检索结果-内蒙古大学图书馆

12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Wang, Wenxiang Li, Kangshun Wang, Hui College of Mathematics and Informatics South China Agricultural University Guangdong Guangzhou510642 China School of Computer and Information Dongguan City University Guangdong Dongguan523808 China School of Software Engineering Shenzhen Institute of Information Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9789811941085

The slicing technology can meet the diversified and personalized service needs of 5G networks, schedule virtual networks dynamically and improve the operation efficiency of the network. The dynamic resource allocation algorithm among slices is the key for the application of slicing technology, which has also become a research hotspot in recent years. However, the current dynamic resource allocation algorithm of 5G network slicing has many problems such as insufficient fairness, difficulty in ensuring user satisfaction and low resource utilization rate, etc. Under the circumstances of a wide variety of 5G services, it is difficult to meet the needs of user satisfaction under differentiated service conditions only from the perspective of the network. From the perspective of Quality of Experience (QoE), a QoE evaluation system for 5G network is constructed, and a QoE-based dynamic resource allocation algorithm of 5G network slicing on the basis of this system is built in this research. The results of the simulation experiments indicate that the algorithm has significant effect in improving QoE of 5G communities, and it also has good performance in improving the resource utilization rate in the communities. © 2022, Springer Nature Singapore Pte Ltd.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Differential Evolution Algorithm for Medical Image Registration 12th

Differential Evolution Algorithm for Medical Image Registrat...

引用

12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Li, Kangshun Wang, Wenxiang Wang, Hui School of Computer and Information Dongguan City University Guangdong Dongguan523808 China College of Mathematics and Informatics South China Agricultural University Guangdong Guangzhou510642 China School of Software Engineering Shenzhen Institute of Information Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9789811941085

Image registration plays an important role in medical image fusion and surgical navigation. Iterative nearest point algorithm (INPA) is a high-precision image registration algorithm, but it also has the problems of huge computation cost and low efficiency. Therefore, we use the differential evolution algorithm to optimize the search process of the iterative nearest point algorithm, so that to improve its registration efficiency. Then a mutation operator selection algorithm based on fitness roughness and a slack selection strategy for differential evolution algorithm were proposed to meet the requirements of high precision and low delay of medical image registration. The registration experiments of chest CT images show that the proposed differential evolution algorithm can not only accelerate the speed of iterative nearest point algorithm, but also improve its registration accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

Attention-aligned mean-teacher learning for unsupervised domain adaptive person re-ID

引用

Science China Technological Sciences 2025年

作者： You LYU Zhen ZHANG Guoliang KANG Wei WEI Institute of Artificial Intelligence Beihang University Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education Beihang University State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University School of Automation Science and Electrical Engineering Beihang University AVICAS Generic Technology Co. Ltd Zhongguancun Laboratory School of Mathematics Sciences Beihang University

In recent years, artificial intelligence has fueled the development of numerous applications [1, 2]. Person re-identification (re-ID) is a typical artificial intelligence system designed to automatically retrieve imag...

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Delivery Date Prediction For Child Birth

Machine Learning Based Delivery Date Prediction For Child Bi...

引用

Information and Communication technology for Development for Africa (ICT4DA), International Conference on

作者： Tsedale Mulugeta Micheal Melese Yibeltal Assefa Birku Gedif Information Science Wollega Institute of Technology Wollega University Information Science College Natural and Computation Science Addis Abeba University Software Engineering Kombolcha Institute of Technology Wollo University Information Science College of Informatics University Of Gonder

Health systems bear a crucial responsibility for the well-being of individuals across their entire lifespan. Accurate prediction of the delivery date for childbirth is of paramount importance for both the expectant mother and her infant. It plays a vital role in ensuring the safety and health of both parties. A miscalculated decision in this regard not only increases the risks for the mother's life but also poses potential harm to the newborn. To address this issue and mitigate associated risks, we have developed a prediction model based on machine learning techniques. In this research study, we utilized the Ethiopian Demographic and Health Survey (EDHS) dataset to predict the delivery date for childbirth and create our model. We follow experimental research design methodology as a result we employed an experiment regression-based machine learning algorithms, including Linear Regression, Multilayer Perceptron, and Random Forest, to develop the predictive model. Once the model was constructed, we evaluated its performance using well established evaluation metrics, such as mean absolute error, mean squared error, and ${\boldsymbol R}^{2}$ score. Upon careful examination of the evaluation results, we determined that the ${\boldsymbol R}^{2}$ score served as a reliable metric to assess the performance of our models. The obtained ${\boldsymbol R}^{2}$ scores were 0.35 for Linear Regression, -25.1 for Multilayer Perceptron, and 0.67 for Random Forest. Based on these scores, it became evident that the Random Forest algorithm exhibited superior performance compared to the other algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparison of ResNet-50 and Vision Transformer Models for Trash Classification 3

Comparison of ResNet-50 and Vision Transformer Models for Tr...

引用

3rd International Conference on Artificial Intelligence and Computer engineering, ICAICE 2022

作者： Liu, Jiongli Sun, Jiayou Zhou, Xuan College of Materials science and Engineering Sun Yat-sen University 132 Outer Ring Road East Guangdong Province Guangzhou510006 China College of Mathematics and Informatics South China Agricultural University 483 Wushan Road Guangdong Province Guangzhou510642 China College of Computer and Software Engineering Huaiyin Institute of Technology 1 Knights East Road Jiangsu Province Huai'an City223003 China

ISBN: (纸本)9781510663473

Paper, glass, fruit, scrap metal equipment, and other materials have become domestic garbage as people's living standards have dramatically improved. Plastic trash, including bags, and disposable lunch boxes, has become a global environmental threat. Carrying out waste separation and encouraging waste reduction at the source is a meaningful way to realize the quantification, harmlessness, and resourcefulness of domestic waste. Pictures contain lots of information data and many methods for successfully extracting and examining that information data. Consequently, one of the primary research projects in the field of pictures is the picture classification problem. Traditional image classification techniques can no longer process and extract information from vast amounts of image data quickly. Throughout the experiments, the accuracy of ResNet-50 is lower than ViT, most likely due to its less extensive set of parameters. And the more the parameters of the model, the higher the precision of training. Additionally, the experiment examined the performance of the ResNet-50 model with and without pretraining, and the results showed that the performance of the model without pretraining was noticeably inferior to the model with pretraining. It is better to have a model which has numerous parameters. © 2023 SPIE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Long-term and short-term plasticity independently mimicked in highly reliable Ru-doped Ge_(2)Sb_(2)Te_(5) electronic synapses

引用

InfoMat 2024年第8期6卷 85-96页

作者： Qiang Wang Yachuan Wang Yankun Wang Luyue Jiang Jinyan Zhao Zhitang Song Jinshun Bi Libo Zhao Zhuangde Jiang Jutta Schwarzkopf Shengli Wu Bin Zhang Wei Ren Sannian Song Gang Niu State Key Laboratory for Manufacturing Systems Engineering Electronic Materials Research LaboratoryKey Laboratory of the Ministry of Education&International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi'an Jiaotong UniversityXi'anthe People's Republic of China School of Integrated Circuits Peking UniversityBeijingthe People's Republic of China Beijing Microelectronics Technology Institute Beijingthe People's Republic of China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual information and Applicationsand School of SoftwareXi'an Jiaotong UniversityXi'anthe People's Republic of China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaithe People's Republic of China Key Laboratory of Microelectronics Device and Integrated Technology The Institute of Microelectronics of Chinese Academy of SciencesBeijingthe People's Republic of China University of Chinese Academy of Sciences Beijingthe People's Republic of China The State Key Laboratory for Manufacturing Systems Engineering&The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology Xi'an Jiaotong UniversityXi'anthe People's Republic of China Leibniz-Institut für Kristallzüchtung Max-Born-Straße 2BerlinGermany Key Laboratory of Physical Electronics and Devices Ministry of EducationSchool of Electronic Science and EngineeringXi'an Jiaotong UniversityXi'anthe People's Republic of China The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology Xi'an Jiaotong UniversityXi'anthe People's Republic of China

In order to fulfill the complex cognitive behaviors in neuromorphic systems with reduced peripheral circuits,the reliable electronic synapses mimicked by single device that achieves diverse long-term and short-term plasticity are *** change random access memory(PCRAM)is of great potential for artificial synapses,which faces,however,difficulty to realize short-term plasticity due to the long-lasting resistance *** work reports the ruthenium-doped Ge_(2)Sb_(2)Te_(5)(RuGST)based PCRAM,demonstrating a series of synaptic behaviors of short-term potentiation,pair-pulse facilitation,longterm depression,and short-term plasticity in the same single *** optimized RuGST electronic synapse with the high transformation temperature of hexagonal phase>380C,the outstanding endurance>108 cycles,the low resistance drift factor of 0.092,as well as the extremely high linearity with correlation coefficients of 0.999 and 0.976 in parts of potentiation and *** investigations also go insight to mechanisms of Ru doping according to thorough microstructure characterization,revealing that Ru dopant is able to enter GST lattices thus changing and stabilizing atomic arrangement of *** leads to the short-term plasticity realized by RuGST ***,the proposed RuGST electronic synapses performs a high accuracy of94.1%in a task of image recognition of CIFAR-100 database using ResNet *** work promotes the development of PCRAM platforms for large-scale neuromorphic systems.

关键词： electronic synapses neuromorphic computing PCRAM Ru-doped Ge_(2)Sb_(2)Te_(5)

来源：评论

学校读者我要写书评

暂无评论

Initial Assessment of the Influence of Robustness on the Weighted Tardiness for a Scheduling Problem with High Demand Volatility Based on a Simulation Model

Initial Assessment of the Influence of Robustness on the Wei...

引用

2021 Winter Simulation Conference, WSC 2021

作者： Letonja, Ziga Furian, Nikolaus Pan, Johannes Vossner, Siegfried Reuter-Oppermann, Melanie Institute of Engineering and Business Informatics Graz University of Technology Graz8010 Austria Technical University of Darmstadt Information Systems-Software Digital Business Group Darmstadt64289 Germany

ISBN: (纸本)9781665433112

Nowadays, customer behavior changes rapidly, resulting in high demand volatility throughout immensely globalized supply chains, especially from key-account customers. Hence, requiring not only excellent products produced in the shortest time but also abiding to defined due dates. In this paper, we propose a multi-objective large neighborhood search (MO-LNS) algorithm with a frozen period focusing solely on minimizing the total weighted tardiness of a two-stage production with random rush orders. The total weighted tardiness is set as the primary objective function and the robustness as the secondary one. The algorithm is implemented in a discrete event simulation (DES) model to observe the effects of varying objective function weights at different levels of order dynamism. The results suggest that there is not a single dominant weight pair, but rather a range, which dominates others. Thus, using a pair from the dominant range increases the likelihood of reducing the tardiness over a period. © 2021 IEEE.

关键词： Supply chains

来源：评论

学校读者我要写书评

暂无评论

Error-Aware Generative Reasoning for Zero-Shot Visual Grounding

引用

IEEE Transactions on Multimedia 2025年

作者： Bu, Yuqi Wu, Xin Cai, Yi Liu, Qiong Wang, Tao Huang, Qingbao South China University of Technology School of Software Engineering Guangzhou510006 China Guangzhou510006 China King's College London Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience LondonWC2R 2LS United Kingdom Guangxi University School of Electrical Engineering Institute of Artificial Intelligence Nanning530005 China

Zero-shot visual grounding is the task of identifying and localizing an object in an image based on a referring expression without task-specific training. Existing methods employ heuristic rules to step-by-step perform visual perception for visual grounding. Despite their remarkable performance, there are still two limitations. First, such a rule-based manner struggles with expressions that are not covered by predefined rules. Second, existing methods lack a mechanism for identifying and correcting visual perceptual errors of incomplete information, resulting in cascading errors caused by reasoning based on incomplete visual perception results. In this article, we propose an Error-Aware Generative Reasoning (EAGR) method for zero-shot visual grounding. To address the limited adaptability of existing methods, a reasoning chain generator is presented, which prompts LLMs to dynamically generate reasoning chains for specific referring expressions. This generative manner eliminates the reliance on human-written heuristic rules. To mitigate visual perceptual errors of incomplete information, an error-aware mechanism is presented to elicit LLMs to identify these errors and explore correction strategies. Experimental results on four benchmarks show that EAGR outperforms state-of-the-art zero-shot methods by up to 10% and an average of 7%. © 1999-2012 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes

arXiv

引用

arXiv 2024年

作者： Pan, Ziying Wang, Kun Li, Gang He, Feihong Lai, Yongxuan School of Informatics Xiamen University China Institute of Software Chinese Academy of Sciences China School of Mathematics and Information Engineering Longyan University China Shenyang Institute of Computing Technology of Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Computer Science and Technology Soochow University China

The class-conditional image generation based on diffusion models is renowned for generating high-quality and diverse images. However, most prior efforts focus on generating images for general categories, e.g., 1000 classes in ImageNet-1k. A more challenging task, large-scale fine-grained image generation, remains the boundary to explore. In this work, we present a parameter-efficient strategy, called FineDiffusion, to fine-tune large pre-trained diffusion models scaling to large-scale fine-grained image generation with 10,000 categories. FineDiffusion significantly accelerates training and reduces storage overhead by only fine-tuning tiered class embedder, bias terms, and normalization layers' parameters. To further improve the image generation quality of fine-grained categories, we propose a novel sampling method for fine-grained image generation, which utilizes superclass-conditioned guidance, specifically tailored for fine-grained categories, to replace the conventional classifier-free guidance sampling. Compared to full fine-tuning, FineDiffusion achieves a remarkable 1.56× training speed-up and requires storing merely 1.77% of the total model parameters, while achieving state-of-the-art FID of 9.776 on image generation of 10,000 classes. Extensive qualitative and quantitative experiments demonstrate the superiority of our method compared to other parameter-efficient fine-tuning methods. The code and more generated results are available at our project website: https://***/. © 2024, CC BY.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Welcome to the Eighth International Workshop on Crowd-Based Requirements engineering (CrowdRE'24)

Welcome to the Eighth International Workshop on Crowd-Based ...

引用

IEEE International Requirements engineering Conference Workshops (REW)

作者： Julian Frattini Gouri Ginde Eduard C. Groen Oliver Karras Norbert Seyff Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Dept. of Electrical and Software Engineering University of Calgary Canada Fraunhofer IESE Kaiserslautern Germany Department of Information and Computing Sciences Utrecht University Netherlands TIB - Leibniz Information Centre for Science and Technology Hannover Germany University of Applied Sciences and Arts Northwestern Switzerland Windisch Switzerland Department of Informatics University of Zurich Zurich Switzerland

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

Welcome to the 8th International Workshop on Crowd-Based Requirements engineering (CrowdRE'24), where scientists and representatives engage in interactive discussions to analyze the state-of-the-art Crowd-Based Requirements engineering (CrowdRE) and to inspire each other in ways to move forward together. The discipline of CrowdRE seeks to address the challenges of traditional requirements engineering (RE) in scaling up to settings with thousands to millions of users of (software) products or (software-driven) services, who form a large and heterogeneous group that can be denoted as a ‘crowd’ [1], [2]. The online user feedback generated by the crowd, such as texts or usage data, can be a valuable source of requirements, problems, wishes, and needs. Responding quickly, effectively, and iteratively to this feedback can greatly increase a product's success. CrowdRE comprises any approach that provides RE with suitable means for this crowd paradigm, especially by involving the crowd and by collecting, harmonizing, analyzing, and interpreting their feedback.

关键词： Conferences Logic gates software Requirements engineering

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：