检索结果-内蒙古大学图书馆

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Rawat, Aayushi Gupta, Sumita Raghuvanshi, Gauri Aggarwal, Tanya Majhi, Vinayak Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Amity Innovation and Design Center Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798331530389

The rapid evolution in the technology industry highlights the importance of developing a more advanced asset tracking system. The need for efficient and accurate asset management has become crucial and predominant across various industries. This paper displays the utilization of low-cost, and energy efficient technologies like Seeed Studio XIAO nRF52840 and Arduino Uno in designing and implementation of an asset tracking system. The design of the system primarily focuses on providing real time location of an asset, improving its tracking accuracy and operational efficiency. This study practically demonstrates the use of inexpensive microcontroller platforms along with BLE technology so as to develop an asset tracking system, the scalable solution that can be implemented across various industries such as logistics, healthcare, and manufacturing. © 2025 IEEE.

关键词： Costs

来源：评论

学校读者我要写书评

暂无评论

Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

来源：评论

学校读者我要写书评

暂无评论

Externally triggered drug delivery systems

引用

Smart Materials in Medicine 2024年第3期5卷 386-408页

作者： Hu, Huiyang Busa, Prabhakar Zhao, Yue Zhao, Chao Department of Chemical and Biological Engineering University of Alabama TuscaloosaAL35487 United States School of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China Center for Convergent Biosciences and Medicine University of Alabama TuscaloosaAL35487 United States

Externally triggered drug delivery systems empower patients or healthcare providers to utilize external stimuli to initiate drug release from implanted systems. This approach holds significant potential for clinical disease management, offering appealing features like enhanced patient adherence through the elimination of needles and medication reminders. Additionally, it facilitates personalized medicine by granting patients control over the timing, dosage, and duration of drug release. Moreover, it enables precise drug delivery to targeted locations where external stimuli are applied. Advances in materials science, nanotechnology, chemistry, and biology have been pivotal in driving the development of these systems. This review presents an overview of the progress in research on drug release systems responsive to external stimuli, such as light, ultrasound, magnetic fields, and temperature. It discusses the construction strategies of externally triggered drug delivery systems, the mechanisms governing triggered drug release, and their applications in disease management. © 2024 The Authors

关键词： Nanoclay

来源：评论

学校读者我要写书评

暂无评论

A Real-Time Semantic Segmentation Network for Robotic Arm Grasp 1

引用

41st computer Graphics International Conference, CGI 2024

作者： Liu, Li Zhou, Xinlei He, Mingwei Yu, Feng Peng, Tao Hu, Xinrong Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

A robotic arm equipped with a vision system can significantly improve production efficiency. However, due to the complexity of the robotic arm’s working environment, the useful information about the objects recognized by the visual system is significantly reduced. To solve this problem, we propose a real-time semantic segmentation network to achieve high-precision and efficient robotic arm grasp. First, we propose an enhanced feature extraction module for the encoding stage, which can improve feature extraction capabilities without increasing or even reducing the amount of model calculations and parameters. Then, we propose an enhanced feature reconstruction module for the decoding stage, which can better preserve essential features in the feature map restoration stage. To validate the efficacy of our proposed approach, experiments are conducted on the rigid dataset Jacquard and the flexible dataset Electric Wires. Experimental results show that our method can quickly and accurately identify target objects, achieving the best trade-off between accuracy and inference speed. Our method achieves an mIOU of 94.66% and an inference speed of 99.39 FPS on the Jacquard dataset. On the Electric Wires dataset, our method achieves an mIOU of 96.70% with an inference speed of 98.44 FPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Happy: A Debiased Learning Framework for Continual Generalized Category Discovery 38

Happy: A Debiased Learning Framework for Continual Generaliz...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ma, Shijie Zhu, Fei Zhong, Zhun Liu, Wenzhuo Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. Although several settings are proposed to study the C-GCD task, they have limitations that do not reflect real-world scenarios. We thus study a more practical C-GCD setting, which includes more new classes to be discovered over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data of known classes, followed by multiple incremental stages where the model is fed with unlabeled data containing both old and new classes. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We delve into the conflicts and identify that models are susceptible to prediction bias and hardness bias. To address these issues, we introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization. For the prediction bias, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, which can significantly enhance the clustering performance of new classes. For the harness bias, we present the hardness-aware prototype sampling, which can effectively reduce the forgetting issue for previously seen classes, especially for difficult classes. Experimental results demonstrate our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various datasets, e.g., 7.5% overall gains on ImageNet-100. Our code is publicly available at https://***/mashijie1028/Happy-CGCD. © 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

Highlight mask-guided adaptive residual network for single image highlight detection and removal

Highlight mask-guided adaptive residual network for single i...

引用

作者： Wang, Shuaibin Li, Li Wang, Juan Peng, Tao Li, Zhenwei School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China Engineering Research Center of Hubei Province for Clothing Information Wuhan China

Specular highlights detection and removal is a challenging task. Although various methods exist for removing specular highlights, they often fail to effectively preserve the color and texture details of objects after highlight removal due to the high brightness and nonuniform distribution characteristics of highlights. Furthermore, when processing scenes with complex highlight properties, existing methods frequently encounter performance bottlenecks, which restrict their applicability. Therefore, we introduce a highlight mask-guided adaptive residual network (HMGARN). HMGARN comprises three main components: detection-net, adaptive-removal network (AR-Net), and reconstruct-net. Specifically, detection-net can accurately predict highlight mask from a single RGB image. The predicted highlight mask is then inputted into the AR-Net, which adaptively guides the model to remove specular highlights and estimate an image without specular highlights. Subsequently, reconstruct-net is used to progressively refine this result, remove any residual specular highlights, and construct the final high-quality image without specular highlights. We evaluated our method on the public dataset (SHIQ) and confirmed its superiority through comparative experimental results. © 2024 John Wiley & Sons Ltd.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

Asymptotics-Aware Multi-View Subspace Clustering

引用

IEEE Transactions on Multimedia 2025年 27卷 3650-3663页

作者： Xu, Yesong Chen, Shuo Li, Jun Yang, Jian Anhui Polytechnic University School of Computer and Information Wuhu241000 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

Recently, multi-view subspace clustering has attracted extensive attention due to the rapid increase of multi-view data in many real-world applications. The main goal of this task is to learn a common representation of multiple subspaces from the given multi-view data, and most existing methods usually directly merge multiple groups of features by the single-step integration. However, there may exist large disparities among different views of the data, and thus the conventional single-step practice can hardly obtain a generally consistent feature representation for the multi-view data. To overcome this challenge, we present a novel approach dubbed "Asymptotics-Aware Multi-view Subspace Clustering (A2 MSC)"to pursue a consistent feature representation in a multi-step way, which iteratively conducts the data recovery to gradually reduce the differences between pairwise views. Specifically, we construct an asymptotic learning rule to update the feature representation, and the iteration result converges to a consistent feature vector for characterizing each instance of the original multi-view data. After that, we utilize such a new feature representation to learn a clustering-oriented similarity matrix via minimizing a self-expressive objective, and we also design the corresponding optimization algorithm to solve it with convergence guarantees. Theoretically, we prove that the learned asymptotic representation effectively integrates multiple views, thereby ensuring the effective handling of multi-view data. Empirically, extensive experimental results demonstrate the superiority of our proposed A2 MSC over the state-of-the-art multi-view subspace clustering approaches. © 2025 IEEE.

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

TSDDISCOVER: Discovering Data Dependency for Time Series Data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Second-Order Semi-Implicit Method for the Inertial Landau-Lifshitz-Gilbert Equation

引用

Numerical Mathematics(Theory,Methods and Applications) 2023年第1期16卷 182-203页

作者： Panchi Li Lei Yang Jin Lan Rui Du Jingrun Chen School of Mathematical Sciences Soochow UniversitySuzhou 215006China School of Computer Science and Engineering Macao University of Science and TechnologyMacao SARChina Center for Joint Quantum Studies and Department of Physics School of ScienceTianjin University92 Weijin RoadTianjin 300072China Mathematical Center for Interdisciplinary Research Soochow UniversitySuzhou 215006China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhouJiangsu 215123China School of Mathematical Sciences University of Science and Technology of ChinaHefeiAnhui 230026China

Electron spins in magnetic materials have preferred orientations collectively and generate the macroscopic *** dynamics spans over a wide range of timescales from femtosecond to picosecond,and then to *** Landau-Lifshitz-Gilbert(LLG)equation has been widely used in micromagnetics simulations over *** theoretical and experimental advances have shown that the inertia of magnetization emerges at sub-picosecond timescales and contributes significantly to the ultrafast magnetization dynamics,which cannot be captured intrinsically by the LLG ***,as a generalization,the inertial LLG(iLLG)equation is proposed to model the ultrafast magnetization ***,the LLG equation is a nonlinear system of parabolic type with(possible)***,the iLLG equation is a nonlinear system of mixed hyperbolic-parabolic type with degeneracy,and exhibits more complicated *** behaves as a hyperbolic system at sub-picosecond timescales,while behaves as a parabolic system at larger timescales spanning from picosecond to *** hybrid behaviors impose additional difficulties on designing efficient numerical methods for the iLLG *** this work,we propose a second-order semiimplicit scheme to solve the iLLG *** second-order temporal derivative of magnetization is approximated by the standard centered difference scheme,and the first-order temporal derivative is approximated by the midpoint scheme involving three time *** nonlinear terms are treated semi-implicitly using one-sided interpolation with second-order *** each time step,the unconditionally unique solvability of the unsymmetric linear system is proved with detailed discussions on the condition ***,the second-order accuracy of the proposed method in both time and space is *** sub-picosecond timescales,the inertial effect of ferromagnetics is observed in micromagnetics simulations,in consistency wi

关键词： Inertial Landau-Lifshitz-Gilbert equation semi-implicit scheme second-order accuracy micromagnetics simulations

来源：评论

学校读者我要写书评

暂无评论

SCB-LEDN: Lightweight and Efficient Object Detection Network for Student Classroom Behavior 41st

SCB-LEDN: Lightweight and Efficient Object Detection Networ...

引用

41st computer Graphics International Conference, CGI 2024

作者： Jiang, Minghua Zheng, Xingwei Wang, Cheng He, Mingwei Liu, Li Yu, Feng School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (纸本)9783031818059

Object detection techniques in computer vision are increasingly employed in education to analyze student performance and evaluate teaching effectiveness. However, current classroom behavior recognition methods have problems such as decreased accuracy, high memory usage, and slow detection speed. In order to address these challenges, we design a lightweight object detection network (SCB-LEDN) based on the classroom monitoring perspective to enhance the detection accuracy of objects while reducing the model’s parameters and accelerating real-time inference. First, we create the student classroom behavior dataset (SCB-E), which includes five behaviors: raising hands, reading, sleeping, writing, and using a mobile phone. Secondly, We propose the partial detection head (PHead), effectively reducing parameter count and improving detection speed. We introduce the diverse branch block (DBB) in the feature extraction layer and create the C2DB module, enhancing student classroom behavior recognition accuracy. We introduce the EfficientRepBiPAN structure in the neck layer, enabling practical preservation and fusion of spatial information. The experimental results show that our method achieves an average precision (mAP) of 91.5% on the SCB-E dataset and reduces the parameters by 20% compared to the baseline method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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：