检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Chen, Yuanbo Zheng, Kedi Gu, Yuxuan Wang, Jianxiao Chen, Qixin State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China

The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery model, i.e. the electrochemical model (EM), in the optimal dispatch of the local energy system with high penetration of EVs which replenish energy through V2G-equipped charge station and battery swapping station (BSS). In this paper, to utilize the EM efficiently, recursive EVB constraints and a corresponding matrix-based state update method are proposed based on EM power characterization. The charging EV state distribution is profiled and a multi-layer BSS model along with binary aggregation is proposed, in order to overcome the computation complexity of combining the refined battery constraints with the mixed integer optimization. Finally, a local energy system scenario is investigated for evaluation. The efficiency and effectiveness of EM consideration are assessed from the perspective of both the system and battery. Copyright © 2024, The Authors. All rights reserved.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

Energy Consumption-Driven UAV Dock Deployment Planning for Power Inspection

Energy Consumption-Driven UAV Dock Deployment Planning for P...

引用

2024 Symposium on Spatial Information to Empower the Metaverse

作者： Fu, Jing Shao, Guiwei Wang, Zhiye Wu, Shaolong Cai, Huanqing Wu, Huaxi Chen, Chi Yang, Bisheng China Electric Power Research Institute Co. Ltd Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application Ministry of Education of China China Institute of Geo-spatial Intelligence Wuhan University Wuhan China

As an important energy in modern society, the stable operation of electricity is the guarantee of social and economic operation, and the loss caused by the damage of power facilities is immeasurable, so the regular monitoring of power facilities is indispensable. UAVs, with flexible flight mode, have played an important role in power inspection. With the development of UAV docks, UAV inspection has entered a new stage. However, due to the fixed location of the docks and limited coverage, how to achieve the optimal deployment of the docks with the lowest cost and full coverage of power facilities through reasonable deployment is one of the important issues that need to be solved at this stage of dock construction. To solve this problem, this paper proposes an energy-driven adaptive optimization method for dock location by combining geospatial analysis with UAV performance index. The experiment results show that our method can achieve an efficient, reasonable and feasible optimal location for UAV dock deployment. © Author(s) 2024.

关键词： Site selection

来源：评论

学校读者我要写书评

暂无评论

Solving multi depot school bus routing problem based on simulated annealing algorithm

Solving multi depot school bus routing problem based on simu...

引用

2022 International Conference on Algorithms, Microchips and Network Applications

作者： Han, Daojun Xiang, Hongzhen Xie, Yi Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781510653290

Multi depot school bus routing problem (MDSBRP) refers to the school bus routing problem (SBRP) with multiple depots, which effectively reduces the service cost by adjusting the order of school buses from depot to station. In this paper, a heuristic algorithm based on simulated annealing is proposed to solve the multi depot school bus routing problem. The problem considers the constraints of school time window and school bus capacity. Through the operation adjustment of depots and stations, the number and driving distance of school buses can be effectively reduced. The existing benchmark case set and modified test cases are used to test the algorithm. The experimental results show that the use of multi depot mode can effectively reduce the number of school buses and save operating costs, which shows the effectiveness of the algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Abstract Syntax Tree for Method Name Prediction: How Far Are We? 23

Abstract Syntax Tree for Method Name Prediction: How Far Are...

引用

23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Qian, Hanwei Liu, Wei Ding, Ziqi Sun, Weisong Fang, Chunrong Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Province China City University of Macau China Nanyang Technological University Singapore

ISBN: (纸本)9798350319583

Method name prediction (MNP) aims to recommend a proper name for a method given by the developer, which can ease the programming task and improve programmer productivity. Due to the excellent expressiveness of code representation, abstract syntax trees (AST) have been widely exploited by MNP techniques. However, it is a complex process to manipulate AST, including AST parsing, AST preprocessing, and AST encoding, of which a change in the scheme may change the AST embeddings and thus affects the performance of MNP. In this paper, we first conduct a comprehensive empirical study to systematically investigate the impact of the sub-processes of AST usage on MNP performance. The empirical findings of this study unmistakably demonstrate that AST has a positive impact on promoting MNP. Moreover, the selection of schemes for AST parsing, preprocessing, and encoding exerts a profound influence on the effectiveness of MNP. Properly combining AST (e.g., using JDT, AST Pathfull, and code2seq as AST parsing, preprocessing, and encoding methods, respectively) can improve MNP performance by 164% in terms of F1-score compared to using only code tokens. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

来源：评论

学校读者我要写书评

暂无评论

Learning with Open-world Noisy data via Class-independent Margin in Dual Representation Space 39

Learning with Open-world Noisy Data via Class-independent Ma...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)157735897X

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Convolution Neural Network for Renal Function Assessment Based on Glomerular Filtration Rate

Convolution Neural Network for Renal Function Assessment Bas...

引用

2021 International Conference on Advanced Technologies and Applications of Modern Industry, ATAMI 2021

作者： Wu, Congzhong Wang, Lulu Yang, Zhen Wang, Jing Han, Li Li, Fei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230601 China School of Computer and Information Hefei University of Technology Hefei Anhui230601 China The Second Hospital of Anhui Medical University Hefei Anhui230601 China

Objective: To develop a Convolution Neural Network (CNN) and discuss its performance in the estimation of Glomerular Fifiltration Rate (GFR) for patients with chronic kidney disease (CKD). Methods: A total of 112 patients with chronic kidney disease were enrolled in this study. The GFR is measured by 99mTc-DTPA renal dynamic and used as standard GFR after normalization by body surface area imaging. We established a CNN model and verified the performance of the model by comparing the GFR predicted by the model with the standard GFR. It turned out that the CNN could better evaluate the GFR of patients which is superior to the CG formula, MDRD formula, CKD-EPI formula and GRNN model. Conclusions: The CNN significantly evaluated GFR for patients with CKD, and it showed better performance than traditional methods and GRNN model and closer to the results of 99mTc-DTPA. The experimental results demonstrated that CNN could be used to estimate GFR. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

引用

2024 Symposium on Spatial Information to Empower the Metaverse

作者： Sun, Shangzhe Chen, Chi Yang, Bisheng Li, Liuchun Xu, Yuhang Jin, Ang State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application Ministry of Education of China China Institute of Artificial Intelligence in Geomatics Wuhan University Wuhan China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme lighting variations, irregular geometries, and the lack of external positioning signals, making human entry hazardous. Fortunately, advancements in robotics and artificial intelligence have enabled unmanned systems to mitigate these challenges, allowing for safer exploration of unexposed spaces. UAVs, as vital unmanned system platforms, are extensively used across various industrial scenarios and are particularly effective for detecting unexposed spaces. To facilitate automated exploration of these areas, we propose the autonomous UAV system, LUOJIA Explorer. Equipped with a LiDAR sensor, data transmission module, and external anti-collision devices to protect the aircraft, LUOJIA Explorer is designed for effective unexposed space detection. We have integrated a laser point cloud positioning and mapping subsystem, along with a planning and control subsystem, into the LUOJIA Explorer. Through both simulation and actual experiments, the Luojia Explorer has demonstrated the capability to achieve stable flight based on self-positioning in unexposed spaces, with an exploration efficiency of 88.83 m3/s. © Author(s) 2024.

关键词： Aircraft accidents

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Classification Method Based on Spiking Neural Network

arXiv

引用

arXiv 2024年

作者： Liu, Yang Li, Yahui Li, Rui Zhou, Liming Dang, Lanxue Mu, Huiyu Ge, Qiang Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475004 China

Convolutional neural network (CNN) performs well in Hyperspectral Image (HSI) classification tasks, but its high energy consumption and complex network structure make it difficult to directly apply it to edge computing devices. At present, spiking neural networks (SNN) have developed rapidly in HSI classification tasks due to their low energy consumption and event driven characteristics. However, it usually requires a longer time step to achieve optimal accuracy. In response to the above problems, this paper builds a spiking neural network (SNN-SWMR) based on the leaky integrate-and-fire (LIF) neuron model for HSI classification tasks. The network uses the spiking width mixed residual (SWMR) module as the basic unit to perform feature extraction operations. The spiking width mixed residual module is composed of spiking mixed convolution (SMC), which can effectively extract spatial-spectral features. Secondly, this paper designs a simple and efficient arcsine approximate derivative (AAD), which solves the non-differentiable problem of spike firing by fitting the Dirac function. Through AAD, we can directly train supervised spike neural networks. Finally, this paper conducts comparative experiments with multiple advanced HSI classification algorithms based on spiking neural networks on six public hyperspectral data sets. Experimental results show that the AAD function has strong robustness and a good fitting effect. Meanwhile, compared with other algorithms, SNN-SWMR requires a time step reduction of about 84%, training time, and testing time reduction of about 63% and 70% at the same accuracy. This study solves the key problem of SNN based HSI classification algorithms, which has important practical significance for promoting the practical application of HSI classification algorithms in edge devices such as spaceborne and airborne devices. © 2024, CC BY.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Multi-view Vision Transformer for Driver Action Recognition 6th

Multi-view Vision Transformer for Driver Action Recognition

引用

6th International Conference on Intelligent Transportation engineering, ICITE 2021

作者： Shan, Guangwei Ji, Qingge Xie, Yuguang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9789811922589

Distracted driving causes many accidents every year, most of which can be avoided with automatic recognition. As a result, vision-based driver action recognition is receiving increasing research attention. In a limited in-vehicle space, actions can be very ambiguous from an individual view. Therefore exploring efficient multi-view action recognition architecture is meaningful. This study aims to detect the distraction of drivers while identifying the cause. A novel driver action recognition architecture named multi-view vision transformer (MVVT) is proposed, which combines classical convolutional neural networks (CNNs) with vision transformer. Self-attention mechanism is utilized to dynamically aggregate temporal information and fuse features of different views jointly. Experiments demonstrate that MVVT can effectively recognize drivers’ behaviors with multi-view input. A promising result of 84.9% accuracy is achieved on a large public driver action dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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：