检索结果-内蒙古大学图书馆

A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory

Mathematical Biosciences and Engineering 2024年第2期21卷 2626-2645页

作者： Hua, Mingfeng Xu, Taihua Yang, Xibei Chen, Jianjun Yang, Jie School of Computer Jiangsu University of Science and Technology Zhenjiang212100 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China

Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fundamental theory for taking RST approach to calculate SSSPs from weighted digraphs. Second, a heuristic search strategy was designed. The weights of edges can be served as heuristic information to optimize the search way of k-step R-related set, which is an RST operator. By using heuristic search strategy, some invalid searches can be avoided, thereby the efficiency of discovering SSSPs was promoted. Finally, the W3SP@R algorithm based on RST was presented to calculate SSSPs of weighted digraphs. Related experiments were implemented to verify the W3SP@R algorithm. The result exhibited that W3SP@R can precisely calculate SSSPs with competitive efficiency. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

A technique of internal relative transmission delay measurement and positioning error correction for GNSS pseudo-satellite systems 7

A technique of internal relative transmission delay measurem...

引用

7th IEEE Information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Liang, Yuexian Zhao, Li Cheng, Yao Li, Chunhui Guilin University of Electronic Technology School of Computer Science and Engineering Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin China

ISBN: (纸本)9798350334197

Internal transmission delay is one of the inherent system parameters of pseudo-satellite transmitters, and it is also one of the factors affecting the positioning service accuracy of pseudo-satellite systems. Aiming at the influence of the internal delay inconsistency deviation caused by the inconsistent components characteristics of different pseudo-satellites, a method of relative delay mensuration and compensating inside the pseudo-satellite transmitter is proposed. According to the structural characteristics of pseudo-satellite transmitter and receiver, a transmission delay measurement model is established, the total transmission delay of the pseudo-satellite transceiver system is measured by high-precision instruments, and the relative internal transmission delay between other pseudo-satellites is obtained by difference method based on one of the pseudo-satellites. Starting from the carrier phase observation equation, the positioning correction model based on single-difference carrier phase is established, and the measured value is converted into the delay compensation factor in the positioning algorithm model for positioning solution. Through the comparative analysis of the experimental results of the positioning solution before and after correction, the method can improve the positioning accuracy effectively, and the feasibility of the method is verified. © 2023 IEEE.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

Efficient energy-preserving methods for the Schrödinger-Boussinesq equation

Efficient energy-preserving methods for the Schrödinger-Bou...

引用

作者： Yan, Jinliang Zheng, Lianghong Lu, Fuqiang Zhang, Qiongyue Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry College of Mathematics and Computer Fujian354300 China Department of Information and Computer Technology No.1 Middle School of Nanping Fujian353000 China Changzhou Institute of Technology Changzhou213032 China

In this paper, we present two novel energy-preserving schemes for the Schrödinger-Boussinesq system based on the second-order Adams prediction-correction method. We first introduce supplementary variables to the initial model simultaneous its invariants to obtain a consistent and structural stable system. Then, we discretize the extended system by directly using the Fourier pseudo-spectral method in space and second-order Adams prediction-correction method in time to construct conservative schemes. Numerical results show that the proposed schemes can reach second-order accuracy and can conserve the discrete invariants very well. © 2022 John Wiley & Sons, Ltd.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing 35

Auto-PIP: Real-time Identification of Critical Performance I...

引用

35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Xiong, Xiao Li, Mengyao Sun, Yongqian Zhao, Yongxin Chen, Xia Deng, Bowen Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area"(SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research. © 2024 IEEE.

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism 35

Multivariate Time Series Anomaly Detection based on Pre-trai...

引用

35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Sun, Yongqian Guo, Yang Liang, Minghan Wen, Xidao Kuang, Junhua Zhang, Shenglin Li, Hongbo Xia, Kaixu Pei, Dan Nankai University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China BizSeer Haihe Laboratory of Information Technology Application Innovation China Tencent Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%. © 2024 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

A Knowledge-Injected Curriculum Pretraining Framework for Question Answering 24

A Knowledge-Injected Curriculum Pretraining Framework for Qu...

引用

33rd ACM Web Conference, WWW 2024

作者： Lin, Xin Su, Tianhuang Huang, Zhenya Xue, Shangzi Liu, Haifeng Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen China University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Knowledge-based question answering (KBQA) is a key task in natural language processing research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be available and restrict its application. Moreover, existing methods focus more on improving language understanding with KGs, while neglect the more important human-like complex reasoning. To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). Specifically, the KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps that could work with different implementations for flexible application. Next, the KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability to reduce the negative impacts of the difference between the generated and natural corpus. Last, to enable the LM with complex reasoning, the CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner to promote model learning. We provide an implementation of the general framework, and evaluate the proposed KICP on four real-word datasets. The results demonstrate that our framework can achieve higher performances, and have good generalization ability to other QA tasks. © 2024 ACM.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Metal surface Feature Fusion for Defect Recognition 3

Metal surface Feature Fusion for Defect Recognition

引用

3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Lyu, Wenkai Yang, Chengzhuan Zhejiang Normal University School of Computer Science and Technology Zhejiang China Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application Zhejiang Province Zhejiang China

ISBN: (纸本)9798350360813

Metal sheets have been widely used in various industrial production and production life aspects. However, the surface of metal plates is prone to multiple defects, which affect the electromagnetic characteristics and coating effect of metal plates. Currently, mainstream inspection uses YOLO, PaddleClas, and other methods to detect flat metal surfaces. These algorithms are all very potent in their classification capabilities, but as generalized defect handling algorithms, they cannot be specific to each type of defect in a real problem. In this paper, we propose a multi-information fusion method for defect recognition. We consider both low-level texture features and high-level convolutional features of metal surfaces for defect recognition. We use a convolutional neural network as the main body and add extracted and processed prompts during training. Finally, we test the proposed method extensively on Camshaft and Kaggle metal surface defects datasets, and the experimental results demonstrate that our method achieves a state-of-the-art recognition performance that surpasses previous defect recognition methods. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Specific Emitter Identification Based on ACGAN and STFT 7

Specific Emitter Identification Based on ACGAN and STFT

引用

7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Wang, Bin Xie, Jun Wang, Feng The 38th Research Institute Of China Electronics Technology Group Corporation China Key Laboratory Of Aperture Array And Space Application China Key Laboratory Of Intelligent Information Processing Anhui Hefei230088 China School Of Computer And Information Hefei University Of Technology Hefei230601 China

ISBN: (纸本)9798350361445

Deep learning methodologies are gaining popularity for enhancing the efficiency of Specific Emitter Identification (SEI). Nevertheless, the lack of labeled datasets continues to pose a challenge in most non-cooperative scenarios for SEI. Addressing this challenge, a framework for SEI is proposed, leveraging Short-Time Fourier Transform (STFT) in conjunction with auxiliary classifier GAN(ACGAN). The Short-Time Fourier Transform of emitters is utilized as radio-frequency fingerprint for the emitters. The input latent layer is infused with label information to steer the training process of the Generative Adversarial Network. Through semi-supervised training, shared weight updates are implemented for the classification network. Experimental results demonstrate that our model can achieve superior or competitive performances in several datasets compared with the former models. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

GM2RC: Graph-based Multitask Modality Refinement and Complement for Multimodal Sentiment Analysis 7

GM2RC: Graph-based Multitask Modality Refinement and Complem...

引用

7th International Symposium on Autonomous Systems, ISAS 2024

作者： Shi, Jiandong Chen, Yuting Zhou, Siwei Li, Ming Zhejiang Normal University Department of Computer Science Jinhua China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua China Zhejiang Institute of Optoelectronics Jinhua China

ISBN: (纸本)9798350363173

Multimodal sentiment analysis has emerged as a critical research area, aiming to analyze complex emotional states using data from multiple sources. While conventional approaches focus on sophisticated fusion techniques to integrate multimodal information, they often struggle with distributional variances among modalities. Bridging this modal distribution gap and optimizing the utilization of modality-sensitive information are crucial challenges. In response, our study presents GM2RC, an innovative framework designed to enhance modality representations for improved multimodal fusion efficacy. GM2RC adopts a two-step approach: intra-modal refinement and intermodal complementation. In the first step, it enhances distinct features within each modality, while the second step fosters the assimilation of shared information across modalities through pairwise learning, thus mitigating inter-modal disparity. This comprehensive approach leads to more accurate sentiment analysis by creating more robust multimodal representations. Further rigorous experiments demonstrate significant boosts of 1.59% in accuracy and 2.03% in w-F1 on the IEMOCAP dataset, and 1.65% in accuracy and 2.73% in w-F1 on the MELD dataset, showcasing the superiority of our proposed GM2RC model. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Research on Instance Segmentation of High-Resolution Remote Sensing Images 7

Research on Instance Segmentation of High-Resolution Remote ...

引用

7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhang, Xiaoying Yang, Houqun Li, Xuanyu Yang, Dangui Shen, Jie Xu, Gaodi Hainan University College of Computer Science And Technology Hainan Haikou China Hainan University Haikou Key Laboratory of Deep Learning And Big Data Application Technology Hainan Haikou China

ISBN: (纸本)9798350359145

Increasingly, high-resolution remote sensing images are being used in real life as a result of the continuous advancement of high-definition remote sensing imaging technology in recent years. They play a crucial role in urban planning, agriculture, oceans, ecological protection, and other fields. A growing amount of remote sensing image data has led to a growing interest in using image instance segmentation to decode remote sensing images by automatically extracting valuable information. The present paper examines and analyzes a large number of instance segmentation methods on remote sensing images. It begins with an explanation of the classification and research progress of natural image instance segmentation techniques, which are categorized into two-stage instance segmentation, single-stage instance segmentation, multi-stage instance segmentation and weakly supervised instance segmentation, and also analyzed in terms of their advantages and shortcomings. And then we discussed it in more detail of remote sensing image instance segmentation using deep learning, and it is explained that each of these methods can be classified into three types based on its technical characteristics: methods that combine multiscale feature fusion, methods that combine dilated convolution and methods that combine attention mechanisms, as well as the benefits and limitations of each method. The results of classical experiments on different datasets are presented, along with an explanation of the commonly used datasets and evaluation metrics in instance segmentation. This paper presents an overview of the current problems and challenges associated with remote sensing image instance segmentation, as well as future trends and developments. © 2023 IEEE.

关键词： Convolution

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：