, Sparsh Pahuja, Hunny Department of Computer Science and Engineering KIET Group of Institutions Delhi NCR Ghaziabad India Department of Electronics and Communication Engineering KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9789819967544

BiFrost is a revolutionary solution for secure online communication and data storage. It addresses security concerns such as eavesdropping, man-in-themiddle attacks, and censorship by offering decentralization, immutability, and data security through the use of blockchain technology. User-submitted data are added directly to the blockchain, creating a global copy in each node, and only authorized users can access the data using private keys. The system is fully decentralized, meaning there is no central authority, making it immune to censorship and government oppression. The technology used in BiFrost includes IPFS, smart contracts, Ethereum, INFURA, Solidity, and SPF. BiFrost provides a secure and decentralized solution for online communication and data storage, eliminating the need for a trusted intermediary and ensuring the privacy and security of data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

A review on analysis and prediction methodologies of crop yield pattern 5th

A review on analysis and prediction methodologies of crop yi...

引用

5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Neha Ahlawat, Anil Chaudhary, Himanshu Department of Computer Science Engineering KIET Group of Institutions Delhi NCR Ghaziabad India Department of Electronics and Communication Engineering KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9781032642987

Making supportive decisions for crop production, such as crop name recommendations and forecasts for crop production, requires machine learning implementation. Numerous machine learning classifiers and algorithms are employed in this. Climatic factors (temperature, rainfall, precipitation, sun radiation, and humidity), edaphic factors (pH, kind, and proportions of nitrogen, phosphorus, and potassium in the soil), and physiographic factors (altitude, steepness of slope, exposure to light, and wind) are some of the variables or factors that influence crop production. However, the most common variables are temperature, soil pH, precipitation, and humidity, and the most common algorithms are Examples of artificial neural networks and deep learning algorithms include Random Forest, Support Vector Machine, Linear Regression, Long Short-Term Memory, Convolution Neural Network, and Deep Neural Network. As part of this study, we reviewed the literature to ascertain the techniques and influencing factors that have been used in agricultural output prediction. © 2024 The Author(s).

关键词： Support vector machines

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：