检索结果-内蒙古大学图书馆

10th International Conference on Information Security Practice and Experience, ISPEC 2014

作者： Zhou, Hongwei Wu, Xin Shi, Wenchang Yuan, Jinhui Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Information Engineering University Zhengzhou China

ISBN: (纸本)9783319063195

Combining short instruction sequences originated only from existing code pieces, Return Oriented Programming (ROP) attacks can bypass the code-integrity effort model. To defeat this kind of attacks, current approaches check every instruction executed on a processor, which results in heavy performance overheads. In this paper, we propose an innovative approach, called HDROP, to detecting the attacks. It utilizes the observation that ROP attacks often make branch predictor in modern processors fail to determine the accurate branch destination. With the support of PMC (Performance Monitoring Counters) that is capable of counting performance events, we catch the abnormal increase in branch mis-prediction and detect the existence of ROP attacks. In HDROP, each basic unit being checked consists of hundreds of instructions rather than a single one, which effectively avoids significant performance overheads. The prototype system we developed on commodity hardware shows that HDROP succeeds in detecting ROP attacks, and the performance tests demonstrate that our approach has acceptably lower overheads. © 2014 Springer International Publishing.

关键词： Computers

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022

引用

International Journal of Digital Earth 2025年第1期18卷

作者： Wang, An Su, Hua Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou China Department of Geography Ghent University Ghent Belgium

Long time series and accurate subsurface temperature data in the global ocean are essential for ocean warming and climate change studies. The sparse in situ observations in the pre-Argo era hinder the reconstruction of long-time series observational data for the global ocean. This study proposes a novel Adaptive Spatio-TEmporal Neighbors with two-point differences (ASTEN) method for subsurface temperature reconstruction, which adaptively learns and adjusts spatio-temporal neighbors depending on the distribution of in situ observations to ensure robust gaps-filling performance across four dimensions. By integrating geoscience domain knowledge and utilizing spatiotemporal autocorrelation, ASTEN simultaneously learns the spatial pattern and temporal variation of subsurface temperature, and significantly enhances the interpretability and accuracy of ocean temperature reconstructions over a long time series compared to the DINCAE and DINEOF. The ASTEN reconstructed temperature data for the upper 1000 m from 1960 to 2022 can effectively track the ocean warming process for more than six decades. This study demonstrates the ASTEN method is well suited for subsurface temperature reconstruction, and holds great potential in the gaps-filling of sparse ocean observations with high missing rates over a large scale. The new reconstruction of subsurface temperature can effectively reduce the uncertainty of subsurface ocean warming analysis. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis

引用

IEEE Transactions on Instrumentation and Measurement 2025年

作者： Lin, Tantao Ren, Zhijun Zhu, Linbo Zhu, Yongsheng Feng, Ke Ding, Weiping Yan, Ke Beer, Michael Xi'an Jiaotong University Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an710049 China Xi'an Jiaotong University Frontier Institute of Science and Technology Xi'an710049 China Xi'an Jiaotong University School of School of Chemical Engineering and Technology Xi'an710049 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science Macau999078 China Leibniz University Institute for Risk and Reliability Hannover30167 Germany University of Liverpool Department of Civil and Environmental Engineering LiverpoolL69 3BX United Kingdom Tongji University International Joint Research Center for Resilient Infrastructure International Joint Research Center for Engineering Reliability and Stochastic Mechanics Shanghai200092 China

In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration. © 1963-2012 IEEE.

关键词： Information fusion

来源：评论

学校读者我要写书评

暂无评论

Regulation of morphology and visible light-driven photocatalysis of WO_(3)nanostructures by changing pH

引用

Rare Metals 2021年第7期40卷 1738-1745页

作者： You-Shu Fan Xiao-Li Xi Yang-Si Liu Zuo-Ren Nie Lin-Yan Zhao Qing-Hua Zhang College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

The controlled preparation of hexagonal tungsten trioxide(h-WO_(3))nanostructures was achieved by adjusting the pH of the precursor *** effect of the pH on the morphology,elemental composition,and photocatalytic performance of the samples was characterized via X-ray diffraction(XRD),scanning electron microscopy,energy dispersive X-ray spectroscopy,and Raman ***-visible(UV-Vis)spectra were used to evaluate the absorbance and the photocatalytic performance of methylene ***(PL),electrochemical impedance spectroscopy,photocurrent response and Brunauer-Emmett-Teller(BET)were used to study the optical properties,electrical performance,and specific surface area of the WO_(3)-nanostructures,*** results indicate that the WO_(3) nanorods prepared at pH=1.0 exhibit the highest photocatalytic performance(87.4%in 1 h),whereas the WO_(3) nanoblocks prepared at p H=3.0 show the *** photocatalytic performance of the one dimensional(1 D)-nanorods can be attributed to their high specific surface area and charge transfer *** h-WO_(3) nanostructures were synthesized via a simple method and without a capping *** show an excellent photocatalytic performance,which is promising for their application in environment purification.

关键词： h-WO_(3) Morphology pH Photocatalysis Charge transfer Optoelectrical properties

来源：评论

学校读者我要写书评

暂无评论

Learning dynamic bayesian network with immune evolutionary algorithm

Learning dynamic bayesian network with immune evolutionary a...

引用

International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Jia, Hai-Yang Liu, Da-You Yu, Peng Key Laboratory of Symbolic Computing and Knowledge Engineering Juin University Ministry of Education Changchun China

ISBN: (纸本)078039092X

Dynamic Bayesian Networks (DBNs) are directed graphical models of stochastic processes, How to learn the structure of DBNs from data is a hot problem of research. In this paper the author presents an Immune evolutionary algorithm for learning the network structure of DBNs. The results of contrast experiment prove that the constringency rate is more rapid than EGA-DBN algorithms. © 2005 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Applications of molten salt and progress of molten salt electrolysis in secondary metal resource recovery

引用

International Journal of Minerals,Metallurgy and Materials 2020年第12期27卷 1599-1617页

作者： Xiao-li Ming Feng Li-wen Zhang Zuo-ren Nie College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

Molten salt is an excellent medium for chemical reaction,energy transfer,and *** salt innovative technologies should be developed to recover metals from secondary resources and reserve metals from primary natural *** these technologies,molten salt electrolysis is an economic and environment-friendly method to extract metals from waste *** the perspective of molten salt characteristics,the application of molten salts in chemistry,electrochemistry,energy,and thermal storage should be comprehensively *** review discusses further directions for the research and development of molten salt electrolysis and their use for metal recovery from various metal wastes,such as magnet scrap,nuclear waste,and cemented carbide *** is placed on the development of various electrolysis methods for different metal containing wastes,overcoming some problems in electrolytes,electrodes,and electrolytic *** focus is given to future development directions for current associated processing obstacles.

关键词： molten salt application molten salt electrochemistry resource recycling cemented carbide scrap

来源：评论

学校读者我要写书评

暂无评论

A two-stage HV-driven adaptive multi-objective evolutionary algorithm and its application in fixed polarity reed-muller circuits

引用

Expert Systems with Applications 2025年 288卷

作者： Lu Yang Shengsheng Wang Ruyi Dong Zihao Fu College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China College of Information and Control Engineering Jilin Institute of Chemical Technology Changchun 130012 China

To achieve a balance between convergence and diversity, we proposed a two-stage HV-driven adaptive multi-objective evolutionary algorithm (TSAMEA). TSAMEA employs a sinusoidal decreasing parameter adjustment method to enhance exploration pace in the first stage. An adaptive parameter control mechanism utilizes historical memory pools and an HV-driven degree adjustment strategy to achieve better exploitation in the second stage. Extensive experimental data demonstrate that TSAMEA outperforms nine other compared MOEAs. The component analysis illustrates the efficacy of each component of TSAMEA. In addition, area and power optimization are now the main limitations in chip design, TSAMEA is applied to area and power optimization for Fixed Polarity Reed-Muller (FPRM) logic circuits and perform well, which further verifies the ability of the TSAMEA to solve practical problems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multiparticipant Federated Feature Selection Algorithm With Particle Swarm Optimization for Imbalanced data Under Privacy Protection

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1002-1016页

作者： Hu, Ying Zhang, Yong Gong, Dunwei Sun, Xiaoyan China University of Mining and Technology School of Information and Control Engineering Xuzhou221008 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China

In the real world, data describing the same learning task may be distributed in different institutions (called participants), and these participants cannot share their own data due to the need of privacy protection. How to select an optimal feature subset on the basis of ensuring the data privacy of each participant is an important and challenging topic. At the same time, data on part participants show imbalanced or even miss some classes, further increasing the difficulty of solving this kind of problem. In view of this, by introducing a trusted third party to process and integrate optimal information from participants, this article proposes a multiparticipant federated evolutionary feature selection algorithm for imbalanced data under privacy protection. First, a multilevel joint sample filling strategy with multiple participants (called sampling-rough selection-fine tuning strategy) is proposed to fill imbalanced or empty classes on each participant while ensuring data privacy. Then, a federated evolutionary feature selection method based on particle swarm optimization (PSO) is proposed by periodically sharing the optimal feature subsets obtained by PSOs on participants. Finally, the proposed algorithm is applied to 15 test datasets, and compared with several typical imbalanced feature selection algorithms and two kinds of ensemble feature selection algorithms. The experimental results show that the proposed algorithm can significantly promote the ability of each participant on processing imbalanced datasets, and improve the classification accuracy of obtained feature subsets while protecting the participants' data privacy. © 2020 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Adaptive debiasing learning for drug repositioning

引用

Journal of Biomedical Informatics 2025年 167卷 104843页

作者： Meng, Yajie Wang, Yi Hu, Xinrong Lu, Changcheng Tang, Xianfang Cui, Feifei Zeng, Pan Yao, Yuhua Yang, Jialiang Xu, Junlin School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan430200 China College of Computer Science and Electronic Engineering Hunan University Hunan Changsha401331 China School of Computer Science and Technology Hainan University Hainan Haikou570228 China College of Computer and Information Science Chongqing Normal University Chongqing Chongqing401331 China School of Mathematics and Statistics Hainan Normal University Hainan Haikou570100 China Key Laboratory of Data Science and Intelligence Education Ministry of Education Hainan Normal University Hainan Haikou China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Hainan Haikou China Geneis Beijing Co. Ltd Beijing Beijing100102 China School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China

Drug repositioning, pivotal in current pharmaceutical development, aims to find new uses for existing drugs, offering an efficient and cost-effective path to drug discovery. In recent years, graph neural network-based deep learning methods have achieved significant success in drug repositioning tasks. However, few studies have analyzed the characteristics of datasets to mitigate potential data biases. In this paper, we analyzed three commonly used drug repositioning datasets and identified a consistent characteristic among them: a trend of node polarization, characterized by the presence of popular entities (those commonly occurring and extensively associated) and long-tail entities (those appearing less frequently with fewer associations). Based on this finding, we propose a deep learning framework with a debiasing mechanism, called DRDM. The framework excels in addressing popular entities’ biases, which often overshadow the subtle patterns in long-tail entities—key for novel insights. DRDM dynamically adjusts association weights during training, enhancing long-tail entity representation and reducing bias. In addition, we employ dual-view contrastive learning to provide rich supervisory signals, thereby further enhancing the model's robustness. We conducted experiments with our method on these three datasets, and the results demonstrated that our approach exhibits strong competitiveness compared to competing models. Case studies further highlighted the potential of the model in practical applications, which could provide new insights for future drug discovery. © 2025

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

引用

IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1188-1198页

作者： Chuan Sun Hongpeng Yin Yanxia Li Yi Chai School of Big Data&Software Engineering Chongqing UniversityChongqing 401331China Key Laboratory of Complex System Safety and Control Ministry of EducationChongqing UniversityChongqing 400044China

The localized faults of rolling bearings can be diagnosed by its vibration impulsive ***,it is always a challenge to extract the impulsive feature under background noise and non-stationary *** paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary *** overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission *** address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components *** experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed ***,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals.

关键词： Dictionary learning impulsive signals detection Kclustering with singular value decomposition(K-SVD) minimum entropy deconvolution rolling bearing signal processing

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：