检索结果-内蒙古大学图书馆

Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： Ravi A Veena P Deepak Raj M Kavin K K Naveen Kumar M Boopathi I Department of Electrical and Electronics Engineering K S R College of Engineering Namakkal

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The main aim of this proposed system is to design an efficient dynamic real-time task scheduling algorithm for autonomous vehicles in order to reduce latency, error rate and enhance the power efficiency. The Demerits of the existing model has static real time task scheduling will be solved by the high-performance ARM processor for dynamic real-time task scheduling. The FreeRTOS is used as an open-source software solution in providing better task scheduling to prioritize the sensor activities depending on the environmental factors and the user customization. The Proposed method employs an AI-based strategy for reduction in Power efficiency from 5mw to 3mw, Computational Latency reduced to 30 ms to 10 ms and improvements in resource utilization in a hard real-time system. The RTOS and STM32 based module is used for task scheduling for prioritizing data in autonomous vehicles for resource optimization, and minimal power consumption and thereby performing efficient and adaptive tasks in Self Driving Vehicles.

关键词： Power demand Scheduling algorithms Heuristic algorithms Dynamic scheduling Real-time systems Resource management Vehicle dynamics Autonomous vehicles optimization Open source software

来源：评论

学校读者我要写书评

暂无评论

high performance algorithms and software in nonlinear optimization

High Performance Algorithms and Software in Nonlinear Optimi...

引用

1999年

作者： Renato De Leone Panos M Pardalos Gerardo Toraldo

ISBN: (纸本)9780792354833

From the Publisher: This book contains a selection of papers presented at the conference on high performance software for nonlinear optimization (HPSNO97) which was held in Ischia, Italy, in June 1997. The book provides an overview of the nonlinear optimization field including algorithms, software evaluation, implementation issues, applications, and areas of research through authoritative papers by some of the most active and well-known researchers in the field. The papers of the Proceedings can be recommended to mathematicians, physicists, and engineers working in the field mentioned above, as well as recommended further reading within graduate studies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving software Quality Assurance Using a Cross-Context-Attentive Cosine Convolutional Model

Improving Software Quality Assurance Using a Cross-Context-A...

引用

Machine Learning and Autonomous Systems (ICMLAS), International conference on

作者： Deena Sunil Devika Dabke Surendra Kumar Saini Ravendra Ratan Singh Jandail MWP Maduranga Sharada Nandan Raw Department of Mathematics Indira Gandhi National Tribal University Amarkantak Madhya Pradesh Department of Mathematics Central University of Karnataka Gulbarga Karnataka Department of Mathematics Indira Gandhi National Tribal University Amarkantak MadhyaPradesh Department of Computer Science South Asian University New Delhi New Delhi Department of Electrical and Electronic Engineering University of Sri JayewardenepuraNugegoda Department of Mathematics National Institute of Technology Raipur Raipur Chhattisgarh

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The software quality determines system performance and reliability. As software systems are becoming increasingly complex, this is an important determinant. Detection of defects at the early stages of the development lifecycle improves software reliability and reduces costs. Traditional software Defect Prediction (SDP) models suffer from class imbalance, complexity of datasets, and inefficient use of contextual information, which affect the accuracy of their predictions. Therefore, the development of reliable software systems still requires a more robust and efficient defect prediction model. In this manuscript, Improving software Quality Assurance Using a Cross-Context-Attentive Cosine Convolutional Model (Sox-CNN-XA) is proposed. This study uses the NASA-MDP dataset containing historical records of software defects and various software metrics. Data pre-processing included data cleaning, normalization, and splitting, which was done in order to enhance the quality of the data and make it ready for training and validation purposes. A new framework for software Defect Prediction (SDP) has been proposed by using the integration of Cosine Convolutional Neural Networks (Cos-CNN) and Cross-Contextual Attention (XCA), which captures spatial and contextual features of data effectively. The hyperparameters in this model have been fine-tuned with Sardine optimization Algorithm (SOA) in order to get improved performances over diverse datasets. Experimental results show that the proposed framework achieves an accuracy of 99.9%, a true positive rate of 99.9%, and a precision of 99.8% while maintaining a false positive rate of 7.2%. The results clearly prove that the model is performing better than traditional methods. The conclusion shows that the framework can increase the reliability of defect prediction, thus enabling software practitioners to deliver high-quality and maintainable software within tight deadlines and budgets

关键词： Training Accuracy software algorithms software quality Predictive models software systems Prediction algorithms software reliability Convolutional neural networks optimization

来源：评论

学校读者我要写书评

暂无评论

A Semi-Supervised QPSO-TR PAPR Reduction Scheme for OTSM Systems

A Semi-Supervised QPSO-TR PAPR Reduction Scheme for OTSM Sys...

引用

Consumer Communications and Networking conference, CCNC IEEE

作者： Rafee Al Ahsan Abraham O. Fapojuwo Fadhel M. Ghannouchi Department of Electrical and Software Engineering (ESE) University of Calgary Alberta Canada

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper proposes a semi-supervised quantum particle swarm optimization-based tone reservation (SS-QPSO-TR) method with affinity propagation (AP) clustering and k-nearest neighbours (KNN) algorithms. The proposed SS-QPSO-TR method reduces the high peak-to-average power ratio (PAPR) in orthogonal time sequency multiplexing (OTSM) systems. The main finding is a significant 4.5 dB, 4.3 dB and 4 dB PAPR reduction in contrast to the original OTSM for QAM-64, 16 and 4, respectively while concurrently reducing transmission computational complexity by 16.7 times, compared to adapted tone reservation in both-domain (A-TR-BD), at the same bit error rate (BER) and error vector magnitude (EVM) performance. The proposed SS-QPSO-TR scheme enables efficient modulation of OTSM transmitters in high-mobility, real-time, wireless communication scenarios.

关键词： Wireless communication Multiplexing Transmitters Bit error rate Peak to average power ratio Nearest neighbor methods Real-time systems Vectors Computational complexity Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Development of Multi Objective Approach for Validation of PID Controller for Buck Converter

Development of Multi Objective Approach for Validation of PI...

引用

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International conference on

作者： Arun Kumar K Gopi Chand Vegineni C. Suresh Bhagath Chandra Chowdari Marella Sireesha Addanki Palak Chimwal Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Samayapuram Trichy Department of Financial Services Insights & Data Capgemini India Principal software developer System soft Technologies llc Information Technology India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was *** results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness.

关键词： Buck converters Regulators Machine learning algorithms Uncertainty Heuristic algorithms Velocity control Power system stability Real-time systems Voltage control optimization

来源：评论

学校读者我要写书评

暂无评论

LMSR: A Low-Jitter Multiple Slots Routing Algorithm in LEO Satellite Networks

LMSR: A Low-Jitter Multiple Slots Routing Algorithm in LEO S...

引用

IEEE conference on Wireless Communications and Networking

作者： Shiran Sun Ran Zhang Kai Liu Zekun Sun Qinqin Tang Tao Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China State Key Laboratory of Space Network and Communications Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In recent years, Low Earth Orbit (LEO) constellation based networks have attracted wide attention from both the academia and the industry. Broadband access and backhaul becomes a typical application for LEO satellite networks. However, the high-speed motion of satellites brings periodic fluctuation of delays, which is a great violation to the Quality of Service (QoS) provisioning. In this work, inspired by the success of software Defined Networking (SDN), and considering the dynamics of LEO satellite networks, we propose a low-jitter multiple slots routing in LEO satellite networks to provide low-jitter end-to-end delay path computing and control. The proposed multiple slots routing optimization mechanism takes account of the topology shift as well as the dynamic propagation delay across multiple topology snapshots, and thus achieves low-jitter performance. The performance improvement of the proposed mechanism is validated by the simulation, which promises the value of jitter under 30 ms.

关键词： Satellites Network topology software algorithms Low earth orbit satellites Quality of service Jitter Routing Delays Topology software defined networking

来源：评论

学校读者我要写书评

暂无评论

Structural optimization of Causal Driven Model Based on Bayesian Network in high-dimensional Data Classification

引用

Applied Mathematics and nonlinear Sciences 2025年第1期10卷

作者： Li, Kuo Wang, Aimin Wang, Limin Zhao, Yuetan Zhu, Xinyu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China

The Bayesian network (BN) model, as a big data graph model that integrates causal inference and probabilistic representation, has received widespread attention and research in both academia and industry. However, with the advent of the big data era, traditional BN structure learning algorithms have encountered unprecedented challenges in processing high-dimensional data, mainly manifested as a sharp increase in computational complexity and difficulty in achieving ideal accuracy requirements within an acceptable time range, which greatly limits their breadth and depth in practical applications. In response to this bottleneck problem, this article innovatively proposes a new approach that combines width learning theory with BN, referred to as Broad Bayesian Neural Network (Broad-BNN). This model effectively reduces the dimensionality of the original high-dimensional data by introducing a feature mapping layer and gradually expanding it, while achieving non-linear transformation of information and effective feature extraction. The experimental results show that the model proposed in this paper has achieved significant performance improvement in high-dimensional data classification problems, not only accelerating training speed but also significantly improving classification accuracy, providing a new perspective and solution for solving the difficulties of high-dimensional data processing. © 2025 Kuo Li et al., published by Sciendo.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

high performance algorithms and software for nonlinear optimization 1

引用

丛书名： Applied optimization

1000年

作者： Gianni Pillo Almerico Murli

ISBN: (数字)9781461302414

ISBN: (纸本)9781402075322;9781461379560

This volume contains the edited texts of the lectures presented at the Workshop on high performance algorithms and software for nonlinear optimization held in Erice, Sicily, at the "G. Stampacchia" School of Mathematics of the "E. Majorana" Centre for Scientific Culture, June 30 - July 8, 2001. In the first year of the new century, the aim of the Workshop was to assess the past and to discuss the future of nonlinear optimization, and to highlight recent achieve ments and promising research trends in this field. An emphasis was requested on algorithmic and high performance software developments and on new computational experiences, as well as on theoretical advances. We believe that such goal was basically achieved. The Workshop was attended by 71 people from 22 countries. Although not all topics were covered, the presentations gave indeed a wide overview of the field, from different and complementary stand points. Besides the lectures, several formal and informal discussions took place. We wish to express our appreciation for the active contribution of all the participants in the meeting. The 18 papers included in this volume represent a significant selection of the most recent developments in nonlinear programming theory and practice. They show that there is plenty of exciting ideas, implementation issues and new applications which produce a very fast evolution in the field.

关键词： optimization Numeric Computing Theory of Computation Operations Research/Decision Theory Operations Research, Management Science

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy optimization

An Adaptive Framework for Multi-View Clustering Leveraging C...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lijian Li Yuanpeng He Chi-Man Pun Department of Computer and Information Science University of Macau Macau China Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-view clustering (MVC) has emerged as a powerful technique for extracting valuable insights from data characterized by multiple perspectives or modalities. Despite significant advancements, existing MVC methods struggle with effectively quantifying the consistency and complementarity among views, and are particularly susceptible to the adverse effects of noisy views, known as the Noisy-View Drawback (NVD). To address these challenges, we propose CE-MVC, a novel framework that integrates an adaptive weighting algorithm with a parameter-decoupled deep model. Leveraging the concept of conditional entropy and normalized mutual information, CE-MVC quantitatively assesses and weights the informative contribution of each view, facilitating the construction of robust unified representations. The parameter-decoupled design enables independent processing of each view, effectively mitigating the influence of noise and enhancing overall clustering performance. Extensive experiments demonstrate that CE-MVC outperforms existing approaches, offering a more resilient and accurate solution for multi-view clustering tasks.

关键词： Adaptation models Noise Signal processing algorithms Entropy Acoustics Noise measurement Data mining Speech processing optimization Mutual information

来源：评论

学校读者我要写书评

暂无评论

A survey of HPC algorithms and frameworks for large-scale gradient-based nonlinear optimization

引用

JOURNAL OF SUPERCOMPUTING 2022年第16期78卷 17513-17542页

作者： Liu, Felix Fredriksson, Albin Markidis, Stefano KTH Royal Inst Technol CST Stockholm Sweden RaySearch Labs Stockholm Sweden

Large-scale numerical optimization problems arise from many fields and have applications in both industrial and academic contexts. Finding solutions to such optimization problems efficiently requires algorithms that are able to leverage the increasing parallelism available in modern computing hardware. In this paper, we review previous work on parallelizing algorithms for nonlinear optimization. To introduce the topic, the paper starts by giving an accessible introduction to nonlinear optimization and high-performance computing. This is followed by a survey of previous work on parallelization and utilization of high-performance computing hardware for nonlinear optimization algorithms. Finally, we present a number of optimization software libraries and how they are able to utilize parallel computing today. This study can serve as an introduction point for researchers interested in nonlinear optimization or high-performance computing, as well as provide ideas and inspiration for future work combining these topics.

关键词： nonlinear optimization HPC Interior-point method Parallel computing

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：