检索结果-内蒙古大学图书馆

2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Yang, Wenjin Yu, Peng Cai, Xiaoying Zhong, Xiaojing He, Yuanrong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources P.R. China Beijing100081 China College of Harbour and Coastal Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781510668898

This study provides a random forest (RF) based method to retrieve high wind speeds, which takes advantage of RF's powerful nonlinear fitting ability and prevents overfitting. The high wind speed data from the Stepping Frequency Microwave Radiometer (SFMR) are used as the reference data. Compared with two traditional GMF models, the C-band cross-polarized ocean model (C-2PO) and the C-band cross-polarized coupled parameter ocean model (C-3PO), the wind speed inversion results using the RF method have been greatly improved. © 2023 SPIE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Msqat: A Multi-Dimension Non-Intrusive Speech Quality Assessment Transformer Utilizing Self-Supervised Representations

SSRN

引用

SSRN 2023年

作者： Shen, Kailai Yan, Diqun Dong, Li Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Convolutional neural networks (CNNs) have been widely utilized as the main building block for many \textit{non-intrusive} speech quality assessment (NISQA) methods. A new trend is to add a self-attention mechanism based on CNN to better capture long-term global content. However, it is not clear whether the pure attention-based network is sufficient to obtain good performance in NISQA. To this end, a framework named \textbf{M}ulti-dimension \textit{non-intrusive} \textbf{S}peech \textbf{Q}uality \textbf{A}ssessment \textbf{T}ransformer (MSQAT) is proposed. To strengthen the interactions of various speech regions between local and global, three multi-dimensional modules are devised. Additionally, speech quality varies not only in different frames, but also in different frequencies. Thus, a two-branch structure is designed to better evaluate the quality of speech. Experimental results demonstrate that the proposed MSQAT has state-of-the-art performance on three standard datasets (NISQA Corpus, Tencent Corpus, and PSTN Corpus). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Laser tracking leader-follower automatic cooperative navigation system for UAVs

引用

International Journal of Agricultural and Biological Engineering 2022年第2期15卷 165-176页

作者： Rui Ming Zhiyan Zhou Zichen Lyu Xiwen Luo Le Zi Cancan Song Yu Zang Wei Liu Rui Jiang College of Engineering South China Agricultural University/Guangdong Laboratory for Lingnan Modern AgricultureGuangzhou 510642China College of Computer and Control Engineering Minjiang University/Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhou 350108China Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence(GDKL-AAI) Guangzhou 510642China Guangdong Engineering Research Center for Agricultural Aviation Application(ERCAAA) Guangzhou 510642China Key Laboratory of Key Technology on Agricultural Machine and Equipment South China Agricultural UniversityMinistry of Education of P.R.ChinaGuangzhou 510642China

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale *** is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative *** study proposed a laser tracking leader-follower automatic cooperative navigation system for *** leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking *** on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was *** with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor *** navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control *** error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction *** image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting *** laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman *** outdoor flight experiments of two UAVs were carried out outdoors using this *** experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is *** average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is *** study demonstrated the possibility

关键词： two-UAVs cooperative visual navigation laser tracking

来源：评论

学校读者我要写书评

暂无评论

Non-Intrusive Speech Quality Assessment: A Survey

SSRN

引用

SSRN 2023年

作者： Shen, Kailai Yan, Diqun Hu, Jing Ye, Zhe Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Speech quality is a critical consideration for applications such as speech enhancement, coding, transmission, and synthesis. Accurately evaluating the quality of degraded speech without a reference is particularly challenging. As a result, non-intrusive speech quality assessment has been extensively researched due to its efficiency and ease of deployment. This survey comprehensively reviews these studies. Commonly used speech quality assessment databases are reviewed, as they serve as validation sets for objective metrics. Non-intrusive speech quality assessment methods are categorized and reviewed, and the performance of state-of-the-art and classical speech quality methods is compared. The survey presents an overview of classic algorithms and recent advancements in the field of non-intrusive speech quality assessment. © 2023, The Authors. All rights reserved.

关键词： Speech enhancement

来源：评论

学校读者我要写书评

暂无评论

A Method for Classification and Evaluation of Pilot’s Mental States Based on CNN

引用

computer Systems Science & Engineering 2023年第8期46卷 1999-2020页

作者： Qianlei Wang Zaijun Wang Renhe Xiong Xingbin Liao Xiaojun Tan Chengdu Institute of Computer Application Chinese Academy of SciencesChengdu610041SichuanChina School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of ChinaGuanghan618307SichuanChina School of Air Traffic Management Civil Aviation Flight University of ChinaGuanghan618307SichuanChina Neurology Department Deyang Second People’s HospitalGuanghan618307SichuanChina

How to accurately recognize the mental state of pilots is a focus in civil aviation *** mental state of pilots is closely related to their cognitive ability in *** the cognitive ability meets the standard is related to flight ***,the pilot’s working state is unique,which increases the difficulty of analyzing the pilot’s mental *** this work,we proposed a Convolutional Neural Network(CNN)that merges attention to classify the mental state of pilots through electroencephalography(EEG).Considering the individual differences in EEG,semi-supervised learning based on improvedK-Means is used in themodel training to improve the generalization ability of the *** collected the EEG data of 12 pilot trainees during the simulated flight and compared the method in this paper with other methods on this *** method in this paper achieved an accuracy of 86.29%,which is better than 4D-aNN and HCNN *** emotion will increase the probability of fatigue appearing,and emotion recognition is also meaningful during the *** we conducted experiments on the public dataset SEED,and our method achieved an accuracy of 93.68%.In addition,we combine multiple parameters to evaluate the results of the classification network on a more detailed level and propose a corresponding scoring mechanism to display the mental state of the pilots directly.

关键词： Pilot mental state EEG attention CNN semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Bidirectional Weighted Co-Association-Based Clustering Ensemble Algorithm for High-Speed Train Delay Prediction 29th

Bidirectional Weighted Co-Association-Based Clustering Ensem...

引用

29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Zhao, Yi Bai, Muchen Ni, Panpan Jiang, Yunan Feng, Xuan Zhang, Xiaobo School of Design SouthWest Jiaotong University Chengdu611756 China School of Computing and Artificial Intelligence SouthWest Jiaotong University Chengdu611756 China Sichuan Institute of Computer Sciences Chengdu610042 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9783031425141

In recent years, high-speed trains are one of the popular research topics in the field of transportation and have attracted much attention from scholars at home and abroad in view of their high efficiency and safety features. High-speed trains are susceptible to unexpected situations of delayed arrival due to the disturbance of external factors including wind and temperature during the trip. The delay of high-speed trains has a vital impact on people's work efficiency and living standards. To solve the problem of intelligent forecast of high-speed train delay, we design and implement a clustering ensemble algorithm based on text data of bullet train operation environment monitoring. We firstly combine the concepts of cosine similarity, Jaccard similarity and information entropy, then fuse a bidirectional weighting formula, and further propose a novel Clustering Ensemble algorithm based on Bidirectional Weighted Co-association (CE-BWC). This approach consists of an inter-cluster weighting formula used to obtain the implicit generalized information by weighting each cluster between clusters, and a reliability weighting formula transferring the cluster information to the data item itself to measure the correlation degree of the data group. Experiments prove that our CE-BWC has better prediction performance for high-speed train delay and outperforms the state-of-the-art benchmark algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Fast Crop Pest Detection Using Lightweight Feature Extraction and Knowledge Distillation

Fast Crop Pest Detection Using Lightweight Feature Extractio...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ze Yang Xianliang Jiang Guang Jin Junkai Huang Jie Bai Dingxin Yu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Pest detection is critical for achieving effective pest control. However, the current deep learning-based pest detection algorithm is unsuitable for deployment on resource-limited edge devices due to its extensive computation and long inference time. Although lightweight models have been widely used for practical detection, their insufficient feature extraction capability leads to a decline in detection accuracy. This paper proposes a fast algorithm for crop pest detection based on lightweight feature extraction and knowledge distillation. Firstly, we introduce partial convolution and propose a lightweight feature extraction module, C3Faster, which reduces the model's computation and speeds up model inference while ensuring effective feature extraction. Secondly, we use knowledge distillation to improve the model's detection accuracy by using teacher networks to assist in training. Finally, we created a dataset, CropPest6, consisting of six crop pest categories and conducted experiments. The experimental results demonstrate that our method reduces the detection time, number of parameters, and computation by 17%, 38%, and 44%, respectively, compared to the baseline model. Furthermore, our method achieves 93.9% Precision, 93.6% Recall, and 97.5% mean Average Precision (mAP), demonstrating its practical suitability for fast crop pest detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

MIANet: Aggregating Unbiased Instance and General Informatio...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yong Yang Qiong Chen Yuan Feng Tianlin Huang School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5 i and COCO-20 i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning 38

HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder fo...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bai, Lu Xu, Zhuo Cui, Lixin Li, Ming Wang, Yue Hancock, Edwin R. School of Artificial Intelligence Beijing Normal University Beijing100875 China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing100875 China School of Information Central University of Finance and Economics Beijing100081 China Zhejiang Institute of Optoelectronicss Jinhua321004 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China Department of Computer Science University of York YorkYO10 5GH United Kingdom

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

RDGFuzz: A directed greybox fuzzing optimization method based on Rich-Branch nodes 22

RDGFuzz: A directed greybox fuzzing optimization method base...

引用

Proceedings of the 2022 6th International Conference on Electronic Information technology and computer Engineering

作者： Zejun Wu Li Lu Qiong Jia Zhihao Chen State Key Laboratory of Mathematical Engineering and advanced Computing China National Local Joint Engineering Laboratory of Network Space Security Technology China Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9781450397148

Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, such as unreasonable seed energy allocation, low code coverage and incomplete testing. To solve the above problems, this paper proposes an optimization method of directed fuzzing based on Rich-Branch nodes. In this method, the concept of Rich-Branch nodes is defined and the algorithm of extracting Rich-Branch nodes is given. The optimization method collects the coverage information of the target program in the running process, calculates the weights of covered functions and nodes in real time by combining CG and CFG of the target program, and generates a list of Rich-Branch nodes. According to the weights of Rich-Branch nodes, the seed energy allocation algorithm of AFLGo is optimized and improved. Compared with AFLGo, this optimization method improves the average code coverage of each targeted point by 56.79%, and has the same target reaching ability as AFLGo.

关键词： Rich-Branch nodes Directed fuzzing Code coverage rate Vulnerability discovery

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：