检索结果-内蒙古大学图书馆

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Jingyu Tian, Jiachen Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Systems Engineering National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350319934

Hadoop is currently the most popular big data processing architecture, which provides a processing framework for managing and analyzing massive data. Hadoop Distributed File System (HDFS) is the core component for storing data in the Hadoop systems. It performs well when storing and managing large-sized files. However, it shows extremely bad performance when dealing with a large number of small files, which is reflected in the consumption of a large amount of memory space of the NameNode node and the low efficiency of accessing small files. Archive file is proposed to solve this problem. It combines small files into larger merged files for storage, and uses index files to record the relevant information of small files in the merged files. However, archive files (e.g., Hadoop Archive, MapFile) all require additional processing and multiple I/O operations to obtain index information before actually accessing the file content, which reduces the efficiency of file access. This paper proposes a novel distributed file storing mechanism called B+ Tree-Based Distributed File Storing mechanism (BTDFS), which combines a large number of small files into a merged file, records the index information of the small files in the merged file, and organizes the index information into a B+ tree for storage. Using BTDFS, while reducing the metadata consumption of the NameNode node, improves the access performance of a single small file with additional abilities, which provides convenience for analyzing a large number of small files. Finally, this paper designs corresponding experiments for the proposed BTDFS, and tests and analyzes the memory usage in N ameN ode and the access performance of small files. The experimental results show that the proposed BTDFS can effectively reduce the memory usage of the NameNode, and the efficiency of accessing small files is improved compared with state-of-the-art methods. © 2022 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Darly: Deep Reinforcement Learning for QoS-aware scheduling under resource heterogeneity Optimizing serverless video analytics

Darly: Deep Reinforcement Learning for QoS-aware scheduling ...

引用

IEEE International Conference on Cloud Computing, CLOUD

作者： Dimitrios Giagkos Achilleas Tzenetopoulos Dimosthenis Masouros Dimitrios Soudris Sotirios Xydis Microprocessors Laboratory and Digital Systems Lab (MicroLab) School of Electrical and Computer Engineering National Technical University of Athens

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams. Typically, video analytics are organized as a set of separate tasks, each of which has different resource requirements (e.g., computational- vs. memory-intensive tasks). The serverless computing paradigm forms a very promising approach for mapping such types of applications, as it enables fine-grained deployment and management in a per-function manner. However, modern serverless frameworks suffer from performance variability issues, due to i) the interference introduced due to co-location of third-party workloads with the serverless funcations and ii) the increasing hardware heterogeneity introduced in public clouds. To this end, this work introduces Darly, a QoS- and heterogeneity-aware Deep Reinforcement Learning-based Scheduler for serverless video analytics deployments. The proposed framework incorporates a DRL agent which exploits low-level performance counters to identify the levels of interference and the degree of heterogeneity in the underlying infrastructure and combines this information along with user-defined QoS requirements to dynamically optimize resource allocations by deciding the placement, migration, or horizontal scaling of serverless functions. Promising results are produced withing our experiments, which are accompanied with the intent to further build upon this groundwork.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparison of Machine Learning Algorithms For Predicting CO2Emissions in the maritime domain

Comparison of Machine Learning Algorithms For Predicting CO2...

引用

International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Vasilis Michalakopoulos Loukas Ilias Panagiotis Kapsalis Spiros Mouzakitis Dimitris Askounis Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece

The maritime industry is a significant source of global carbon dioxide (CO 2 ) emissions, and the accurate prediction of emissions in this domain is of paramount importance. In this paper, we conduct a comparative analysis of machine learning algorithms for predicting CO 2 emissions in the maritime domain. Using a unique dataset, comprising historical maritime data for different vessel voyages, including vessel type, voyage information and environmental factors, we employ decision trees, a number of regression, and artificial neural network algorithms. Performance evaluation is conducted using established metrics such as R2 (%), Mean Absolute Error, Normalized Root Mean Squared Error and Symmetric Mean Biased Error. Our findings reveal that the Extra Trees Regressor and Multi-layer Perceptron regressor algorithms outperform the other methods in terms of prediction accuracy demonstrating the least amount of error. Our research contributes to the existing literature by highlighting the potential of machine learning in predicting maritime CO 2 emissions and provides insights for further research, such as exploring alternative algorithms and incorporating real-time data Integration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mitigating Exposure Bias in Discriminator Guided Diffusion Models

arXiv

引用

arXiv 2023年

作者： Tsonis, Eleftherios Tzouveli, Paraskevi Voulodimos, Athanasios Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Diffusion Models have demonstrated remarkable performance in image generation. However, their demanding computational requirements for training have prompted ongoing efforts to enhance the quality of generated images through modifications in the sampling process. A recent approach, known as Discriminator Guidance, seeks to bridge the gap between the model score and the data score by incorporating an auxiliary term, derived from a discriminator network. We show that despite significantly improving sample quality, this technique has not resolved the persistent issue of Exposure Bias and we propose SEDM-G++, which incorporates a modified sampling approach, combining Discriminator Guidance and Epsilon Scaling. Our proposed approach outperforms the current state-of-the-art, by achieving an FID score of 1.73 on the unconditional CIFAR-10 dataset. © 2023, CC BY.

关键词： Discriminators

来源：评论

学校读者我要写书评

暂无评论

Minimizing Maximum Tardiness in a Distributed Flow Shop Manufacturing Problem under No-Waiting and Sequence Dependent Setup Time Constraints 14

Minimizing Maximum Tardiness in a Distributed Flow Shop Manu...

引用

14th International Conference on Intelligent systems: Theories and Applications, SITA 2023

作者： Sayah, Achraf Aqil, Said Lahby, Mohamed Hassan Ii University of Casablanca Higher Normal School Laboratory of Applied Mathematics and Computer Sciences Morocco Hassan Ii University of Casablanca National Higher School of Arts and Crafts Laboratory Artificial Intelligence and Complex Systems Engineering Morocco

ISBN: (纸本)9798350308211

The distributed no-wait flow-shop manufacturing problem with sequence dependent setup time (DNWFSMP-SDST) is a challenging optimization problem that arises in various industries, including manufacturing and logistics. This problem involves scheduling a set of jobs on multiple machines in a distributed environment, where each job has a specific processing time and requires setup time between different machine transitions. The objective is to minimize the maximum tardiness of all jobs, which represents the maximum delay in completing any job beyond its desired due date. This study addresses the DNWFSMP-SDST optimization problem by employing two algorithms: Nawaz-Enscore-Ham (NEH) and Greedy Randomized Adaptive Search Procedure (GRASP). The NEH is a constructive heuristic that constructs an initial solution by iteratively inserting jobs into a partially ordered sequence. In contrast, GRASP combines a greedy randomized construction phase with a local search procedure to iteratively enhance the initial solution. The simulation study compares NEH and GRASP algorithms for solving DNWFSMP-SDST using bench-marking instance. The results show the pros and cons of each algorithm, helping practitioners make better decisions. Where the NEH algorithm outperform for GRASP technique solving the kind of problem. Overall, this research has enabled us to improve our methods for solving scheduling problems in distributed environments. © 2023 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

A Next Generation Library of AI-Based Data-Driven Services for the Built Environment

A Next Generation Library of AI-Based Data-Driven Services f...

引用

International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Elissaios Sarmas Stathis Stamatopoulos Panagiotis Kapsalis Konstantinos Touloumis Vangelis Marinakis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

Nowadays, buildings account for almost 40% of the overall energy consumption. Thus, it is essential to involve a broad range of stakeholders throughout the building lifecycle, with the development of innovative, cross-cutting energy services, which exploit the ever-increasing volume of available data. In this paper we present a novel library of AI-based data-driven services for the built environment with the aim of addressing decarbonization challenges and of facilitating datadriven decision making. The issue of handling energy-related data in an efficient way is tackled, incorporating them in user-friendly, micro-services oriented technological components. The architecture of this AI-base library is thoroughly presented covering learning, optimization and simulation tasks. showcase our approach to energy resource management.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Artificial sensory neurons and their applications

引用

Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

An efficient memory data organization strategy for application-characteristic graph processing

引用

Frontiers of computer Science 2022年第1期16卷 191-193页

作者： Peng FANG Fang WANG Zhan SHI Dan FENG Qianxu YI Xianghao XU Yongxuan ZHANG Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage SystemEngineering Research Center of data storage systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor... 详细信息

关键词： application processing pattern

来源：评论

学校读者我要写书评

暂无评论

Large-scale coherent Ising machine based on optoelectronic parametric oscillator

引用

Light(Science & Applications) 2022年第12期11卷 2972-2981页

作者： Qizhuang Cen Hao Ding Tengfei Hao Shanhong Guan Zhiqiang Qin Jiaming Lyu Wei Li Ninghua Zhu Kun Xu Yitang Dai Ming Li State Key Laboratory on Integrated Optoelectronics Institute of SemiconductorsChinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijingChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghaiChina Peng Cheng Laboratory ShenzhenChina

Ising machines based on analog systems have the potential to accelerate the solution of ubiquitous combinatorial optimization *** some artificial spins to support large-scale Ising machines have been reported,e.g.,superconducting qubits in quantum annealers and short optical pulses in coherent Ising machines,the spin stability is fragile due to the ultra-low equivalent temperature or optical phase *** this paper,we propose to use short microwave pulses generated from an optoelectronic parametric oscillator as the spins to implement a large-scale Ising machine with high *** proposed machine supports 25,600 spins and can operate continuously and stably for ***,the proposed Ising machine is highly compatible with high-speed electronic devices for programmability,paving a low-cost,accurate,and easy-to-implement way toward solving real-world optimization problems.

关键词： stability oscillator coherent

来源：评论

学校读者我要写书评

暂无评论

Exploring the Integration of Educational Robotics and the Internet of Things in Learning Environments

Exploring the Integration of Educational Robotics and the In...

引用

International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Elena Kakoulli Salomi Evripidou Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This study explores the integration of Educational Robotics (ER) and the Internet of Things (IoT) in learning environments, highlighting their collective impact on educational practices. It assesses ER and IoT’s application, challenges, and opportunities, offering insights into their role in enhancing pedagogy and learning outcomes. Specifically, our exploration uncovers the transformative potential of ER and IoT in fostering critical thinking, problem-solving skills, and digital literacy among students and reveals the pivotal role these technologies play in preparing learners for the demands of the digital age and Industry 4.0, while also highlighting the need for strategic implementation and teacher support. Our findings elucidate the potential of ER and IoT to innovate teaching strategies and curriculum design, serving as a guide for educational stakeholders, such as curriculum developers, educators, researchers, and policymakers, in leveraging these technologies to revolutionize instructional practices.

关键词： Training Information age Fourth Industrial Revolution Internet of Things Stakeholders Problem-solving Distributed 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：