检索结果-内蒙古大学图书馆

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

作者： Peiliang, Zhang Ablimit, Mijit Yilahun, Hankiz Hamdulla, Askar Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi China Xinjiang University Xinjiang Key Laboratory of Multilingual Information Technology School of Information Science and Engineering Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Aiming at the disadvantage of static word vector model to the problem of polysemy of words, a model based on dynamic word vector and two-way GRU neural network combined with attention mechanism is proposed to recognize emotion of microblog short text comments, which is helpful to analyze and monitor social public opinions. First, the word vector is obtained through ERNIE pre-training model, and then transmitted to the bidirectional GRU neural network. Then, the keywords are weighted in combination with the Attention mechanism. Finally, the emotion is classified through the softmax layer. Experimental results show that the classification accuracy of the proposed model on the weibo_senti_100k data set is 96.51%. © 2022 IEEE.

关键词： Dynamics

来源：评论

学校读者我要写书评

暂无评论

Application of multi-classification method of skin cancer based on dermoscopic image 21

Application of multi-classification method of skin cancer ba...

引用

2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021

作者： Chen, Qian Li, Min Chen, Cheng Chen, Chen Lv, Xiaoyi College of Software Key Laboratory of Software Engineering Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China College of Information Science and Engineering Xinjiang University Urumqi China

ISBN: (纸本)9781450395588

As a kind of cancer with high incidence, skin cancer seriously threatens people's life and health. Early detection, early diagnosis, and early treatment are one of the effective ways to increase the survival rate of patients with skin diseases. Therefore, this paper used the ResNet50 model as a feature extractor based on deep learning, and combined machine learning algorithms to explore the heterogeneity among multi-type of skin cancer, and tried to construct the best performance computer-aided diagnosis model. The research first used preprocessing techniques such as hair noise removal and data enhancement, and then achieved a classification accuracy of 90.497% on the public data set of ISIC2019. The proposed model effectively relieves the huge work pressure of dermatologists, and provides a reference for the better use of dermoscopic images for intelligent diagnosis and classification of skin cancer. © 2021 ACM.

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Target detection algorithm with wide field of view in foggy weather based on improved CSLBP operator 4

Target detection algorithm with wide field of view in foggy ...

引用

4th International Conference on Image processing and Intelligent Control, IPIC 2024

作者： Lian, Xiya Jia, Zhenhong Zhou, Gang Wang, Jiajia Song, Sensen Xiao, Xiaohui Shi, Fei College of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510682313

In order to solve the problem of unclear foreground targets and false foregrounds after performing target detection tasks in foggy video images with wide fields of view, we propose an improved video target detection algorithm for the CSLBP operator to detect foreground targets in different scenes. The improved CSLBP operator is used to extract the texture features of the wide-field image, and the foreground target pixels are judged combined with background modeling. This method is not sensitive to changes in indoor lighting or outdoor sunlight, can effectively solve the shadow problem, and more completely extracts foreground moving targets. It also has obvious effects in bad weather such as foggy weather, and has good robustness. © 2024 SPIE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Exploration on the mixed practice teaching methods of team experience under the training of new engineering talents

Exploration on the mixed practice teaching methods of team e...

引用

2020 International Conference on Information Science and Education, ICISE-IE 2020

作者： Fei, Shi Hong, Jia Zhen Yun, Zhou Yan Juan, Chen Xinjiang University School of Information Science and Engineering Key Laboratory of Signal Detection and Processing No.666 Shengli Road Tianshan District Urumqi Xinjiang China

ISBN: (纸本)9781665422611

Facing the requirements of the first-class discipline of electronic information engineering and the construction of new engineering courses, this paper analyzes the characteristics of practice teaching under the background of new engineering, puts forward a team experience hybrid practical teaching method for new engineering talents training, expounds the implementation process of this method in detail, and applies the method to the practice project design course of electronic information engineering experimental class. The results show that this method can improve students' self-learning ability, knowledge application ability, problem-solving ability, and engineering practice ability. © 2020 IEEE.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

BD-YOLO: Optimising Insect Imbalance Data detection Models

BD-YOLO: Optimising Insect Imbalance Data Detection Models

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Jian-Yi Jia, Zhen-Hong Zhou, Gang Wang, Jia-Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665410205

Vision-based insect detection is an important task in smart agriculture. Field-collected insect image datasets usually show severe imbalances in categories. Previous models have insufficient accuracy as well as low real-time performance for detecting unbalanced data of insects. To address the issues described above, an improved framework Balance Detect YOLO(BD-YOLO) based on YOLOv8 is proposed in this paper. We innovatively designed Focaler-CPIoU loss to increase the weight of a few classes of samples to achieve the optimisation of pest imbalance data detection. We introduced lightweight convolutional MBConv to improve the recognition accuracy while reducing the number of model parameters. In order to verify the effectiveness of the method in this paper, we established an insect dataset containing six types of insects, in which the most numerous thrips is 431.5 times more than the least numerous chrysopaperla. The experimental results show that compared with YOLOv8, the mAP50 reaches 61.7% with a 73.1% reduction in the number of model parameters, an improvement of 5.4%, and an improvement of 769 in FPS. A feasible approach to optimise the detection of unbalanced number of insects is provided in this paper along with the ability to be deployed on lightweight hardware devices. © 2024 IEEE.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

Prosody Prediction with Discriminative Representation Method 3

Prosody Prediction with Discriminative Representation Method

引用

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

作者： Zhang, Jipeng Yilahun, Hankiz Feng, Xiaoqin Chen, Yunlin Yang, Xipeng Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing China Xinjiang Key Laboratory of Multilingual Information Technology China Mobvoi Ai Lab Suzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Rhythm affects the naturalness and intelligibility of Text-To-Speech (TTS). However, rhythm prediction remains a great challenge, usually in two aspects: 1) the united annotation is a relatively difficult task, which depends on expert's experience. 2) traditional methods based on conditional random field (CRF), which heavily rely on feature engineering, such as word segmentation, part of speech(pos) etc. For above problems, we propose a method to reduce the dependency for united annotation data and conduct the joint experiment which use one unified model on independent data. Meanwhile, we also propose an algorithm of Layer Look Up Table (LLUT): use an embedding layer to learn a discriminative representation for different level of prosody data without any feature engineering. By using this method, the classifier can share the parameters and predict for different prosody level separately, which reduces the number of trainable model parameters. In order to better represent the input text, we use the pre-training model, like BERT, to provide the semantic information. Our experiment shows that the method of LLUT, is better able to acquire the discriminative meaning of different prosody levels. And also, our algorithm is proved to be general for sequence annotation tasks thus we can do extra task, like polyphone-prosody prediction. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Recommendation Algorithm with Co-embedded Item Attributes and Ratings 4

A Hybrid Recommendation Algorithm with Co-embedded Item Attr...

引用

4th International Conference on Applied Machine Learning, ICAML 2022

作者： Xin, Gaowei Qin, Jiwei Zheng, Jiong School of Information Science and Engineering Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665462655

In today's recommendation algorithm field, attribute feature information of users or items is usually introduced as auxiliary information to alleviate the sparsity problem, which can achieve good recommendation results to some extent. To make better use of the auxiliary information for data compression and dimensionality reduction, autoencoder models are usually employed. However, existing autoencoder-based models require the same input and output dimensions, resulting in data information loss and poor autoencoder scalability, which ultimately leads to low recommendation line performance. To cope with this problem, we propose a hybrid collaborative recommendation algorithm with co-embedded item attributes and ratings (HCR-item). By one-hot encoding the discrete item attribute features and then fusing the rating features with the item attribute features as the input of the autoencoder, the autoencoder completes the data compression and reconstructs the rating interaction prediction matrix. The algorithm proposed in this paper integrates item attribute features and rating features together, flexibly uses different feature information, extends the feature space, and solves the problem of low recommendation performance caused by sparsity. The algorithm has been extensively experimented on several real datasets and compared with other algorithms, and the results show that HCR-item has better recommendation performance. © 2022 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Low-Resource Speech keyword Search Based on Residual Neural Network 16th

Low-Resource Speech Keyword Search Based on Residual Neural ...

引用

16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Wang, Dafei Huang, Zhihua Li, Hui Liu, Wenchen School of Information Science and Engineering Xinjiang University Urumqi830017 China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830017 China

ISBN: (纸本)9783031202322

Low-resource speech keyword search is usually based on DTW template matching. However, this method has low precision and is sensitive to speaker or recording environment changes. This paper proposes a low-resource speech keyword search method based on image recognition. Bottleneck features are extract through a pre-trained model based on multi-language to generate similarity matrix images. ResNet is used to classify similarity matrix images. According to the image classification result, it is determined whether the speech keyword appears. This model does not require prior language knowledge and abundant corpus resources, so it is suitable for low-resource speech keyword search. We validate the performance of the model on different datasets. On SWS 2013, the model recall, accuracy, and F1 increased by 20.83%, 17.59%, and 19.18%, respectively. On QUESST 2014, they increased by 24.44%, 24.96%, and 24.70%, respectively. Experimental results show that the propose method is effective. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Template matching

来源：评论

学校读者我要写书评

暂无评论

Single-Channel Speech Separation Based on Non-negative Matrix Factorization and Factorial Conditional Random Field

引用

Chinese Journal of Electronics 2018年第5期27卷 1063-1070页

作者： LI Xu TU Ming WANG Xiaofei WU Chao FU Qiang YAN Yonghong Key Laboratory of Speech Acoustics and Content Understanding Institute of AcousticsChinese Academy of Sciences Signal Analysis Representation and Perception Laboratory Arizona State University Xinjiang Laboratory of Minority Speech and Language Information Processing

A new Non-negative matrix factorization(NMF) based algorithm is proposed for single-channel speech separation with a prior known speakers, which aims to better model the spectral structure and temporal continuity of speech signal. First, NMF and k-means clustering are employed to obtain multiple small dictionaries as well as a state sequence that describes the temporal dynamics between these dictionaries for each ***, a Factorial conditional random field(FCRF) model is trained using the state sequences and dictionaries to jointly model the temporal continuity of two speakers' mixed signal for separation. Experiments show that the proposed algorithm outperforms the baselines with respect to all metrics, for example sparse NMF(+1.12 dB SDR, +2.37 dB SIR, +0.40 dB SAR, +0.2 MOS), nonnegative factorial hidden Markov model(+2.04 dB SDR,+4.26 dB SIR, +0.62 dB SAR, +1.0 MOS) and standard NMF(+2.8 dB SDR, +5.08 dB SIR, +1.06 dB SAR, +1.2 MOS).

关键词： Single-channel speech separation Non-negative matrix factorization Factorial conditional random field k-means clustering

来源：评论

学校读者我要写书评

暂无评论

Unsupervised wide field-of-view video image change detection

Unsupervised wide field-of-view video image change detection

引用

2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Liu, Rui Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

Video surveillance requires simultaneous monitoring of multiple areas. Consequently, real-time automatic change detection of the monitored areas becomes very important. In the context of wide field-of-view conditions, the combination of a wide field-of-view, intricate environmental factors, and a substantial presence of random noise can lead to the degradation of visual fidelity and a diminished signal-to-noise ratio in the video images acquired through the image sensor. As a consequence, the task of detecting subtle changes becomes challenging for the surveillance system. To address the above problems, we have proposed a change detection method that leverages improved difference images and super fast and robust fuzzy c-means with constraints clustering. Initially, we employ an improved log-ratio operator and an improved mean-ratio operator to generate two distinct difference images. Subsequently, the wavelet fusion algorithm is applied to merge these two difference images, effectively integrating their distinctive features and producing a fused difference image with differentiability. Then, the new difference image is subjected to soft threshold primary classification and a cumulative distribution function normalization to obtain the difference image after primary classification. Finally, the super fast and robust fuzzy c-means with constraints clustering algorithm is employed for the ultimate classification, enabling the separation of changed and unchanged areas within the image. © 2024 SPIE.

关键词： Clustering 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：