检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2025年第1期6卷 221-233页

作者： Yang, Yang Wang, Chao Gong, Lei Wu, Min Chen, Zhenghua Li, Xiang Chen, Xianglan Zhou, Xuehai University of Science and Technology of China School of Computer Science and Technology Hefei230026 China 138632 Singapore East China Normal University School of Data Science and Engineering Shanghai200062 China

Ensemble object detectors have demonstrated remarkable effectiveness in enhancing prediction accuracy and uncertainty quantification. However, their widespread adoption is hindered by significant computational and storage demands, limiting their feasibility in resource-constrained settings. To overcome this, researchers have focused on distilling the knowledge from ensemble object detectors into a single model. In this article, we introduce probabilization based ensemble distillation (ProbED), an innovative ensemble distillation framework that consolidates knowledge from multiple object detectors into a single, resource-efficient model. Unlike traditional ensemble distillation methods that average the outputs of subteachers, ProbED captures comprehensive outcome distributions from all subteachers, providing a more nuanced approach to knowledge transfer. ProbED employs knowledge probabilization to achieve a sophisticated and refined aggregation of teacher knowledge, including feature knowledge, semantic knowledge, and localization knowledge, resulting in dual improvements in prediction accuracy and uncertainty quantification for the student model. In particular, ProED's novel knowledge probabilization-based approach to aggregating teacher knowledge is inspired by our empirical observations, which demonstrate that knowledge probabilization excels in effectively representing uncertainty, improving prediction, and facilitating robust knowledge transfer. Furthermore, we introduce a random smoothing perturbation technique to modify inputs within ProbED, further enhancing the distillation process. Extensive experiments highlight ProbED's ability to significantly enhance the prediction accuracy and uncertainty quantification of various object detectors, demonstrating its superior performance compared to other state-of-the-art techniques. © 2024 IEEE.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

引用

computers, Materials & Continua 2025年第5期83卷 1929-1949页

作者： Honglin Wang Yaolong Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关键词： YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function

来源：评论

学校读者我要写书评

暂无评论

FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

引用

Chinese Physics B 2022年第2期31卷 109-118页

作者： Fei Yu Zinan Zhang Hui Shen Yuanyuan Huang Shuo Cai Sichun Du School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation *** MHNN is simulated and dynamically analyzed,and implemented on ***,a new pseudo-random number generator(PRNG)based on MHNN is *** post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of *** experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming *** random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high ***,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).

关键词： memristive Hopfield neural network(MHNN) pseudo-random number generator(PRNG) FPGA image encryption decryption system

来源：评论

学校读者我要写书评

暂无评论

Fast Phase Estimation Approach for Distributed Scatterer based on Power Method

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12819-12830页

作者： Zhu, Chuanguang Gao, Yangqi Zhang, Liya Liao, Mengguang Wu, Wenhao Sanya Institute of Hunan University of Science and Technology Sanya572024 China Hunan University of Science and Technology School of Earth Sciences and Spatial Information Engineering Xiangtan411201 China

Distributed scatterer (DS) possesses a medium signal-to-noise ratio (SNR), and its interferometric phase is typically estimated from sample covariance matrix (SCM) using phase linking (PL) algorithm. However, the phase estimation of DS is very time consuming. The eigenvalue decomposition (EVD) methods benefit from highly optimized eigen-decomposition libraries and therefore have a higher computational efficiency. Nevertheless, EVD methods are still insufficient due to the massive data provided by modern SAR satellites with multi-polarizations and high spatio-temporal resolution. The essence of conventional EVD methods is to decompose the SCM (or modified SCM) with a series of eigenvalue-eigenvector pairs and then select only the eigenvector corresponding to the largest eigenvalue as the optimal estimate. Inspired by that, we proposed a fast phase estimation approach based on power method (PM), a straightforward algorithm that approximates the largest eigenvalue and its associated eigenvector. The convergence rate of PM depends on the ratio between the second and the first largest eigenvalue, i.e., λ2/λ1. We demonstrate that there is an intrinsic correlation between λ2/λ1 and the traditional quality measure (γPTA). Based on this correlation, we design two termination criteria for PM to enable fast and accurate phase estimation. Experiments with simulated and real SAR data demonstrate that PM achieves comparable phase estimation accuracy to EVD but is 3–6 times faster. Additionally, we compare the computational efficiency and accuracy of EVD, PM, and the eigendecomposition-based maximum likelihood estimator of interferometric phase (EMI), providing practical guidance for different application scenarios. © 2008-2012 IEEE.

关键词： Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

OPTIMIZATION RESEARCH OF IMPROVED GENERALIZED NORMAL DISTRIBUTION OPTIMIZATION ALGORITHM IN WSN COVERAGE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2023年第2期85卷 95-110页

作者： Hu, Biling Hou, Mengshu Xie, Fei Liu, Yanan School of Computer science Hefei Normal University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

A coverage control strategy based on an improved generalized normal distribution optimization algorithm is proposed for coverage optimization of sensor networks. Firstly, IGNDO uses a combination of Logistic and Tent chaotic mappings to initialize the original population in the initialization phase;secondly, it uses nonlinear control parameters to adjust the probability of local search and global search being selected in the iterative phase, and perturbs the update of individuals after iterative update with partial Lévy flight to Improve their ability to escape from their local best;Finally, the algorithm’s search ability is further improved by using random regression transgression. In the experimental phase, the improved algorithm is firstly tested with benchmark functions, and secondly IGNDO is employed to sensor network coverage optimization, and the optimal results are obtained in comparison with other optimization algorithms. © 2023, Politechnica university of Bucharest. All rights reserved.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography

引用

International Journal of Advanced computer science and Applications 2025年第4期16卷 542-551页

作者： Maria, Mahfuza Akter Akhand, M.A.H. Kamal, Abdus Samad Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Graduate School of Science and Technology Gunma University Kiryu376-8515 Japan

In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and efficient connectivity networks of the brain have been examined in this study by constructing a connectivity feature map (CFM) with four widely used connectivity methods from the Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to research how this connectivity’s patterns are influenced by emotion. According to the investigation results, emotions are mainly related to the parietal, central, and frontal regions. The parietal region is more responsible for emotion alteration among these three regions. Positive emotions are associated with more direct correlations and dependencies than negative ones. When experiencing negative emotions, the regions of the brain function more synchronously as well as there are less flow of information. Whether direct or inverse, there is less correlation between brain regions in the higher frequency band than in the lower frequency band. Higher frequencies are associated with increased dependence and directed information transfer between brain regions. Generally, the electrodes in the same lobe show stronger connectivity than those in different lobes. At a glance, the present study is a comprehensive analysis to understand brain network stimulation for emotion from EEG, and it significantly differs from the existing emotion recognition studies typically focused on recognition proficiency. © (2025), (science and Information Organization). All rights reserved.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Analysis of influencing factors of lane change prediction with data missing

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Li, Ye Yu, Le Xing, Lu Liu, Fei School of Traffic and Transportation Engineering Central South University Changsha Hunan China School of Traffic and Transportation Engineering Changsha University of Science and Technology Changsha Hunan China

The accurate behavior prediction of autonomous vehicles relied heavily on precise data supplied by various sensors. In spite of this, data missing due to a communication barrier or equipment failure is an unavoidable issue in the process of data acquisition, which significantly impacts lane-changing (LC) prediction. To solve this issue, the influence of data missing on lane change prediction was explored from the several aspects such as missing pattern, missing proportion and data interpolation. The different degrees of data missing in three patterns, namely block missing, temporally correlated missing and random missing, were examined. Missing values are repaired and LC behavior is predicted based on machine/deep learning models, and hyperparameters are optimized using genetic algorithms. By analyzing the factors that affect the model's generalization ability, the difference between the best data repair result and the best prediction result was further explored. The results show that: (1) the proposed methods can effectively solve the problems of data missing repair and prediction accuracy, especially the KNNImputer for repair and the Transformer for prediction;(2) temporally correlated missing is inferior to random missing for filling in errors, but superior for prediction performances. This indicates that the selection of repair methods should take into account not only the best data imputation, but also the best prediction performance;and (3) there are significant differences in the stability of prediction models under different data missing patterns and the CNN-BiLSTM-SA has achieved relatively good performance IEEE

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

引用

computer Modeling in engineering & sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

引用

science China(Technological sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

来源：评论

学校读者我要写书评

暂无评论

CUTE: A Collaborative Fusion Representation-Based Fine-Tuning and Retrieval Framework for Code Search 19th

CUTE: A Collaborative Fusion Representation-Based Fine-Tunin...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Song, Qihong Liu, Jianxun Hu, Haize School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9783031545207

Code search aims at searching semantically related code snippets from the large-scale database based on a given natural descriptive query. Fine-tuning pre-trained models for code search tasks has recently emerged as a new trend. However, most studies fine-tune models merely using metric learning, overlooking the beneficial effect of the collaborative relationship between code and query. In this paper, we introduce an effective fine-tuning and retrieval framework called CUTE. In the fine-tuning component, we propose a Collaborative Fusion Representation (CFR) consisting of three stages: pre-representation, collaborative representation, and residual fusion. CFR enhances the representation of code and query, considering token-level collaborative features between code and query. Furthermore, we apply augmentation techniques to generate vector-level hard negative samples for training, which further improves the ability of the pre-trained model to distinguish and represent features during fine-tuning. In the retrieval component, we introduce a two-stage retrieval architecture that includes pre-retrieval and refined ranking, significantly reducing time and computational resource consumption. We evaluate CUTE with three advanced pre-trained models on CodeSearchNet consisting of six programming languages. Extensive experiments demonstrate the fine-tuning effectiveness and retrieval efficiency of CUTE. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Query 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：