检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Silvestri, Matteo Gabrielli, Edoardo Silvestri, Fabrizio Tolomei, Gabriele Department of Computer Science Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

We tackle the problem of community membership hiding, which involves strategically altering a network’s structure to obscure a target node’s membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained optimization task. To solve this, we propose ∇- CMH, a gradient-based method that modifies the network’s structure within the feasible bounds for an individual target node, effectively concealing its membership. Experimental results across multiple datasets and community detection algorithms show that our approach surpasses existing baselines, offering a better balance between accuracy and computational efficiency. © 2025, CC BY.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Error Dependent Sampling to Reduce Transient Events in Event Based control

Error Dependent Sampling to Reduce Transient Events in Event...

引用

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Oscar Miguel-Escrig Julio-Ariel Romero-Pérez José Sánchez-Moreno Sebastián Dormido-Bencomo Department of System Engineering and Design Universitat Jaume I Castelló de la Plana Spain Department of Computer Science and Automatic Control Universidad Nacional de Educación a Distancia (UNED) Madrid Spain

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

One of the objectives of event-based control is the reduction of generated events to perform an appropriate process control. Among the event generation techniques used to this end, those quantifying the error signal can be found more frequently for its simplicity of implementation. However, they entail a drawback in the form of trade-off between event generation and performance. Aiming to reduce the number of events, specifically in the transient response, without degrading the performance, an error dependent sampling scheme is studied in this work.

关键词： Industrial electronics Transient response Event detection Process control Robustness Regulation Generators Transient analysis

来源：评论

学校读者我要写书评

暂无评论

Applying t-Distributed Stochastic Neighbor Embedding for Improving Fingerprinting-Based Localization System

IEEE Sensors Letters

引用

IEEE Sensors Letters 2023年第9期7卷 1页

作者： Tarekegn, Getaneh Berie Tai, Li-Chia Lin, Hsin-Piao Tesfaw, Belayneh Abebe Juang, Rong-Terng Hsu, Huan-Chia Huang, Kai-Lun Singh, Kanishk National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30010 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan Feng Chia University Department of Electronic Engineering Taichung40724 Taiwan Industrial Technology Research Institute Information and Communications Research Laboratories Hsinchu310401 Taiwan

Large-scale location estimation is crucial for many artificial intelligence Internet of Things (IoT) applications in the era of smart cities. This letter proposes a deep learning-based outdoor positioning scheme for large-scale wireless settings using fingerprinting techniques. We first developed a feature extraction technique using t-distributed stochastic neighbor embedding (t-SNE) to extract dominant and distinguishable features while eliminating noises from the radio fingerprints. Afterward, we developed a deep learning-based coarse-fine localization framework to improve positioning performance significantly. Based on our numerical analysis, the proposed scheme reduces computation time by 64.41%, and the average positioning error is 38 cm. Therefore, the proposed approach significantly improved positioning accuracy and reduced computation time. © 2017 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Target Localization of Short Vertical Line Array Based on Least Squares Method

Target Localization of Short Vertical Line Array Based on Le...

引用

International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Shaochun Qu Zhipeng Cai Jing Ma School of Computer Science and Engineering Xi’an Technological University Xi’an China North Automatic Control Technology Institute Taiyuan China

In the shallow sea environment, array of small array elements may not be able to accurately locate targets due to undersampling. To address this issue, the paper proposed a method called sound field extension based on the reception of array of small array elements, which generates a virtual measurement field of a vertical array with lager number. The method utilizes the extended acoustic field for VTRM. This approach significantly improves the focusing and positioning performance of the TRM. The key implementation lies in estimating the normal wave mode coefficients from the acoustic field acquired by the short vertical line array. The paper uses the least squares method to estimate the mode coefficients. Experimental results demonstrate that compared to conventional time processing methods for an array of small array elements, the VTRM method using the a vertical array with a large number of elements can effectively enhance targeting and focusing performance of the short array. For array of small array elements with 21 elements, the SNR (Signal Interference to Noise Ratio) effectively improves by 4.32 dB.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Remaining Useful Life Prediction of Aircraft Engines based on FA-SDAE-BiGRU

Remaining Useful Life Prediction of Aircraft Engines based o...

引用

International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Xiaojun Bai Yunpeng Sun Gong Meng School of Computer Science and Engineering Xi’an Technological University Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Aiming at the problems of insufficient feature extraction and inaccurate prediction in existing aircraft engines remaining useful life prediction algorithms, a fusion model based on feature attention mechanism, stacked noise reduction autoencoder and bidirectional gated cycle units (BiGRU) was proposed. Firstly, weight is assigned to each sensor data of aircraft engine by the feature attention mechanism, and new weight data is obtained. Next, the stack noise reduction autoencoder is used to extract the weighted data. Then, bidirectional gated recurrent units are employed to predict the time series data and obtain the remaining useful life of the aircraft engine. To accurately select the hyperparameters of BiGRU, the Bayesian optimization algorithm is utilized for hyperparameter optimization. Finally, ablation experiments were conducted on the CMPASS dataset to validate the effectiveness of the proposed method, resulting in a root mean square error and score of 13.51 and 252.61, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hard Disk Failure Prediction Based on Association Analysis and Long Short-Term Memory Network

SSRN

引用

SSRN 2023年

作者： Xiaojun, Bai Pan, Zhaofeng Meng, Gong Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature;secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Stress Matrix-Based Formation control of Multi-Agent Systems with Discrete-Time Communication

Stress Matrix-Based Formation Control of Multi-Agent Systems...

引用

International Conference on Systems and control (ICSC)

作者： Okechi Onuoha Brendan Chijioke Ubochi Suleiman Kurawa Zezhi Tang Changshun Wu Yi Dong School of Physics Engineering and Computer Science (SPECS) University of Hertfordshire UK The Department of Electrical and Electronic Engineering Federal University of Technology Akure Nigeria The Department of Electrical Engineering University of Manchester UK The Department of Automatic Control and Systems Engineering University of Sheffield UK University Grenoble Alpes Grenoble France The School of Electronics & Computer Science University of Southampton UK

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws.

关键词： Target tracking Simulation Accelerated aging control systems Formation control Stability analysis Stress Time-varying systems Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis Research of Laser Gyroscope Based on Optimized-Kernel Extreme Learning Machine

SSRN

引用

SSRN 2023年

作者： Xiaojun, Bai Ma, Zhenxi Chen, Wei Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Traditional diagnostic models for laser gyroscopes, widely utilized as high-precision angular velocity sensors in aerospace applications, often suffer from limited reliability and accuracy due to the difficulty of feature extraction, a high computational cost, and training time. In light of these challenges, the present study puts forth a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method leverages Wavelet Packet Decomposition (WPD) to effectively extract features from the laser gyroscope signal as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis, after which we employ the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for enhanced optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches, while also offering the advantages of a shorter training time and increased precision. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

Reliability Prediction Based on SVR and Improved SCSO

Reliability Prediction Based on SVR and Improved SCSO

引用

International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Xiaojun Bai Hongyue Liu Gong Meng School of Computer Science and Engineering Xi’an Technological University Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Reliability prediction can provide basis for the identification of potential improvement area, cost control, and mission reliability assessment, etc. However, for complex equipment, there are many reliability influencing factors and incomplete knowledge of failure causes, which lead to a significant disparity between the predicted outcomes and the real values for traditional reliability prediction methods. To address the above issues, this research paper introduces an approach that utilizes the Support Vector Regression (SVR) model and Sand Cat Swarm Optimization (SCSO). To begin with, the sliding window technique is employed on the historical reliability data to generate time series samples, with the 5 adjacent data as sample data, and the sixth as label of the sample, and train SVR model on these samples; Second, the SVR model parameters are optimized using the ISCSO algorithm to obtain the optimal combination of parameters. In the testing stage, firstly, historical reliability data was used to predicted future data by the model, and the predicted data are then added to the sequence to form new samples, thus old data are discarded and new data are predicted continuously to realize continuous reliability prediction. Finally, the algorithm proposed in this paper is validated on a diesel engine reliability dataset. The algorithm proposed in this paper demonstrates its superiority through the Normalized Root Mean Squared Error (NRMSE) evaluation. The NRMSE of SVR-ISCSO is 8.86E-05, showcasing a remarkable 99.24% year-on-year decrease compared to the standard SVR. Additionally, it exhibits a 5.86% year-on-year decrease compared to SVR-SCSO, further validating the effectiveness of the proposed algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Bicheng Guo Conghao Zhou Haoyu Liu Shibo He Jiming Chen Xuemin Sherman Shen College of Control Science and Engineering Zhejiang University China Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework.

关键词： Continuing education Training Deep learning Adaptation models Multi-access edge computing Computational modeling Data models Global communication Convergence

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：