检索结果-内蒙古大学图书馆

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

Performance Analysis of Heterogeneous Optical Beams based MIMO Visible Light Communication System 7

Performance Analysis of Heterogeneous Optical Beams based MI...

引用

7th International Conference on Computer and Communications, ICCC 2021

作者： Ding, Jupeng Chih-Lin, I. Wang, Lili Zhao, Kai Zheng, Jiong Xinjiang University Key Laboratory of Signal Detection and Processing In Xinjiang Uygur Autonomous Region School of Information Science and Engineering Uygur China China Mobile Research Institute Beijing China Ludong University School of Information and Electrical Engineering Yantai China

ISBN: (纸本)9781665409506

To a large extent, the multiple input multiple output (MIMO) visible light communication (VLC) is still restricted in homogeneous Lambertian research paradigm. In this work, the heterogeneous optical beams configuration is proposed for the MIMO VLC system. Specifically, two typical non-Lambertian optical beams i.e. LUXEON Rebel UB and LB patterns are introduced to heterogeneous MIMO beams configurations. In typical indoor scenario, the channel capacity of the proposed heterogeneous MIMO VLC system is evaluated and compared with the counterpart of the homogeneous Lambertian MIMO VLC system. Numerical results illustrate that the LUXEON Rebel UB based heterogeneous configuration could provide more than 9.7% average MIMO VLC capacity gain. Respectively, the capacity dynamic range is elevated to about 74.0-156.2 Mbps from the original about 75.9-116.2 Mbps of homogeneous configuration. © 2021 IEEE.

关键词： Visible light communication

来源：评论

学校读者我要写书评

暂无评论

Obstacle detection in Off-road Environments Based on LiDAR

Obstacle Detection in Off-road Environments Based on LiDAR

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Arzigul Ahat Eksan Firkat Tayir Mijit Askar Hamdulla School of Information Science and Engineering Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

Mapping off-road terrain is a challenging task, especially when compared to urban terrain. The complexity and roughness of off-road terrain make it difficult to achieve accurate mapping results. Obstacle detection in such environments is also a challenging task, as the performance of LiDAR-based methods depends on the point cloud density at the location of the obstacle. High line beam LiDAR is expensive, while low line beam LiDAR is sparse, making it difficult to obtain dense point clouds. In this paper, we propose a multi-LiDAR fusion approach that uses roof LiDAR as the primary sensor and front LiDAR as a supplement. The approach employs point cloud filtering, dedistortion, and point cloud registration to fuse data from multiple sensors. This results in a richer view of the environment and more intensive measurement data. We combine the voxel map with a twodimensional grid map and accelerate the voxel map using a GPU to generate accurate maps. Experimental results show that our proposed method achieves the highest accuracy and recall rates, improving the accuracy and robustness of obstacle detection in off-road environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screening Attention Network for Urban Carbon Emission Prediction

MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screenin...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ben Wang Xizhong Qin Jiwei Qin Xiaoyu Zhang Haodong Ma Xinjiang Key Laboratory of Signal Detection and Processing College of Computer Science and Technology Xinjiang University Ürümqi China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time series analysis to predict urban carbon emission, but there is a strong spatial correlation between the carbon emission data of several cities. Therefore, this paper designs a multi-scale spatial-temporal feature screening attention network to predict target cities' future carbon emission data. Firstly, this paper combines the daily carbon emission data of the near-neighbouring cities and the daily homologous emission data of the target city to analyze the urban carbon emission data from a spatio-temporal perspective. Then, this paper designs a multi-scale spatial interactive convolution module and a multi-scale temporal convolution module to extract multi-scale spatio-temporal features effectively. In addition, the feature screening module is designed to reduce the adverse effects of redundant features. Finally, the multi-scale features are used to predict the future carbon emissions of the target city through a predictor. The experimental results show that our prediction model is superior to the existing methods in six datasets.

关键词： Correlation Convolution Urban areas Time series analysis Energy conservation Carbon dioxide Predictive models Feature extraction Cybernetics

来源：评论

学校读者我要写书评

暂无评论

Target Speaker Extraction with Attention Enhancement and Gated Fusion Mechanism

Target Speaker Extraction with Attention Enhancement and Gat...

引用

Asia-Pacific signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Wang Sijie Askar Hamdulla Mijit Ablimit School of Information Science and Engineering Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Urumqi China

The objective of a target speaker extraction system is to extract the speech of the target speaker from a mixture of multiple speakers and noises using a certain amount of additional information of the target speaker. In this paper, we investigate the improvements of the baseline system by incorporating the light-weight CBAM module in the target extractor, and the gated fusion module (GFM) in the fusion layer. The CBAM introduces attention enhancement to baseline model with no significant increase in the number of parameters and complexity, and the previous concatenation-based fusion method used for speaker embedding and input mixture (or intermediate output) is replaced by GFM, enabling the model to better leverage the supplementary information provided by speaker embedding. Experimental results on datasets built from WSJ0-2mix and WHAM! demonstrate that both the CBAM module and the light-weight GFM module individually improve the model performance, and the GFM module shows better improvement on WHAM!. However, the combination of these two modules only exhibits mutually beneficial effects on the clean dataset WSJ0-2mix, while the performance of the combined module on the noisy dataset WHAM! is inferior to that of using the GFM module alone.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Relational Classification Model Fusing Entity and Sentence Semantic Information

Enhancing Relational Classification Model Fusing Entity and ...

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Chunji Wei Xizhong Qin Taiping Yuan School of Information Science and Engineering Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

Relationship classification aims at mining the relationship between two entities in a sentence and is an essential basis for the information extraction task. However, traditional relationship classification methods, which only use the information of words in a sequence to predict the relationship between entities, cannot use the semantic information of entities and sentences for relationship classification. To solve these problems, this paper proposes a new model, ES-BERT: this model uses the word-to-word association in the BERT model to obtain the semantic information of entities by the positional encoding of entity pairs in a sentence. In addition, we use the rich semantic knowledge in the internal hidden layer of Bert to obtain the semantic information of the sentence, which can better help the model understand the sentence. Finally, the semantic information of entities and the semantic information of sentences in the hidden layer of BERT are fused, and the fused information is used for relationship classification. The experimental results show that the model proposed in this paper achieves an excellent F1 value of 88.87% on the SemEva1-2010 Task 8 dataset, which is better than other Bert-based Sota models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MUNet++: Multilevel wavelet nested UNet++ demoiréing residual network

引用

Displays 2024年 83卷

作者： Gao, Guxue Lai, Huicheng Jia, Zhenhong The College of Computer Science and Technology Xinjiang University Urumqi830017 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830017 China

When mobile phones and digital cameras are used to capture information on a screen or store scenes with rich textures, an unpleasant moiré phenomenon occurs, which seriously degrades the quality of the image and affects subsequent image processing tasks. Therefore, to remove the moiré patterns, we propose a multilevel wavelet nested UNet++ demoiréing residual network called MUNet++. Unlike these methods based on the RGB domain, our method is performed in the frequency domain. The multilevel discrete wavelet transform (DWT) and the inverse discrete wavelet transform (IDWT) are elegantly embedded into the UNet++ encoder-decoder structure and transformed to the multilevel frequency domain, which captures more moiré features and preserves details of the image. In addition, we design a dual branch efficient pixel fusion attention module (DBEPFAM) that combines multiscale information, channel attention and the proposed pixel attention fusion module (PAFM) to improve the representation of features. In the network layer, a simple nonlinear unit (NU) is designed and embedded in the middle of the network layer to improve the representation capability of the network. Experimental results on two public datasets show that MUNet++ can effectively remove the moiré patterns and outperform existing state-of-the-art techniques. © 2024

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

A Joint Network Based on Interactive Attention for Speech Emotion Recognition

A Joint Network Based on Interactive Attention for Speech Em...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Ying Hu Shijing Hou Huamin Yang Hao Huang Liang He Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China

Speech emotion recognition (SER) has played a vital role in human-machine interaction. In this paper, we propose a separate spectrum-based SER model and a joint network combining pre-trained and spectrum-based models. In the joint network, we design an interactive attention module to effectively fuse the intermediate features from two models. Our proposed separate spectrum-based model is superior to four compared spectrum-based methods under the speaker-dependent setting. For the application in real scenarios, we compared our proposed joint network with six methods utilizing the pre-trained model under the speaker-independent setting. Experimental results show that our proposed joint network achieves the best performance among four unimodal models on the unweighted accuracy (UA) of 73.32 % and weighted accuracy (WA) of 72.48 %, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SAMNER: Image Screening and Cross-Modal Alignment Networks for Multimodal Named Entity Recognition

SAMNER: Image Screening and Cross-Modal Alignment Networks f...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Luyi Yang School of Computer Science and Technology Xinjiang University Xinjiang China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the proliferation of social media data, Multimodal Named Entity Recognition (MNER) has received much attention; using different data modalities is crucial for the development of natural language processing and neural networks. However, existing methods suffer from two drawbacks: 1) text-image pairs in the data only sometimes correspond to each other, and it is impossible to rely on contextual information due to the short text nature of social media. 2) Despite the introduction of visual information, heterogeneity gaps may occur in previous complex fusion methods, leading to misidentification. This paper proposes a new synthetic image with a selected graphic alignment network(SAMNER) to address these challenges and construct a matching relationship between external images and text. To solve the graphic mismatch problem, we use a stable diffusion model to generate the images and perform entity labeling. Specifically, we generate images and perform entity labeling through the stable diffusion model to generate the image with the highest match to the text, filter the generated images by the internal image set to generate the best image, and then perform multimodal fusion to predict the entity labeling, we design a simple and effective multimodal attentional alignment mechanism to obtain a better visual representation, and we conduct a large number of experiments. The experiments prove that our model produces competitive results on the two publicly available datasets.

关键词： Visualization Matched filters Image recognition Social networking (online) Text recognition Neural networks Named entity recognition

来源：评论

学校读者我要写书评

暂无评论

Joint Image Restoration For Domain Adaptive Object detection In Foggy Weather Condition

Joint Image Restoration For Domain Adaptive Object Detection...

引用

IEEE International Conference on Image processing

作者： Jing Ma Meng Lin Gang Zhou Zhenhong Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Driven by deep learning, object detection methods have made significant progress in recent years. However, there is still a domain shift between synthetic foggy data and real foggy data, this leads to a undesirable decrease in detection results when applying the algorithm model trained on synthetic foggy datasets. In this article, we design a domain-adaptive YOLOX object detection algorithm by joint image restoration, in order to improve object detection performance in foggy scenes. Specially, we design an end-to-end domain adaptive framework that combines dehazing module and YOLOX together. To achieve feature alignment, we introduced a domain classifier in the feature spaceand discuss its optimal placement in the framework. Experimental results show that the proposed domain adaptation method achieved 62.60 percent mAP on real foggy image datasets RTTs, outperforming other state-of-the-art methods.

关键词： Deep learning Adaptation models Object detection Feature extraction Data models Image restoration Classification algorithms

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：