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

149 篇 会议
1 册 图书

馆藏范围

150 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

82 篇 工学
- 72 篇 计算机科学与技术...
- 35 篇 软件工程
- 18 篇 控制科学与工程
- 13 篇 电气工程
- 11 篇 信息与通信工程
- 8 篇 动力工程及工程热...
- 7 篇 生物工程
- 6 篇 机械工程
- 6 篇 石油与天然气工程
- 4 篇 建筑学
- 4 篇 土木工程
- 3 篇 力学（可授工学、理...
- 3 篇 化学工程与技术
- 3 篇 生物医学工程（可授...
- 2 篇 电子科学与技术（可...
- 2 篇 交通运输工程
- 2 篇 航空宇航科学与技...
- 2 篇 环境科学与工程（可...
- 2 篇 安全科学与工程
- 2 篇 网络空间安全
43 篇 理学
- 37 篇 数学
- 12 篇 系统科学
- 9 篇 生物学
- 6 篇 统计学（可授理学、...
- 2 篇 化学
22 篇 管理学
- 19 篇 管理科学与工程(可...
- 11 篇 工商管理
- 7 篇 图书情报与档案管...
5 篇 经济学
- 5 篇 应用经济学
2 篇 法学
- 2 篇 社会学
2 篇 医学
1 篇 农学

主题

11 篇 optimization
7 篇 evolutionary alg...
7 篇 multiobjective o...
5 篇 accuracy
4 篇 deep learning
4 篇 neural networks
4 篇 yolo
4 篇 feature extracti...
4 篇 training
3 篇 reinforcement le...
3 篇 convergence
3 篇 knowledge transf...
3 篇 benchmark testin...
3 篇 prediction algor...
3 篇 data models
3 篇 convolutional ne...
2 篇 complex systems
2 篇 roads
2 篇 automation
2 篇 big data

机构

5 篇 college of compu...
4 篇 school of artifi...
4 篇 school of electr...
4 篇 school of journa...
3 篇 school of electr...
2 篇 signal and commu...
2 篇 huazhong univers...
2 篇 school of artifi...
2 篇 school of inform...
2 篇 national key lab...
2 篇 the institute of...
2 篇 tianjin hezhong ...
2 篇 eberhard karls u...
2 篇 school of comput...
2 篇 college of infor...
2 篇 software college...
2 篇 school of econom...
2 篇 hebei university...
2 篇 school of law he...
2 篇 school of safety...

作者

3 篇 jin yaochu
3 篇 liang jing
2 篇 songbai liu
2 篇 ying bi
2 篇 mao huiting
2 篇 wei liu
2 篇 liu yang
2 篇 chen chen
2 篇 hao xingxing
2 篇 bi ying
2 篇 li chen
2 篇 chen li
2 篇 luo yamei
2 篇 zhao tian-fang
2 篇 liu songbai
2 篇 yue caitong
2 篇 he cheng
2 篇 jing liang
2 篇 tian-fang zhao
2 篇 ma aoxiang

语言

145 篇 英文
5 篇 其他

检索条件"任意字段=5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023"

共 150 条记录，以下是21-30 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：

Time Series Anomaly Detection Based on data Stream Clustering 5

Time Series Anomaly Detection Based on Data Stream Clusterin...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Feng, Xudong Liu, Jin Yin, Jialu Wang, Handing Liu, Ruochen School of Artificial Intelligence Xidian University Xi'an China Engine Research Institute Aecc Shenyang Shenyang China

ISBN: (纸本)9798350393521

With the development of network-based software system, companies and operators pay more and more attention to the analysis of key performance indicators (KPI), such as network traffic and user browsing time. the challenges of anomaly detection of KPI data include the lack of labels and the unexpected changes of time series data distribution, named concept drift. Stream data clustering method is also used to deal with the above situation of stream data. An anomaly detection method based on stream data clustering is introduces in this paper to deal with the challenges above. this algorithm is called similarity based stream clustering (SBSC), which aims to deal with seasonal time series and concept drift. By using the density based stream data clustering framework based on similarity as a distance measure for time series, SBSC can deal with seasonal time series well and detect the concept drift phenomenon of time series. Compared with several common unsupervised anomaly detection algorithms, the algorithm is more efficient at identifying seasonal time series anomalies on real KPI data, and it is easier to detect concept drift and subsequence anomalies. © 2023 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

data-driven Regression Modeling for Measuring the Influence of Infodemic over Social Media 5

Data-Driven Regression Modeling for Measuring the Influence ...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Zhao, Tian-Fang Zhang, Ling-Ling Zhang, Zhi-Xuan Ma, Li-Ming School of Journalism & Communication Jinan University Guangzhou China School of Journalism &communication Shantou University Guangzhou China School of Computer Science South China University of Technology Guangzhou China

ISBN: (纸本)9798350393521

the infodemic, which encompasses rumors, fake news, disinformation, and misinformation, has resulted in significant collateral damage alongside epidemics. However, the main factors and impact of the infodemic on epidemics remain unresolved. this study systematically reviews the concept and influence mechanism of the infodemic on social media based on previous literature and external influencing factors. Empirical data is collected through questionnaires from 1125 individuals. By employing ordered logistic regression modeling, this paper analyzes the factors influencing respondents' awareness of the infodemic from four perspectives: the extent of social media usage, media preference, rational information processing, and personal factors such as education level and age. the findings suggest that individuals who are more influenced by social media exhibit higher preference for its usage and employ more rational information processing. Additionally, it is observed that those with lower levels of education and older age tend to have relatively higher levels of information cognition. © 2023 IEEE.

关键词： Epidemiology

来源：评论

学校读者我要写书评

暂无评论

AGF-Net: Adaptive Road Extraction Network with Global Feature Fusion 5

AGF-Net: Adaptive Road Extraction Network with Global Featur...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Zhang, Yajuan Zhang, Lan Niu, PengHui Xu, Wenjia School of Artificial Intelligence Hebei University of Technology TianJin China Data Center Hebei Remote Sensing Center Shijiazhuang China

ISBN: (纸本)9798350393521

Remote sensing image road extraction is of great significance for vehicle navigation and emergency insurance. However, due to the limited presence of roads in remote sensing images, they are often obstructed by shadows from trees or buildings along the road, leading to fragmented extraction results. Moreover, the variation in road scales within remote sensing images makes it challenging to accurately extract small-sized roads. therefore, automatic road extraction from remote sensing images poses a significant challenge. To address these issues, this paper proposes an adaptive road extraction method based on global feature fusion. the proposed method employs an encoder-decoder structure for end-to-end road extraction. In the middle module, a dilated convolution strip attention module is designed to enhance the continuity of roads. the dilated convolution module expands the receptive field, while the strip attention module adaptively emphasizes the relevant features along the road direction. Multiple global feature fusion modules in the skip connection are designed to fuse the features of different stages. Meanwhile, a multi-scale strip convolution module is employed to compensate for the discrepancies in feature maps across different stages. Extensive experiments reveal that AGF-Net outperforms existing methods on different road datasets (Massachusetts Road dataset and CHN6-CUG Road dataset). © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Federated k-Dominant Skyline: An Efficient Approach Under Differential Privacy 5

Federated k-Dominant Skyline: An Efficient Approach Under Di...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Cai, Guoqing Shi, Yexuan Zhou, Hao Zhou, Nan Guo, Manxue Jia, Zimeng China Mobile Research Institute Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing China Zhongguancun Pan Connected Mobile Commun. Technology Innovation and Application Research Institute Beijing China

ISBN: (纸本)9798350393521

k-dominant skyline queries have been widely used in many applications, such as multi-criteria decision making and recommendation. In recent years, a new computing paradigm, called data federation, has emerged to provide collaborative data analytics among several data owners who would like to share their data under privacy protections. this paradigm brings a new challenge to the k-dominant skyline query, where data is distributed among these owners and privacy leakages should be prevented during the query processing. Accordingly, we define the problem of federated k-dominant skyline query and show that existing solutions cannot be extended to answer this query with satisfactory efficiency. To improve the efficiency, we present an observation of a vector aggregation based re-formulation and design an efficient solution under differential privacy. Finally, extensive experiments are conducted to demonstrate that our solution is notably faster than the state-of-the-arts. © 2023 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Collaborative Production Task Allocation Decision for Multi Smart Factory 5

Collaborative Production Task Allocation Decision for Multi ...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Wu, Xiaodan Lin, Yuchen Lin, Tao School of Economics and Management Heibei University of Technology Tianjin China School of Artificial Intelligence Heibei University of Technology Tianjin China

ISBN: (纸本)9798350393521

In this study, we address a multi-factory production task allocation problem in make-to-order manufacturing that simultaneously investigates the two decisions playing critical roles in most firms: order assignment and production time. the model takes into consideration the production time windows, order splitting, and varying capacities of the factories in different periods. To efficiently allocate orders among parallel factories, we improve flexibility and fairness in production task allocation. the research aims to achieve balanced task allocation and cost optimization as primary objectives involving multiple periods and multi-items. We formulate the integrated problem as a mixed-integer nonlinear problem with minimized total costs and assignment fairness among factories. An improved Non-Dominated Sorting Genetic Algorithm (NSGA-II) is developed to address this problem. What's more, we incorporate positive feedback characteristics from the Ant Colony optimization (ACO) algorithm into the mutation process, enhancing the optimization efficiency of the algorithm while maintaining population diversity. the crossover rate and mutation rate also adaptively improve the convergence, stability, and solution quality of the algorithm. Numerous computational experiments illustrate that the I-NSGA-II algorithm is more capable of finding a feasible solution set in the Pareto space. Finally, simulation results validate the effectiveness of the proposed model and algorithm in achieving satisfactory solutions. Some managerial insights are also explored and reported. © 2023 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models 5

Evolutionary Multi-Objective Neural Architecture Search for ...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Yang, Shangshang Zhen, Cheng Tian, Ye Ma, Haiping Liu, Yuanchao Zhang, Panpan Zhang, Xingyi School of Artificial Intelligence Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Automation for Process Industries Northeastern University State Key Laboratory of Synthetical Shenyang China

ISBN: (纸本)9798350393521

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures. © 2023 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

An Unconstrained Auxiliary Framework for Constrained Many-Objective optimization 5

An Unconstrained Auxiliary Framework for Constrained Many-Ob...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Qiao, Kangjia Liang, Jing Bi, Ying Yu, Kunjie Yue, Caitong Qu, Boyang School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Electrical and Information Zhongyuan University of Technology Zhengzhou China

ISBN: (纸本)9798350393521

Constrained many-objective optimization problems (CMaOPs) include the optimization of many objective functions and satisfaction of constraints, which seriously enhance the difficulty of problems. Although several constrained many-objective evolutionary algorithms (CMaOEAs) have been designed, they still have difficulties in tackling many objectives and constraints at the same time. To better solve CMaOPs, this paper proposes an unconstrained auxiliary framework, in which an auxiliary task without constraints is developed to reduce the search difficulties of constraints. Moreover, to tackle many objectives, the existing CMaOEAs are employed to address the auxiliary task, in which the constraint values of solutions are set to zeros. In the experiments, one classic CMaOEA and two latest CMaOEAs are integrated into the framework to form three new algorithms. the results show the effectiveness and superiority of the framework. Besides, the winner among three new algorithms is compared with several existing CMaOEAs and shows better results. Meanwhile, we discuss the reasons that why the unconstrained framework is effect for the existing benchmark functions. Accordingly, we refer to that new test functions are urgently needed for the development of constrained many-objective optimization. © 2023 IEEE.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Dynamic Constrained Multi-Objective Evolutionary optimization via Adaptive Two-Stage Archiving and Autoencoder Prediction 5

Dynamic Constrained Multi-Objective Evolutionary Optimizatio...

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Wang, Qianhui Ye, Yulong Zhu, Qingling Liu, Songbai Lin, Qiuzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350393521

Dynamic constrained multi-objective optimization problems (DCMOPs) are characterized by time-varying objectives and constraints, requiring optimization algorithms that can rapidly track the changing Pareto-Optimal Set (POS).A new dynamic constrained multi-objective evolutionary algorithm with adaptive two-stage archiving and autoencoder prediction is proposed in this paper, called ATAP to effectively solve DCMOPs. Specifically, ATAP designs a differential denoising autoencoder (DDA) prediction strategy, which applies a denoising autoencoder to thoroughly analyze change trends of historical population and predict some initial solutions in the new environment. Subsequently, to promote greater diversity within the initial population, a differential rule is implemented, effectively addressing the potential scarcity of diversity caused by constraints. Moreover, ATAP introduces an adaptive two-stage archiving (ATA) constraint handling technique, which can dynamically adjust evolutionary stages based on the state of the population. this approach can adaptively determine preferences between objectives and constraints, achieving a better balance. In this way, ATA and DDA are well cooperated to efficiently solve DCMOPs with time-varying constraints and objectives. the experimental results demonstrate that the proposed ATAP is effective and has some advantages over three competitive algorithms when solving the CEC2023 DCMOP benchmark. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

data Uncertainty Learning in Breast Cancer Recognition 5

Data Uncertainty Learning in Breast Cancer Recognition

引用

5th international conference on data-driven optimization of complex systems, docs 2023

作者： Zhao, Ku Luo, Tao Chen, Kehan Zhang, Libo College of Artificial Intelligence Southwest University Chongqing China

ISBN: (纸本)9798350393521

Breast cancer has emerged as the most prevalent malignancy, occupying the top position in both incidence and mortality among cancers in women. Distinguishing between benign and malignant breast tumors plays a crucial role in achieving early detection and treatment of breast cancer. As breast biopsy is a critical diagnostic evidence for confirming the malignant tumors of the breast, using its pathological images to diagnose the benign or malignant nature of breast tumors by deep learning models has become an important approach in breast cancer recognition. the noise in the digitization process of biopsy samples' pathological images will lead to decision uncertainty. Nevertheless, most existing deterministic models fail to accurately represent the uncertainty in the data, leading to incorrect breast cancer diagnoses. Misdiagnosis comes at a high cost, as it can result in patients missing the optimal treatment window or receiving unnecessary treatments, etc. To solve this problem, we introduce data uncertainty learning, an effective method for representing noise, into breast cancer recognition for the first time. then, we propose a data uncertainty learning model for breast cancer recognition task, which learns the mean to recognize breast cancer and variance to represent uncertainty of samples. Specifically, we design a variance regularizer to guide the model adjusting optimization weights of the samples based on the uncertainty and enable the model to focus more on high-quality samples while suppressing the influence of noise, which enhances the performance of breast cancer recognition. Finally, insightful evaluation demonstrates the effectiveness of our model. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Innovization-based Evolutionary Large-Scale Multiobjective optimization 6

Innovization-based Evolutionary Large-Scale Multiobjective O...

引用

6th international conference on data-driven optimization of complex systems (docs)

作者： Liu, Songbai Yan, Qianqiang Wang, Zeyi Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798350377859;9798350377842

How to handle the high dimensional search space effectively is a burning research question in evolutionary computation when solving large-scale multiobjective optimization problems. To alleviate it, we propose a new evolutionary algorithm with a tactic of innovization-based representation learning, which aims to discover innovative solution principles from compressed representations. Specifically, a neural net, including an encoder and a decoder, is iteratively learned to transform input solutions into the predictions that dominate them. the encoder thus can capture their compressed representations that retain the information necessary for making good predictions. the decoder can create innovative promising solutions from newly explored representations by evolutionary search. Finally, the proposed algorithm is tested on synthetic and real-world benchmarks with the dimension of variables up to 10(4). Comparison results show the superior performance of the proposed optimizer in solving these benchmarks within 10(5) function evaluations.

关键词： Evolutionary Algorithms Large-Scale Multiobjective optimization Innovization Representation Learning

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15页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：