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

154 篇 会议
41 篇 期刊文献

馆藏范围

195 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

120 篇 工学
- 81 篇 计算机科学与技术...
- 76 篇 软件工程
- 54 篇 信息与通信工程
- 21 篇 光学工程
- 21 篇 生物工程
- 14 篇 化学工程与技术
- 12 篇 电气工程
- 12 篇 电子科学与技术（可...
- 12 篇 控制科学与工程
- 12 篇 生物医学工程（可授...
- 9 篇 仪器科学与技术
- 7 篇 机械工程
- 6 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 土木工程
- 3 篇 建筑学
- 3 篇 环境科学与工程（可...
- 3 篇 网络空间安全
72 篇 理学
- 34 篇 物理学
- 24 篇 数学
- 23 篇 生物学
- 16 篇 化学
- 7 篇 统计学（可授理学、...
- 4 篇 大气科学
34 篇 管理学
- 25 篇 图书情报与档案管...
- 9 篇 管理科学与工程(可...
8 篇 医学
- 8 篇 基础医学(可授医学...
- 8 篇 临床医学
- 7 篇 药学(可授医学、理...
4 篇 教育学
- 4 篇 教育学

主题

25 篇 feature extracti...
11 篇 deep learning
11 篇 training
10 篇 neural networks
10 篇 convolution
10 篇 accuracy
9 篇 speech processin...
9 篇 semantics
6 篇 data models
6 篇 adaptation model...
5 篇 object detection
5 篇 speech enhanceme...
5 篇 attention mechan...
4 篇 noise reduction
4 篇 raman spectrosco...
4 篇 noise measuremen...
4 篇 transformers
4 篇 signal processin...
4 篇 acoustics
4 篇 optimization

机构

52 篇 school of comput...
41 篇 xinjiang key lab...
27 篇 key laboratory o...
16 篇 school of inform...
13 篇 department of el...
9 篇 school of intell...
9 篇 xinjiang key lab...
8 篇 school of inform...
7 篇 department of el...
7 篇 key laboratory o...
7 篇 key laboratory o...
7 篇 china mobile res...
7 篇 college of infor...
6 篇 xinjiang key lab...
6 篇 college of softw...
6 篇 college of softw...
5 篇 school of comput...
5 篇 key laboratory o...
5 篇 xinjiang key lab...
4 篇 college of compu...

作者

22 篇 jia zhenhong
22 篇 hamdulla askar
12 篇 shi fei
12 篇 he liang
12 篇 askar hamdulla
11 篇 liang he
11 篇 gang zhou
11 篇 zhenhong jia
10 篇 wang jiajia
10 篇 zhou gang
10 篇 lv xiaoyi
9 篇 chen chen
9 篇 chen cheng
9 篇 zhihua huang
8 篇 ying hu
8 篇 qin xizhong
7 篇 fei shi
7 篇 huang xiaohui
7 篇 hu ying
7 篇 ablimit mijit

语言

192 篇 英文
2 篇 其他
2 篇 中文

检索条件"机构=Key Laboratory of Signal Detection and Processing"

共 195 条记录，以下是71-80 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：

Unsupervised Domain Adaptive Learning for Image Desnowing with Real-World Data

Unsupervised Domain Adaptive Learning for Image Desnowing wi...

引用

IEEE International Conference on Image processing

作者： Jingxu Ren Gang Zhou Yusen Zhu Yangxin Liu Juan Chen Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

Snow images usually contain snow grains, snow streaks, and mist, which greatly affect the visibility of images. Currently, supervised learning with synthetic data often faces limitations when it comes to handling real-world snow images. To address this crucial issue, this work proposes an unsupervised domain adaptation image snow removal framework. The framework improves the performance on real-world images by learning a domain classifier in adversarial training manner. Additionally, considering the diversity of snowflake shapes and sizes in real-world snow images, we design a multiple-kernel dilated convolution module. Extensive experiments on three representative datasets have validated that our model can achieve better results than existing desnowing methods. More importantly, experiments on real datasets show that the proposed method obtains state-of-the-art performance in real-world desnowing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improved Self-Consistency Training with Selective Feature Fusion for Sound Event detection

Improved Self-Consistency Training with Selective Feature Fu...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Mingyu Wang Yunlong Li Ying Hu Key Laboratory of signal detection and processing Xinjiang University Urumqi China

Sound event detection (SED) is a joint task of identifying the categories and time boundaries of sound events within an audio clip. In this paper, we propose an improved self-consistency training (ISCT) strategy for semi-supervised SED based on Mean Teacher (MT) method. For teacher and student models, each adopts two branches with the same CRNN structure, the two branches help training the model by means of consistency regularization. ISCT strategy incorporates self-consistency loss on the basis of MT loss to improve the generalization performance of the model. A selective feature fusion (SFF) module is designed for applying in the shallow layers of the feature extraction part to selectively fuse the features with different scales. A parallel attention (PA) module is designed for applying in the deep layers of the feature extraction part to obtain much richer high-level features by the channel and spatial-wise attention. Ablation experiments verify the effectiveness of our proposed ISCT strategy, SFF and PA modules. In addition, compared with four methods, our proposed method achieves competitive performance on the DCASE 2020 task4 dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Rumor detection Method Incorporating Correlation Features 3

A Rumor Detection Method Incorporating Correlation Features

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Cai, Xiangsheng Tohti, Turdi Hamdulla, Askar Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi830017 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Most of the rumors on social media (e.g., Twitter) revolve around a certain domain or topic, which means that they want to express roughly the same meaning, only the way of expression or the words used are different. Most of the existing rumor detection work focuses on text semantic features, user information features, comment features, and communication structure features, but rarely utilizes the rumor features associated with the original text. Therefore, a GCN-BERT -based rumor detection method is proposed in this paper, which not only utilizes the rumor features of a message itself but also utilizes the rumor features of all the texts and words associated with it when performing rumor detection. Finally, three Twitter datasets are used for comparison experiments, and the results fully demonstrate that the method is more effective than existing rumor detection methods. © 2022 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Fast Sampling Based on Policy Gradient for Diffusion-Based Speech Enhancement

Fast Sampling Based on Policy Gradient for Diffusion-Based S...

引用

International Symposium on Chinese Spoken Language processing

作者： Yubo Jiang Zhihua Huang School of Computer Science and Technology Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Urumqi

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

The effectiveness of diffusion-based generative models in speech enhancement tasks has been reported. These generative methods can produce high-quality audio with less distortions and outperform their discriminative counterparts, especially in mismatched conditions. However, due to the multi-step denoising property, these models usually have slower audio generation speed and a tendency to accumulate errors in the enhancement process. To address these issues, we propose a novel approach that considers the reverse process as a sequential decision-making task and constructs the distribution similarity metric into the training objective. In this way, the generator can learn data distributions well and explore more efficient sampling trajectories. The results of our experiments indicate that our proposed method not only reduces the number of steps required for audio generation, but it also produces even higher quality samples and preserves the generalizability to unknown noise.

关键词： Training Measurement Noise reduction Noise Reinforcement learning Speech enhancement Probabilistic logic Distortion Generators Trajectory

来源：评论

学校读者我要写书评

暂无评论

RoPE-BAM: Nested Entity Recognition Based on Rotary Position Embedding and Biaffine Attention Mechanism 3

RoPE-BAM: Nested Entity Recognition Based on Rotary Position...

引用

3rd International Conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Deng, Jinxin Qin, Xizhong Yang, Rong Lv, Xiaoyi School of Information Science and Engineering Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China The Fifth Affiliated Hospital of Xinjiang Medical Urumqi830017 China

ISBN: (纸本)9781510668355

Named entity recognition (NER) involves two main types: nested NER and flat NER. The span-based approach classifies entity types by head-tail pair span representations and can handle nested and flat entities uniformly. However, the span-based approach uses a single feature and ignores the relative position information between head-tail pairs, which affects the precision of entity recognition. Therefore, we propose a nested entity recognition model that combines rotary position embedding and biaffine attention mechanism (RoPE-BAM) to improve the model performance by adding relative position features to the span representations. Concretely, we first obtain the head sequence and tail sequence representations through two feedforward networks. Then, to incorporate the relative position features, rotary position embedding is applied to both head and tail sequences. Finally, we use a biaffine attention mechanism to capture the span representations while generating the relative position information in the span. Extensive experiments were conducted on five widely-used benchmark datasets to demonstrate the effectiveness of our proposed RoPE-BAM model. © 2023 SPIE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Log anomaly detection based on parallel fusion of CNN and GRU

Log anomaly detection based on parallel fusion of CNN and GR...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Cheng Li Jiaqing Mo Gang Zhou Guangming Li Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the development of deep learning and big data technology, system log anomaly detection technology has received widespread attention. Although many methods have achieved good results in anomaly detection in logs, it remains an extremely difficult task mining the rich dependencies hidden within log data from both global and local perspectives. In this paper, we propose a multi-scale parallel fusion network based on GRU and CNN, which more comprehensively captures the overall dependencies of log data. Specifically, we employ BERT to semantize the preprocessed log sequences, subsequently feeding the semantic vectors into the neural networks of a dual-branch CNN and a variant GRU. This approach enables the capture of both local and global features of the log sequences. Finally, we parallelly fuse the outputs of the dual-branch CNN and variant GRU. We conduct extensive experiments and evaluate three well-known benchmark datasets, BGL, Thunderbird, and Spirit. The results show that our method is better than other methods.

关键词： Deep learning Fuses Neural networks Semantics Network architecture Big Data Feature extraction

来源：评论

学校读者我要写书评

暂无评论

An end-to-end algorithm for predicting missing load data based on waveGAIN

An end-to-end algorithm for predicting missing load data bas...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jiang Zhang Gang Zhou Yuwei Feng Sen Yang Wanlin Wang Jiaqing Mo Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The accuracy of short-term load forecasting has improved with the development of deep learning on complete datasets. However, load data is often lost due to various complex factors in the power system, causing it is a challenge for incomplete load forecasting. To address this issue, this paper proposes an end-to-end load-completion and forecasting network. Specially, we designed the Wavelet-enhanced Generative Adversarial Imputation Net (waveGAIN) to improve the accuracy of data completion. Then, we propose an end-to-end training strategy that combines waveGAIN and Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the accuracy of data filling and reduce the load forecasting error. The experimental results show that our proposed method improves the prediction accuracy in the case of different patterns and levels of missing data. And our method achieves good results even in case of 80% missing data.

关键词： Training Deep learning Accuracy Load forecasting Predictive models Data models Power systems

来源：评论

学校读者我要写书评

暂无评论

LinWA:Linear Weights Attention for Time Series Forecasting

LinWA:Linear Weights Attention for Time Series Forecasting

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Qiang Li Jiwei Qin Dezhi Sun Fei Shi Daishun Cui Jiachen Xie School of Computer Science and Technology Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Transformer has achieved excellent results in the field of time series forecasting. However, some recent studies have pointed out that the existing Transformer models are ineffective in preserving the temporal order information. To solve this problem, we propose a Linear Weighted Attention mechanism (LinWA), a new, simple, and effective attention weights calculation method. Linwa calculates the linear weights and the attention weights, respectively, and combines them to reduce the proportion of the original attention weights to use the order information effectively and improve the forecasting performance. Additionally, we introduce an adaptive parameter adjustment mechanism to adjust the proportion between linear weights and attention weights dynamically. Experimental results on six publicly available datasets show that LinWA preserves temporal order well and improves performance on many Transformer models.

关键词： Adaptation models Attention mechanisms Time series analysis Neural networks Benchmark testing Transformers Forecasting

来源：评论

学校读者我要写书评

暂无评论

HiparaLog: Improving Log-based Anomaly detection through Parameter Feature Integration

HiparaLog: Improving Log-based Anomaly Detection through Par...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Guangming Li Jiaqing Mo Gang Zhou Cheng Li Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Anomaly detection methods based on system logs are effective means of maintaining large-scale systems. In recent years, numerous excellent log anomaly detection methods have emerged. However, existing log anomaly detection methods identify anomalous logs either through the single template feature or by combining template features with the contextual information, ignoring the role of parameter value features in log messages. In this paper, we propose a novel log anomaly detection method named HiparaLog. HiparaLog converts each raw log message into a semantic vector, which incorporates both parameter features and template features. It then detects anomalies by employing a self-attention-enhanced GRU model, which has the ability to capture the global dependencies and contextual information within the log sequence. In this way, HiparaLog is able to detect abnormal logs from multiple dimensions. Our experimental results on two of the most challenging datasets demonstrate that HiparaLog achieves superior performance compared to current state-of-the-art methods.

关键词： Accuracy Semantics Neural networks Feature extraction Vectors Large-scale systems Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model 31

RGR-KBQA: Generating Logical Forms for Question Answering Us...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Feng, Tengfei He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798891761964

In the field of natural language processing, Knowledge Base Question Answering (KBQA) is a challenging task that involves accurately retrieving answers from structured knowledge. Existing methods often face issues when generating query statements using LLMs, as the knowledge introduced may be imprecise and the models themselves may exhibit hallucination problems, leading to low accuracy, particularly when dealing with complex questions. To address these challenges, we introduce a novel semantic parsing approach called RGR-KBQA, which adopts a Retrieve-Generate-Retrieve framework. The first retrieval step introduces factual knowledge from a knowledge graph to enhance the semantic understanding capabilities of LLMs, thereby improving generation accuracy of logical form. The second step uses a fine-tuned model to generate the logical form, and the final step involves unsupervised relation and entity retrieval to further enhance generation accuracy. These two retrieval steps help alleviate the hallucination problems inherent in LLMs. Experimental results show that RGR-KBQA demonstrate promising performance on CWQ and WebQSP datasets. © 2025 Association for Computational Linguistics.

关键词： Question answering

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20页 << < 4 5 6 7 8 9 10 11 12 13 > >>

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：