检索结果-内蒙古大学图书馆

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhouqing Han Xiaojun Zhu Lijie Xu Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

We consider barrier coverage applications where a set of UAVs are deployed to monitor whether intruders pass through a line, i.e., the barrier. Due to limited energy supply of UAVs, a charging pile is used to recharge UAVs. The problem is to place UAVs on top of the barrier and schedule them to the charging pile such that the barrier is seamlessly covered and the total number of UAVs is minimized. We decompose the problem into subproblems by dividing the barrier into disjoint subsegments and covering each subsegment independently. We prove a theoretical lower bound on the minimum number of UAVs required to cover the barrier forever. We then propose two scheduling strategies. In the first strategy, only fully recharged backup UAVs will be scheduled to take over UAVs running out of energy. If there are enough UAVs, this strategy can cover the barrier forever. The second strategy is proposed to deal with the situation that the number of UAVs is insufficient. Under the strategy, if no backup UAV is fully recharged, the one with the most battery energy will be selected to take over the monitoring task. When the number of UAVs is insufficient, the barrier can still be covered for a long time. We analytically derive the number of UAVs required by the first strategy, and the monitoring duration of the second strategy in case of insufficient UAVs. Simulations verify the effectiveness of the proposed solutions.

关键词： Schedules Conferences Force Batteries Task analysis Monitoring

来源：评论

学校读者我要写书评

暂无评论

Influence of wavelet characteristics on single-phase grounding fault line selection

引用

IOP Conference Series: Earth and Environmental Science 2022年第1期983卷

作者： Jing Gong School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044 China

It is difficult to select the fault line in distribution network because of the small single-phase grounding fault current. Wavelet transform is especially suitable for fault transient analysis because of its unique time-frequency localization performance. However, the characteristics of different wavelets have a direct impact on the line selection results. Improper selection of wavelets will directly lead to misjudgment. Firstly, the transient characteristics of single-phase grounding fault and the relationship between wavelet transform modulus maxima and singularity are analysed, and the line selection criterion of wavelet modulus maxima is given. Then, the influence of wavelet vanishing moment order, support length, symmetry, and orthogonality on the detection effect of signal singularity is analysed, and four principles for selecting wavelet in fault line selection are proposed. The larger the order of vanishing moment and support length, the more conducive to detection. The influence of vanishing moment order is much greater than that of support length. The 10kV system simulation model is built, five representative wavelets are selected to decompose the zero- sequence current of the fault line in four scales respectively, and the different modulus maxima corresponding to the sudden change point are compared. The experimental results prove the reliability of the wavelet selection principles proposed in this paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Wavelet packet coefficient of transient first half wave line selection method for single-phase grounding fault

引用

IOP Conference Series: Earth and Environmental Science 2022年第1期983卷

Overvoltage will occur after single-phase grounding fault in distribution network, endangering system insulation. It is of great significance to find out the fault line as soon as possible to ensure the safe operation of the system. Wavelet modulus maxima can be used to detect the sudden change singularity of transient current. However, the judgement of single mode maximum value is easily influenced by the form of fault and interference, leading to misjudgement. In this paper, the transient characteristics of single-phase grounding fault are analyzed, and the magnitude and phase characteristics of the first half wave are given; Based on wavelet packet algorithm, several key problems such as boundary effect, wavelet selection and decomposition scale are explored; Taking the zero sequence voltage as the reference to determine the fault interval, the zero sequence current of each line is decomposed by three-scale wavelet packet using db6 wavelet, and the wavelet coefficients of the characteristic frequency band are extracted for comparison. The fault line is the one with the largest amplitude and different phase from others. The distribution system model of neutral grounding through arc suppression coil is built. Simulation is carried out for several cases with great difficulty in line selection, such as short circuit when phase voltage crosses zero, high resistance grounding and long line fault. The experimental results prove the reliability of this method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Task-oriented Domain-specific Meta-Embedding for Text Classification

Task-oriented Domain-specific Meta-Embedding for Text Classi...

引用

Conference on Empirical Methods in Natural Language Processing (EMNLP)

作者： Wu, Xin Cai, Yi Li, Qing Wang, Tao Yang, Kai South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Kings Coll London Dept Biostat & Hlth Informat London England City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781952148606

Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks. However, domain-specific knowledge is still ignored by existing meta-embedding methods, which results in unstable performances across specific domains. Moreover, the importance of general and domain word embeddings is related to downstream tasks, how to regularize meta-embedding to adapt downstream tasks is an unsolved problem. In this paper, we propose a method to incorporate both domain-specific and task-oriented information into meta-embeddings. We conducted extensive experiments on four text classification datasets and the results show the effectiveness of our proposed method.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Multi-stage hybrid multiobjective evolutionary algorithm for multi-type vehicle routing problem with simultaneous pickup and delivery and time windows

引用

Computers and Industrial Engineering 2025年 207卷

作者： Zhang, Wenqiang Li, Shun Deng, Miaolei Mu, Yashuang Li, Peng Ministry of Education China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology China School of Artificial Intelligence and Big Data Henan University of Technology China College of Information Science and Engineering Henan University of Technology China Institute for Complexity Science Henan University of Technology China

The vehicle routing problem (VRP) is a fundamental and extensively studied problem in logistics and transportation. Despite its significance, real-world applications often require more complex variants to address specific operational constraints and objectives. To address these limitations, this paper introduces the multi-type vehicle routing problem with simultaneous pickup and delivery and time windows (MTVRPSPDTW). Although hybrid multiobjective algorithms have been successful in combination optimization problems in recent years, it is still challenging to improve the performance of the algorithms by combining them with different timing. We propose a multi-stage hybrid evolutionary multi-objective optimization with a multi-region sampling strategy (MS-HEMO-MRSS) to optimize both vehicle number and wait time of MTVRPSPDTW. The algorithm integrates a three-stage hybrid approach, combining a global search using the multi-region sampling strategy (MRSS) with a local search based on routing sequence differential evolution (RSDE). The initial stage employs MRSS to quickly position the population near the Pareto front from various directions. The second stage utilizes RSDE to accelerate convergence towards central and edge areas of the Pareto front. In the final stage, individuals on Pareto front are selected and RSDE is used again to guide them towards the edge regions to enhance distribution performance. Specialized encoding, decoding techniques, and genetic operators tailored for two vectors are introduced to optimize MTVRPSPDTW. Comparative experiments with traditional multiobjective evolutionary algorithms demonstrate significant convergence and notable distribution performances, highlighting the benefits of employing different optimization strategies at different evolutionary stages. © 2025 Elsevier Ltd

关键词： Pareto principle

来源：评论

学校读者我要写书评

暂无评论

SSM-DTA: Breaking the Barriers of data Scarcity in Drug-Target Affinity Prediction

arXiv

引用

arXiv 2022年

作者： Pei, Qizhi Wu, Lijun Zhu, Jinhua Xia, Yingce Xie, Shufang Qin, Tao Liu, Haiguang Liu, Tie-Yan Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China No.59 Zhong Guan Cun Avenue Haidian District Beijing100872 China Microsoft Research AI4Science No.5 Dan Ling Street Haidian District Beijing100080 China CAS Key Laboratory of GIPAS EEIS Department University of Science and Technology of China No.96 JinZhai Road Baohe District Anhui Province Hefei230026 China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the SSM-DTA framework, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling (MLM) using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS, and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations, and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes. Our code is available at https://***/QizhiPei/SSM-DTA. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Instant Edit Propagation on Images Based on Bilateral Grid

引用

Computers, Materials & Continua 2019年第8期61卷 643-656页

作者： Feng Li Chaofeng Ou Yan Gui Lingyun Xiang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore639798Singapore

The ability to quickly and intuitively edit digital content has become increasingly important in our everyday ***,existing edit propagation methods for editing digital images are typically based on optimization with high computational cost for large ***,existing edit propagation methods are generally inefficient and highly ***,to improve edit efficiency,this paper proposes a novel edit propagation method using a bilateral grid,which can achieve instant propagation of sparse image ***,given an input image with user interactions,we resample each of its pixels into a regularly sampled bilateral grid,which facilitates efficient mapping from an image to the bilateral *** a result,all pixels with the same feature information(color,coordinates)are clustered to the same grid,which can achieve the goal of reducing both the amount of image data processing and the cost of *** then reformulate the propagation as a function of the interpolation problem in bilateral space,which is solved very efficiently using radial basis *** results show that our method improves the efficiency of color editing,making it faster than existing edit approaches,and results in excellent edited images with high quality.

关键词： Instant edit propagation bilateral grid radial basis function image editing

来源：评论

学校读者我要写书评

暂无评论

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

arXiv

引用

arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Online Tensor Decomposition for Hyperspectral data Representation

Online Tensor Decomposition for Hyperspectral Data Represent...

引用

Chinese Automation Congress (CAC)

作者： Jinrong He Yanxin Shi Chengwang Xie Zeyu Bao Zongwen Bai College of Mathematics and Computer Science Yan’an University Shaanxi China School of Computer and Information Engineering South China Normal University Shanwei China School of the Gifted Young University of Science and Technology of China Hefei China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Shaanxi China

ISBN: (纸本)9781665426480

Tensor is a natural representation of visual data, such as hyperspectral image data. Other than vector representation based feature extraction methods, tensor-based representation methods are more informative. Most existing data representation methods only work on vector spaces which are implemented by stacking the original tensor elements into a long vector. However, the vectorization will inevitably lost the underlying structural information and lead to high computational complexity. In order to reduce the memory requirements, we proposed an online tensor decomposition (OTD) method for hyperspectral image feature extraction. It does not require to load the whole tensor data set into main memory, and with the insertion of new tensor samples, it can update projection matrices incrementally, which is desirable for large data sets. In the experiments, the proposed method was applied into the task of apple hyperspectral data classification. The classification results confirm the effectiveness of online tensor decom-position method.

关键词： Visualization Tensors Memory management Stacking Feature extraction Matrix decomposition Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning for Robust Android Malware Familial Classification

arXiv

引用

arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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：