检索结果-内蒙古大学图书馆

Utilizing Multipath Effects for Mobile Charging

IEEE Transactions on Mobile computing 2025年

作者： Gao, Jing Wu, Die Zhang, Linglin Li, Jingwen Yang, Jin Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University College of Computer Science Chengdu610065 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%. © 2002-2012 IEEE.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

Age Estimation of Giant Pandas Based on Narrowed Window Ordered Regression

Age Estimation of Giant Pandas Based on Narrowed Window Orde...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Zihao Zhang Han Su Rong Hou Pengcheng Wu Ping Xu Peng Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The age estimation of giant pandas is a crucial aspect of panda conservation efforts. Traditional methods for giant panda age estimation are time-consuming and expensive. Previous research has explored computer vision approaches for this task, primarily focusing on standardized frontal images of giant pandas. However, capturing clear frontal photos in the wild is challenging. In this study, we proposed a method based on Deep Convolutional Neural Networks (DCNN) for age estimation using multi-angle facial images of giant pandas. We expanded the original giant panda frontal age dataset into a multi-angle dataset, consisting of 13,524 facial images from 285 pandas. To enhance the model’s ability to fit multi-angle data, we incorporated deformable convolutions during feature extraction. We introduced a narrowed window ordered regression (NWR), and comparisons across multiple images to reduce spatial distances between feature vectors from different angles. The final model achieved a Mean Absolute Error (MAE) of 3.38. demonstrating superior performance even on frontal data. The results indicate the effectiveness of our approach in utilizing multi-angle facial data for wild giant panda age estimation. Computer vision technologies showcase their potential for meaningful contributions to wildlife conservation efforts.

关键词： Deformable models Computer vision Wildlife Neural networks Estimation Focusing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Nested Edge Addition Strategy for Network Controllability Robustness Enhancement

A Nested Edge Addition Strategy for Network Controllability ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Chengpei Wu Siyi Xu Zhuoran Yu Junli Li School of computer science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu Sichuan China

Edge rectification is a widely used method to enhance network robustness. However, in some networked systems, edge rectification may be challenging or even infeasible to implement. An edge addition strategy is proposed as an alternative optimization method in this paper. Nested Ring Structure (NRS), whereby each node's edges connect its nearest neighbors along the backbone direction, have exhibited robust controllability against random attacks. Therefore, The Nested Edge Addition (NEA) strategy is proposed, which enhances network controllability by building NRS through edge addition to a given initial network. With a small number of added edges, NEA can rapidly enhance network controllability, allowing the network to be controlled using just one driver node. The more nested edges are added, the stronger the NRS in a network, thus exhibiting better controllability robustness. The effectiveness of NEA is verified by simulations on both synthetic and real-world networks. Extensive experimental results demonstrate that NEA is an efficient strategy for designing network topology and optimizing real-world networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Perceptual Evaluation Based on WOA-BP Neural Network

Perceptual Evaluation Based on WOA-BP Neural Network

引用

Artificial Intelligence and Intelligent Information Processing (AIIIP), International Conference on

作者： Jiao Juanni Zheng Yong Li Wei College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China

In modern society, various patterns play an indispensable role. With the help of computer technology, the efficiency of designers in the field of pattern design has been significantly improved. However, in the process of pattern design, we often only rely on the subjective experience of designers, while ignoring the personal preferences of consumers. In order to explore the relationship between different pattern designs and consumers' perceptual evaluation, the Kansei engineering theory is first employed to capture consumers' perceptual evaluation of the patterns. Subsequently, various pattern designs and consumers' perceptual evaluation were utilized as inputs and outputs for the WOA-BP neural network, thereby constructing an intelligent model capable of predicting consumers' perceptual evaluation of different pattern designs. The experimental results demonstrate that the model has a highly accurate prediction level. With this model, designers can quickly adjust the pattern design to better meet the aesthetic needs of today's consumers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting Robustness Performance with Noises in Network Representation

Predicting Robustness Performance with Noises in Network Rep...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Chengpei Wu Siyi Xu Junli Li School of computer science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu Sichuan China

The connectivity and controllability of complex networks play an important role in ensuring the proper functioning of network systems. Robustness of connectivity and controllability is the ability of a network to maintain its basic functions against various malicious attacks. Convolutional neural network (CNN)-based approaches provide an efficient framework to approximate the network robustness, which significantly reduces computation time compared to attack simulations. In this paper, the performance of CNN-based prediction for connectivity and controllability robustness is investigated, when there are noises in the network input representation. Two CNN-based predictors are compared and investigated, 1) convolutional neural network-based robustness predictor (CNN-RP), and 2) spatial pyramid pooling-based convolutional neural network (CNN-SPP). Two aspects of network information noises are considered and investigated, 1) the random node information noises (RNIN), and 2) the random edge information noises (REIN). The following main conclusions are obtained from extensive experimental studies on synthetic networks: 1) CNN-RP is more tolerant than CNN-SPP to network noises, 2) The characteristics of small-world and scale-free networks make them have a favorable anti-noise ability, and 3) RNIN has less impact on CNN-based prediction performance than REIN, RNIN and REIN show opposite effects on prediction performance when the size of the predicted network is out of the training network size range.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IC-CViT: Inverse-Consistent Convolutional Vision Transformer for Diffeomorphic Image Registration

IC-CViT: Inverse-Consistent Convolutional Vision Transformer...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Tao Xu Ting Jiang Xiaoning Li College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu China

Diffeomorphic registration plays a crucial role in medical image analysis due to the invertible and one-to-one mapping transformation. In recent years, with the development of deep learning technology, convolutional neural networks (CNNs) have been a broad focus of research in medical image registration, and CNN-based methods have made great progress. However, the results of most existing methods generally are not necessarily diffeomorphic, generating implausibly bijective mappings between images due to the interpolation and discrete representation. Furthermore, the performances of CNNs may be limited by a lack of precise comprehension of global and long-range cross-image spatial relevance. Vision Transformer (ViT) is capable of enhancing the long-distance information interaction ability to identify the semantically anatomically correspondences of medical images. Compared with CNN, ViT has weak local feature extraction ability due to less inductive bias, especially in small-scale training datasets, meaning that the samples between adjacent pixels cannot be exploited adequately. To address the above challenges, we propose a novel Inverse-Consistent Convolutional Vision Transformer (IC-CViT) network for diffeomorphic image registration. Specifically, image pairs can explicitly conduct bi-directional registration through the predicted deformation filed, generated within the diffeomorphic mappings space and restricted by the proposed inverse consistent loss term. We verify our method on two 3D brain MRI scan datasets including OASIS and LPBA40. Comprehensive results demonstrate that IC-CViT achieves state-of-the-art registration accuracy while maintaining desired diffeomorphic properties.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Understanding Wireless Charger Networks: Concepts, Current Research, and Future Directions

引用

IEEE Communications Surveys and Tutorials 2024年

作者： Ren, Meixuan Dai, Haipeng Liu, Tang Deng, Xianjun Dou, Wanchun Yang, Yuanyuan Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Stony Brook University Department of Electrical and Computer Engineering NY11794 United States

Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networks. Although extensive research has been conducted in this area over the last decade, there is currently no comprehensive survey to compile the latest literature and provide insights into future research directions. To fill this gap, our survey explores the recent developments in the active research area of WCNs. This paper starts by providing a framework of WCNs in detail, covering aspects of network architecture, various charging models, network design issues, and typical applications of WCNs. Then, we give an overview of charger deployment schemes, focusing on omnidirectional, directional, non-radiative, and heterogeneous charger deployments. We also provide an overview of charging scheduling schemes, encompassing power control, time allocation, energy beamforming, and multi-resource scheduling. Moreover, we explore communication optimization schemes, including Medium Access Control (MAC) protocols, routing protocols, broadcast transmission, and data collection. Finally, we highlight some future research directions and present corresponding open issues to advance the research on WCNs. © 1998-2012 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

Unraveling the Neural Dynamics of Mathematical Interference in English Reading:A Novel Approach with Deep Learning and Fnirs Data

SSRN

引用

SSRN 2024年

作者： Liang, Zhijie Wang, Ling Sun, Bo Wang, Daifa Yang, Juan School of Computer Science Sichuan Normal University Chengdu610066 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China College of Foreign Languages Sichuan Normal University Chengdu610066 China School of Biological Science and Medical Engineering Beihang University Beijing100083 China

English has emerged as the predominant global language, making it a central focus in educational field seeking to integrate English with other disciplines for improved learning efficiency. Despite numerous studies demonstrating a connection between English learning performance and mathematical logic skills, current explanations and theoretical frameworks predominantly rely on behavioral analysis. This highlights the need for neuroscientific evidence to elucidate the neural mechanisms supporting this correlation, especially in terms of internal neural processes. To bridge this gap, the present study employs functional near-infrared spectroscopy (fNIRS) to construct a novel dataset on mathematical interference in English acquisition. Utilizing this dataset, a novel deep learning model named AC-LSTM is proposed, amalgamating Transformer and LSTM architectures to identify residual mathematical cognition during the English learning process. The AC-LSTM model achieves an exceptional accuracy rate of 99.8%, surpassing other machine learning and deep learning models. Moreover, a multi-class classification experiment is conducted to discern algebra, geometry, and quantitative reasoning interference, with the AC-LSTM model achieving the highest accuracy of 75.9% in this classification task. Furthermore, crucial brain channels for interference detection are pinpointed through grid search, and alterations in vital brain regions (R-Broca and L-Broca) are unveiled via association rule analysis. By integrating fNIRS, deep learning, and data mining techniques, this study delves into cognitive interference in English learning, providing valuable insights for educational neuroscience and data mining research. © 2024, The Authors. All rights reserved.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Cross-Angle Facial Individual Verification of Giant Panda

Cross-Angle Facial Individual Verification of Giant Panda

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jiawei Wu Han Su Peng Min Mengnan He Pengcheng Wu Gai Luo Rong Hou Peng Chen College Computer Science of Sichuan Normal University Chengdu China College of Computer Science Sichuan Normal University Chengdu China and also with Visual Computing and visual reality Key Laboratory of Sichuan Province Chengdu China College of Sichuan Normal University Chengdu China Chengdu Research Base of Giant Panda Chengdu Research Base of Giant Panda Breeding Sichuan Normal University Chengdu China Chengdu Research Base of Giant Panda Breeding

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Enhancing the protection of giant pandas necessitates a precise means of verifying their individual information. With the development of deep learning technology, some individual recognition methods for giant pandas have emerged. However, these methods often neglect cross-angle facial recognition, a common occurrence in natural settings, and predominantly focus on identification rather than verification, rendering them unsuitable for wild populations of unknown individuals. Cross-angle facial verification poses novel challenges, notably feature misalignment and geometric deformation induced by varying angles, particularly in the verification process reliant on comparing feature discrepancies. To address this issue, we developed the CrossPandaFace model. This model employs a Pixel Drift Unit (PDU) to adjust feature pixels and utilizes template features generated by the Template Generation Module (TGM) as a reference to align giant pandas from different angles onto a unified template. Furthermore, Multi-scale Feature Supplement (MSFS) compensates for the potential risk of losing local features of aligned features. Experimental results show state-of-the-art performance, thus affirming the efficacy of our model for cross-angle giant panda verification.

关键词： Deep learning Deformation Face recognition Rendering (computer graphics) Protection Cybernetics

来源：评论

学校读者我要写书评

暂无评论

An extended sequential quadratic method with extrapolation

arXiv

引用

arXiv 2023年

作者： Zhang, Yongle Pong, Ting Kei Xu, Shiqi Department of Mathematics Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu China Department of Applied Mathematics the Hong Kong Polytechnic University Hong Kong Hong Kong Department of Mathematics Sichuan Normal University Chengdu China

We revisit and adapt the extended sequential quadratic method (ESQM) in [3] for solving a class of difference-of-convex optimization problems whose constraints are defined as the intersection of level sets of Lipschitz differentiable functions and a simple compact convex set. Particularly, for this class of problems, we develop a variant of ESQM, called ESQM with extrapolation (ESQMe), which incorporates Nesterov's extrapolation techniques for empirical acceleration. Under standard constraint qualifications, we show that the sequence generated by ESQMe clusters at a critical point if the extrapolation parameters are uniformly bounded above by a certain threshold. Convergence of the whole sequence and the convergence rate are established by assuming Kurdyka- Lojasiewicz (KL) property of a suitable potential function and imposing additional differentiability assumptions on the objective and constraint functions. In addition, when the objective and constraint functions are all convex, we show that linear convergence can be established if a certain exact penalty function is known to be a KL function with exponent 1/2;we also discuss how the KL exponent of such an exact penalty function can be deduced from that of the original extended objective (i.e., sum of the objective and the indicator function of the constraint set). Finally, we perform numerical experiments to demonstrate the empirical acceleration of ESQMe over a basic version of ESQM, and illustrate its effectiveness by comparing with the natural competing algorithm SCPls from [35]. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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：