检索结果-内蒙古大学图书馆

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

作者： Wentao Shi Tao Zhang Baoqi Huang Bing Jia Ministry of Education Engineering Research Center of Ecological Big Data China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

Angle of Arrival (AoA) estimation based on Constant Tone Extension (CTE) signals in Bluetooth 5 suffers from carrier frequency offsets (CFO) arising from the carrier frequency mismatch between the receiver and the transmitter, extra noises coming from switching frequencies when sampling CTE signals, as well as the phase folding problem due to the usage of mod2π in mapping the increasing phase values in estimation models into [0, 2π). To address these issues, a generalized phase model involving the CFO compensation and the time offset is established, and an adaptive gradient descent optimization algorithm based on the real sampling mechanism is proposed to solve the phase folding problem efficiently while obtaining high-precision AoA estimation. Experimental results show that our method reduces the average estimation error by at least 10% in outdoor scenes and 61% in indoor scenes compared to the conventional methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PM$^{2}$2VE: Power Metering Model for Virtualization Environments in Cloud data Centers

引用

IEEE Transactions on Cloud computing 2023年第3期11卷 3126-3138页

作者： Ziyu Shen Xusheng Zhang Zheng Liu Yun Li School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory for Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Virtualization technologies provide solutions for cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources is a critical foundation of virtualization scheduling. Containers are the smallest unit of virtual resource scheduling and migration. Although many practical models for estimating the power consumption of virtual machines (VMs) have been proposed, few power estimation models of containers have been put forth. In this article, we propose a fast-training piecewise regression model based on a decision tree for VM power metering and estimate the power of containers configured on the VM by treating the container as a group of processes on the VM. We select appropriate features from the collected metrics of VMs/containers to help our model fit the nonlinear relationship between power and features well. Besides, we optimize the leaf nodes of the regression tree, realizing the effective power metering of virtualization environments. We evaluate the proposed model on 13 tasks in PARSEC and compare it with several commonly used models in data centers. The experimental results prove the effectiveness of the proposed model, and the estimated power of containers is in line with expectations.

关键词： Containers data centers Power demand Estimation data models Virtualization Servers

来源：评论

学校读者我要写书评

暂无评论

AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

引用

42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization

arXiv

引用

arXiv 2024年

作者： Yuan, Zhenlong Cao, Jiakai Li, Zhaoxin Jiang, Hao Wang, Zhaoqi Institute of Computing Technology Chinese Academy of Sciences China Agricultural Information Institute Chinese Academy of Agricultural Sciences China Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs China

In this paper, we introduce Segmentation-Driven Deformation Multi-View Stereo (SD-MVS), a method that can effectively tackle challenges in 3D reconstruction of textureless areas. We are the first to adopt the Segment Anything Model (SAM) to distinguish semantic instances in scenes and further leverage these constraints for pixelwise patch deformation on both matching cost and propagation. Concurrently, we propose a unique refinement strategy that combines spherical coordinates and gradient descent on normals and pixelwise search interval on depths, significantly improving the completeness of reconstructed 3D model. Furthermore, we adopt the Expectation-Maximization (EM) algorithm to alternately optimize the aggregate matching cost and hyperparameters, effectively mitigating the problem of parameters being excessively dependent on empirical tuning. Evaluations on the ETH3D high-resolution multi-view stereo benchmark and the Tanks and Temples dataset demonstrate that our method can achieve state-of-the-art results with less time consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

CoNav: A Benchmark for Human-Centered Collaborative Navigation

arXiv

引用

arXiv 2024年

作者： Li, Changhao Sun, Xinyu Chen, Peihao Fan, Jugang Wang, Zixu Liu, Yanxia Zhu, Jinhui Gan, Chuang Tan, Mingkui South China University of Technology China Pazhou Laboratory China UMass Amherst United States MIT-IBM Watson AI Lab United States Key Laboratory of Big Data and Intelligent Robot Ministry of Education China

Human-robot collaboration, in which the robot intelligently assists the human with the upcoming task, is an appealing objective. To achieve this goal, the agent needs to be equipped with a fundamental collaborative navigation ability, where the agent should reason human intention by observing human activities and then navigate to the human's intended destination in advance of the human. However, this vital ability has not been well studied in previous literature. To fill this gap, we propose a collaborative navigation (CoNav) benchmark. Our CoNav tackles the critical challenge of constructing a 3D navigation environment with realistic and diverse human activities. To achieve this, we design a novel LLM-based humanoid animation generation framework, which is conditioned on both text descriptions and environmental context. The generated humanoid trajectory obeys the environmental context and can be easily integrated into popular simulators. We empirically find that the existing navigation methods struggle in CoNav task since they neglect the perception of human intention. To solve this problem, we propose an intentionaware agent for reasoning both long-term and short-term human intention. The agent predicts navigation action based on the predicted intention and panoramic observation. The emergent agent behavior including observing humans, avoiding human collision, and navigation reveals the efficiency of the proposed datasets and agents. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Object Detection Based on Generalized Features

Few-Shot Object Detection Based on Generalized Features

引用

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

作者： Qiuqin Chen Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou Fuzhou China

Few-shot object detection aims to rapidly detect novel classes of objects using a minimal number of annotated instances. Compared to methods such as meta-learning, few-shot object detection based on transfer learning can achieve better performance. However, when training on base-class datasets, the model gradually improves its detection performance on base classes but does not enhance its generalization. A base-class model lacking generalization tends to result in lower detection performance for novel classes during fine-tuning. To address the aforementioned issues, this paper tackles the problem from two aspects: firstly, we introduce the Confusion-Dropblock Module (CDM) to perturb the model's features, thus enhancing feature generalization. Secondly, we incorporate Deformable Convolution Modules to mitigate the impact of object shapes and sizes on detection performance, thereby improving overall detection performance. Experimental results on the COCO dataset demonstrate the effectiveness of the proposed algorithm, outperforming the compared methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The State of Charge Estimation of Lithium-ion Battery Pack Based on PSO-KELM

The State of Charge Estimation of Lithium-ion Battery Pack B...

引用

Chinese Control and Decision Conference, CCDC

作者： Mengyuan Chen Hongyan Ma Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Accurately estimating the State of Charge (SOC) is of great significance for ensuring the safe operation of lithium-ion batteries and preventing overcharging or discharging. However, as SOC is the internal state of the battery unit and cannot be directly measured, it is difficult to obtain accurate SOC values. To improve the estimation accuracy of SOC, this paper establishes a prediction model using Kernel Extreme Learning Machine (KELM), and uses Particle Swarm Optimization (PSO) to optimize the kernel function parameter S and regularization coefficient C of KELM and determine the optimal values, a SOC prediction method for lithium-ion batteries based on PSO-KELM is proposed. The input of the PSO-KELM model is battery voltage, current, and temperature, the output is the actual SOC value. The results show that the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE) of SOC prediction are reduced to 2.76%, 2.18%, and 6.89%, increase coefficient of determination (R 2 ) to 0.9919. The PSO-KELM model improves prediction accuracy compared to ELM and KELM models, and has good convergence and generalization properties.

关键词： Lithium-ion batteries Accuracy Extreme learning machines Estimation Predictive models Battery charge measurement data models

来源：评论

学校读者我要写书评

暂无评论

Driving Capability, a Unified Driver Model for ADAS

Driving Capability, a Unified Driver Model for ADAS

引用

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

作者： Sun, Bohua Zhai, Yang Li, Yaxin Deng, Weiwen Zhao, Shuai State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China Co. Ltd. Tianjin China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

To allocate driving privilege in a reasonable way in shared control for intelligent vehicle, the study on driving capability, the unified driver model for ADAS in the longitudinal and lateral scenarios was proposed, which can improve the safety and comfort for intelligent vehicles as well. Driving capability is defined and analyzed and car-following stimulate in longitudinal scenario and moving double lane change stimulate in lateral scenario were designed. data collection was conducted in Driver-In-the-Loop intelligent Simulation Platform (DILISP). Driving capability identification model was established basing on Hammerstein process and Principal Component Analysis (PCA) was used to decouple and reduce the dimension for the key parameters in Hammerstein identification model. The classification is done basing on the particle clustering algorithm and the evaluation equation for driving capability was calculated by Multiple Linear Regression (MLR). Results show that the proposed evaluation method for driving capability in the longitudinal and lateral scenarios can achieve accurate and reliable evaluation results. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

A Novel Band-Notched Balanced Bandpass Filter with Differential-Mode Reflectionless Characteristics

A Novel Band-Notched Balanced Bandpass Filter with Different...

引用

International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Shipeng Zhao Zhongbao Wang Mingming Gao Hongmei Liu School of Information Science and Technology Dalian Maritime University Dalian Liaoning China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao Liaoning China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

A novel band-notched balanced bandpass filter (BPF) with differential-mode (DM) reflectionless characteristics is proposed in this paper. The original balanced BPF is composed of a parallel-coupled transmission line structure based on signal interference techniques, which can introduce two transmission zeros to improve the filtering selectivity. By utilizing the absorption networks, one notch band at the center of frequency and DM reflectionless characteristics can be achieved in the meanwhile. The common-mode (CM) rejection resistors loaded between the half-wavelength microstrip transmission lines enable suppression of CM noise. The operating frequency of the balanced BPF is located at 1.0 GHz with 3-dB fractional bandwidth of 61.5%. The CM rejection is greater than 20 dB in the filtering bandwidth.

关键词： Band-pass filters Wireless communication Resistors Radio frequency Power transmission lines Filtering Interference

来源：评论

学校读者我要写书评

暂无评论

Feature Rotation Invariance Learning for Point Cloud Analysis 10

Feature Rotation Invariance Learning for Point Cloud Analysi...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

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：