检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

565 篇 会议
7 篇 期刊文献
2 册 图书

馆藏范围

574 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

176 篇 工学
- 173 篇 计算机科学与技术...
- 108 篇 电气工程
- 28 篇 软件工程
- 5 篇 控制科学与工程
- 4 篇 信息与通信工程
- 1 篇 力学（可授工学、理...
- 1 篇 材料科学与工程（可...
- 1 篇 电子科学与技术（可...
59 篇 理学
- 59 篇 数学
- 2 篇 物理学
- 2 篇 系统科学
- 2 篇 统计学（可授理学、...
19 篇 经济学
- 18 篇 理论经济学
- 1 篇 应用经济学
12 篇 管理学
- 8 篇 管理科学与工程(可...
- 7 篇 工商管理
- 4 篇 图书情报与档案管...
1 篇 法学
- 1 篇 社会学

主题

290 篇 scientific compu...
91 篇 computer science
64 篇 cloud computing
51 篇 computational mo...
36 篇 polynomials
34 篇 algorithm design...
28 篇 algebra
27 篇 computer archite...
25 篇 mathematics
25 篇 testing
22 篇 software algorit...
22 篇 complexity theor...
21 篇 electronic mail
20 篇 monitoring
20 篇 standards
19 篇 grid computing
19 篇 semantics
18 篇 application soft...
18 篇 distributed comp...
17 篇 concurrent compu...

机构

10 篇 research institu...
8 篇 tech univ cluj n...
7 篇 department of co...
7 篇 faculty of infor...
6 篇 faculty of mathe...
6 篇 department of co...
6 篇 faculty of compu...
5 篇 department of co...
5 篇 department of co...
5 篇 computer science...
4 篇 department of co...
4 篇 nicolaus coperni...
4 篇 research institu...
4 篇 institute e-aust...
4 篇 department of co...
4 篇 romanian acad in...
4 篇 computer science...
4 篇 faculty of autom...
3 篇 department of co...
3 篇 department of co...

作者

9 篇 gabriel ciobanu
8 篇 adina magda flor...
7 篇 petcu dana
7 篇 james h. davenpo...
7 篇 jebelean tudor
7 篇 eneia nicolae to...
7 篇 nebojsa bacanin
7 篇 tudor jebelean
6 篇 catalin stoean
6 篇 ciobanu gabriel
6 篇 nicolae tapus
6 篇 valentin cristea
5 篇 mugurel ionut an...
5 篇 dorel lucanu
5 篇 ardagna danilo
5 篇 ciprian dobre
5 篇 ruxandra stoean
5 篇 stephen m. watt
5 篇 florin pop
5 篇 simson daniel

语言

574 篇 英文

检索条件"任意字段=7th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing"

共 574 条记录，以下是561-570 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

SUDS: A Simplified U-Net Architecture with Depth-Wise Separable Convolutions

SUDS: A Simplified U-Net Architecture with Depth-Wise Separa...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Vlad-Constantin Ionete Cosmin Marsavina Department of Computers and Information Technology University Politehnica of Timisoara Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Medical image segmentation is one of the most important topics in the field of computer vision and plays a crucial role in computer-aided diagnosis. U-Net paved the way for a series of variants that took advantage of the key characteristics of this network. In this article, several features proposed in different variants of U-Net are adapted and experimented upon to create a new architecture that maintains the idea of a U-shaped structure. the proposed architecture takes advantage of the efficient depth-wise separable convolution, but with a twist. Instead of using the pointwise convolution as the last step in the depth-wise separable convolution, it utilizes the so-called Ghost Module. this results in a highly efficient network with a reduced complexity, that still has excellent segmentation performance. We compared SUDS with U-Net and its variants across multiple segmentation tasks from two categories, skin lesion segmentation and colonscopy segmentation. Experiments demonstrate that SUDS has similar segmentation accuracy compared to the other networks, while the number of parameters and floating-point operations are greatly reduced.

关键词： Training Image segmentation three-dimensional displays Accuracy Convolution scientific computing Computer architecture Skin Complexity theory Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Novel Lossless Crypto-Compression Scheme for Medical Images

Novel Lossless Crypto-Compression Scheme for Medical Images

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Ionut-Eduard Matei Computer Science Department West University of Timisoara Timisoara Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

the transmission of patients' medical images over the network increases the instances of fraud involving the theft of patient information. Crypto-compression schemes are used to ensure high security for medical images and better compression, which is necessary for efficient network transmission. In this paper, I propose a new crypto-compression scheme and a novel lossless compression algorithm based on an improved version of the Lempel-Ziv 77 algorithm, which uses a sliding window of 64 KB in size, combined with a variation of the RLC algorithm. Unlike other methods, the proposed crypto-compression scheme employs a double level of security ensured by the ElGamal cryptosystem based on elliptic curves and the XChaCha20 stream cipher, thus providing more secure transmission of images. the crypto-compression scheme (ECX-LZ-RLC) proposed in this paper is compared with four modern crypto-compression methods, such as JPEG-AES, FMT-AES, JPEG-EC-LFSR, and EC-ElGamal. Experimental results show that ECX-LZ-RLC provides a higher compression rate and a better average crypto-compression time compared to other modern crypto-compression schemes, evaluated for grayscale and RGB medical images with different resolutions, taken from certain datasets.

关键词： Image coding Image resolution scientific computing Elliptic curves Streaming media Gray-scale Fraud Security Compression algorithms Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Class-Incremental Learning Enhanced: Assessing New Exemplar Selection Strategies in the iCaRL Framework

Class-Incremental Learning Enhanced: Assessing New Exemplar ...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Erik Zsolt Varga Faculty of Mathematics and Computer Science West University of Timişoara Timişoara Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

the field of machine learning has increasingly focused on incremental learning, enabling systems to continually adapt and improve by integrating new knowledge while retaining previously learned information. One particular area of interest is class-incremental learning, where the learning system sequentially acquires new classes without access to or with limited exposure to past data. the primary challenge in class-incremental learning is catastrophic forgetting, wherein the model tends to overlook previously learned classes when confronted with new tasks. One of the class-incremental learning approaches to mitigate catastrophic forgetting is the Incremental Classifier and Representation Learning (iCaRL) framework. In this paper, we propose three new selection criteria for the iCaRL approach. Our best selection criterion, which uses the K-Means clustering algorithm to create diverse groups and then selects exemplars close to the centroids of the clusters, outperforms the original iCaRL selection criterion by over 16% for the MNIST dataset and by over 12% for the FashionMNIST dataset in terms of average accuracy. the full implementation of the iCaRL approach, along with the three proposed selection criteria and detailed experimental results logs, can be found in our publicly available Github repository. By contributing to the ongoing development of class-incremental learning techniques, we aim to support the creation of more effective and robust lifelong machine learning systems.

关键词： Representation learning Learning systems Adaptation models Machine learning algorithms Incremental learning Accuracy scientific computing Clustering algorithms Classification algorithms Software development management

来源：评论

学校读者我要写书评

暂无评论

Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting

Assessing Predictor Influence in LSTM Models for Enhanced So...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Alexandru-Adrian Ciobanu Nebojsa Bacanin Tilman Sanders Catalin Stoean Department of Computer Science University of Craiova Craiova Romania Faculty of Informatics and Computing Singidunum University Belgrade Serbia Department of Electrical Engineering and Computer Science FH Muenster Steinfurt Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. this study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. the results also demonstrate that predictor importance varies significantly between the two models. these findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance.

关键词： Accuracy Computational modeling Solar energy Production Predictive models Data models numerical models Forecasting Long short term memory Meteorology

来源：评论

学校读者我要写书评

暂无评论

Fluorescence Lifetime of NV Centers for Temperature-Insensitive All-Optical Magnetic Field Sensing

Fluorescence Lifetime of NV Centers for Temperature-Insensit...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Ludwig Horsthemke Jens Pogorzelski Dennis Stiegekötter Frederik Hoffmann Ann-Sophie Bülter Lutz Langguth Robert Staacke Sarah Trinschek Markus Gregor Peter Glösekötter Department of Electrical Engineering and Computer Science FH Münster—University of Applied Sciences Steinfurt Germany Quantum Technologies GmbH Leipzig Germany Department of Engineering Physics FH Münster—University of Applied Sciences Steinfurt Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

We investigate the combined temperature and magnetic field dependent behavior of high NV-density microdiamonds in an all-optical frequency-domain based setup, utilizing the change in fluorescence lifetime. We use these frequency-domain data to train neural networks for the prediction of temperatures and magnetic fields. At zero magnetic field, we show the ability to predict temperatures in the range of 0° C to 100°C, with a standard deviation of 1.24 °C. Furthermore, we show the ability to predict applied magnetic fields, while varying the ambient temperature. We achieve a higher accuracy, compared to the approach of observing of the fluorescence intensity at a single excitation frequency.

关键词： Temperature sensors Temperature distribution Temperature dependence Accuracy scientific computing Frequency-domain analysis Fluorescence Sensors Magnetic fields Standards

来源：评论

学校读者我要写书评

暂无评论

How to Tackle Fake Medical Content with Clustering and Transformers

How to Tackle Fake Medical Content with Clustering and Trans...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Radu Razvan Slavescu Marina Bianca Trif Anca Marginean Kinga Cristina Slavescu Department of Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania 2nd Pediatrics Clinic Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Spreading fake medical information / news online might threat the public health, e.g., by decreasing the vaccination rates or the level of trust in the healthcare process. the system presented in our paper aims to address this by determining in an automatic manner whether a text either contains or implies some claims known to be false. To this end, it relies on the Monant dataset, which comprises medical posts and claims whose veracity has been established by experts in the field. the dataset is used in two ways. First, the set of false claims aimed to be detected was obtained from it. these false claims are clustered, allowing the next steps to be performed against one cluster only, thus reducing the response time. Second, to detect a post's falsehood, this gets summarized using transformers, then the summary is compared similarity-wise with each element of the chosen cluster of false claims, using all-MiniLM-L6-v2 and a variation of BioBERT. A high similarity score is interpreted as the presence of the false claim in the summary. the results obtained from the experiments demonstrate, through manually checked examples, that the system manages to identify about 77% of the false claims within the text at hand (with an F1-value of 0.73).

关键词： Hands scientific computing Clustering algorithms Transformers Vaccines Time factors Public healthcare Fake news

来源：评论

学校读者我要写书评

暂无评论

Enhanced Anomaly Detection in Automotive Systems Using SAAD: Statistical Aggregated Anomaly Detection

Enhanced Anomaly Detection in Automotive Systems Using SAAD:...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Dacian Goina Eduard Hogea George Maties Faculty of Mathematics and Computer Science West University of Timisoara Timisoara Timis Faculty of Electrical Engineering Gheorghe Asachi Technical University of Iasi Iasi

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

this paper presents a novel anomaly detection methodology termed Statistical Aggregated Anomaly Detection (SAAD). the SAAD approach integrates advanced statistical techniques with machine learning, and its efficacy is demonstrated through validation on real sensor data from a Hardware-in-the-Loop (HIL) environment within the automotive domain, where physical components of the vehicle are interfaced with simulation models. the HIL setup provides a controlled, real-time testing framework that replicates actual driving conditions, allowing for the validation of the vehicle's control systems under various operational scenarios without the need for a physical vehicle prototype. the key innovation of SAAD lies in its ability to significantly enhance the accuracy and robustness of anomaly detection when combined with Fully Connected Networks (FCNs) augmented by dropout layers. Comprehensive experimental evaluations indicate that the standalone statistical method achieves an accuracy of 72.1%, whereas the deep learning model alone attains an accuracy of 71.5%. In contrast, the aggregated method achieves a superior accuracy of 88.3% and an F1 score of 0.921, thereby outperforming the individual models. these results underscore the effectiveness of SAAD, demonstrating its potential for broad application in various domains, including automotive systems.

关键词： Deep learning Technological innovation Accuracy Statistical analysis scientific computing Robustness Data models Anomaly detection Automotive engineering Testing

来源：评论

学校读者我要写书评

暂无评论

Exploring Sum-Product Networks for Image Classification. Applications

Exploring Sum-Product Networks for Image Classification. App...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Maria-Alexandra Badea Elena Pelican Raluca Vernic Doctoral School of Mathematics Ovidius University Constanta Romania Faculty of Mathematics and Informatics Ovidius University Constanta Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Sum-Product Networks (SPN) represent a significant and novel development within the probabilistic graphical models domain that unites the representational capabilities of deep learning with effective probabilistic inference. Our focus is on presenting these unique structural properties of SPNs along with their theoretical foundations and putting them into application on real-life diverse datasets. Despite its promising prospects, only a few existing applications are using this technology. To illustrate how this method can be used in practice, we have applied the model to four different datasets: the popular MNIST dataset, the Fashion MNIST dataset, the Fast, Furious and Insured dataset, and the Garbage Classification dataset, respectively. In the first two datasets, we used SPN s to classify hand-written digits and fashion products respectively displaying their effectiveness within a standard image classification task. the models' architecture incorporates SPN-specific layers together with conventional convolutional layers thereby achieving remarkable performance measures. the results obtained for the last two datasets outperformed the results obtained by the classical deep learning methods. All these show that SPNs form a strong basis for theoretical exploration and practical application to probabilistic modeling along with deep learning. this paper adds to the collective understanding of SPN applications and, at the same time, highlights possible further areas of study within the field.

关键词： Deep learning Support vector machines Adaptation models Uncertainty scientific computing Probabilistic logic Stability analysis Convolutional neural networks Standards Image classification

来源：评论

学校读者我要写书评

暂无评论

Synthetic Dataset Generation for Edge Drone Inference and Training

Synthetic Dataset Generation for Edge Drone Inference and Tr...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Bogdan Nedelcu Adina Magda Florea Faculty of Automatic Control and Computers Politehnica University of Bucharest

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

this research investigates the generation of synthetic aerial images for training deep neural networks., particularly targeting the task of object detection in mostly top-down views. the experiment leveraged a simulation environment to create a complex dataset., addressing the challenges associated with collecting real-world aerial images. the work encompassed several critical components., including algorithm parameter variation for ground truth annotation., the exploration of bounding box generation techniques and an investigation into camera orientations and altitude to simulate real-world scenarios. Additionally., the research delved into the training of neural network models., specifically tailored for low-power devices., particularly the Coral Edge TPU. the findings of this research indicate the feasibility of utilizing synthetic environments and datasets to train neural networks for aerial image analysis. the trained model showcased promising results., with the potential for broader applications in object detection. the study also addressed safety considerations., emphasizing the need for high-resolution cameras in drone operations to detect objects., such as human bodies., from safe distances. Furthermore., the paper outlines a roadmap for future work., highlighting areas such as multi-class object detection., integration with autopilot systems., and real-world testing. this research serves as a foundational step toward harnessing synthetic data and deep learning for enhanced drone capabilities in diverse scenarios., with a commitment to safety and compliance with aviation regulations.

关键词： Training scientific computing Image edge detection Object detection Cameras Regulation Safety Drones Synthetic data Testing

来源：评论

学校读者我要写书评

暂无评论

Infrastructure as Code for Heterogeneous computing

Infrastructure as Code for Heterogeneous Computing

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Daniel Vladusic Dragan Radolovic XLAB Slovenia

ISBN: (数字)9781728176284

ISBN: (纸本)9781728176291

Setting up an infrastructure for application deployment is a non-trivial task. We usually simplify the task by using the Infrastructure as Code (IaC) approach. through IaC we set-up software defined infrastructure, able to run applications. Several tools and platforms have been developed to describe the system and to implement the actual deployment of the application (e.g., Puppet, Chef, Ansible, Terraform, etc.). When the infrastructure is heterogeneous (e.g., combination of Cloud and HPC) the challenges to set up a collaborative infrastructure is even harder, as the paradigms and environments differ significantly. Cloud infrastructure is focusing on servers, events, functions, while the HPC infrastructure is procedural and takes the application and merely executes it, usually on bare metal. As expected, the applications deployed on the respective systems differ in their dynamics and longevity. Whilst deployment of applications on the Cloud can be solved with the aforementioned tools, the HPC systems are in this respect in their infancy - the application is usually scheduled to be executed on a pre-defined set of processors and from that point on, the scripts merely gather input data, execute the application and then organize the output data. Merging the two approaches is thus currently rather hard and requires an explicit boundary between the Cloud and HPC parts of the application, posing a significant issue for the overall modelling and thus set-up of the system with the IaC approach. SODALITE, an H2020 project, is targeting simplification of the application deployment complexity while retaining or improving application performance on targeted HPC heterogeneous and cloud systems. the application deployment is abstracted through modelling of application's component relationships, policies and performance. the application is deployed using appropriate container technologies, matching the targeted heterogeneous HPC and cloud-based platforms. the starting point is th

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共58页 << < 49 50 51 52 53 54 55 56 57 58 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：