A5083705297- Cloud Computing and Resource Management
- IoT and Edge/Fog Computing
- Video Surveillance and Tracking Methods
- Privacy-Preserving Technologies in Data
- Blockchain Technology Applications and Security
- Caching and Content Delivery
- Advanced Data Storage Technologies
- Distributed and Parallel Computing Systems
- Image Enhancement Techniques
- Complex Network Analysis Techniques
- Advanced Graph Neural Networks
- Text and Document Classification Technologies
- Software-Defined Networks and 5G
- Advanced Image and Video Retrieval Techniques
- Fire Detection and Safety Systems
- Privacy, Security, and Data Protection
- Functional Brain Connectivity Studies
- Traffic Prediction and Management Techniques
- Recommender Systems and Techniques
- Topic Modeling
- Human Mobility and Location-Based Analysis
- Distributed systems and fault tolerance
- Anomaly Detection Techniques and Applications
- Advanced Text Analysis Techniques
- Parallel Computing and Optimization Techniques
Henan University
2018-2025
Beijing University of Posts and Telecommunications
2018
China Education and Research Network
2018
Central South University
2014-2017
Abstract The Siamese network-based tracker calculates object templates and search images independently, the template features are not updated online when performing tracking. Adapting to interference scenarios with performance-guaranteed tracking accuracy background clutter, illumination variation or partial occlusion occurs in area is a challenging task. To effectively address issue abovementioned improve location accuracy, this paper devises residual attentional aggregation network...
Summary With the increasing scale of tasks in cloud computing, problem high energy consumption becomes increasingly serious. To deal with problem, we propose a computing model, which takes into account execution and transmission cost processor. Then, based on this put forward task scheduling optimization algorithm named modified particle swarm (M‐PSO) to handle local optimum slow convergence problem. Different from PSO, M‐PSO can dynamically adjust inertia weight coefficient improve speed...
Abstract In the era of information explosion, energy consumption cloud data centers is significant. It’s critical to reduce large-scale while guaranteeing quality service (QoS), especially video computing platforms. The application virtual machine (VM) consolidation has been regarded as a promising approach improve resource utilization and save centers. this paper, an efficient QoS-aware VM method proposed address issues. A combined prediction model based on grey ARIMA applied host status...
Summary Cloud computing has gained more and attention from industrial academic circle since it offers pay‐as‐you‐go model, business applications based on the cloud are also increasing. These meet requirement of users while at same time triggering problem high energy consumption in data centers. To deal with problem, we propose a new algorithm named EEOM (Energy Efficiency Optimization VM Migrations). Under considering CPU memory factors, key three steps for algorithm, including trigger time,...
During traffic data acquisition, missing often arise owing to equipment failures and network disruptions. Despite extensive research on imputation, two primary limitations persist: First, existing methods struggle fully integrate the spatiotemporal correlations low-rank structures inherent in data. Second, current has mostly focused completely at random (MCAR), with limited attention other patterns. We propose an innovative method, tensor completion graph fusion (TCGNF), address these...
Semantic segmentation plays a crucial role in practical applications, such as autonomous driving and robot navigation. However, prevalent semantic networks suffer from two primary challenges: oversized with redundant parameters that hinder network inference speed excessively lightweight structures sacrifice accuracy. Therefore, it is essential to design strikes balance between accuracy speed. We propose the asymmetric residual bottleneck module, which incorporates dilated convolution,...
In the cloud data centers, due to variable resource requirements of tenants, designers applied infrastructure as a service (IaaS) model provide services for tenants with allocating in charging. The application virtualization technology enables multiple virtual machines (VMs) share resources physical machine (PM). Meanwhile, efficiency centers greatly depends on working VMs. Virtual placement (VMP) plays vital role minimizing total energy consumption and wastage (CDCs). this article, we...
Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual consolidation is a promising access to alleviate cloud data centers prominent energy consumption service level agreement violations with improvement quality (QoS). In this article, we propose an efficient algorithm (AESVMP) based on Analytic Hierarchy Process (AHP) for accordance measure. Firstly, take into consideration three key criteria including host power consumption, available balance ratio,...
Abstract Objective . The clinical diagnosis of Parkinson’s disease (PD) relying on medical history, symptoms, and signs is subjective lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD. Approach. This study proposes a deep learning approach the automatic PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude Low Frequency Fluctuations Regional Homogeneity extracted from rs-fMRI as inputs. inputs are then...
Abstract Existing work generally classifies news headlines as a matter of short text classification. However, due to the strong domain nature and limited length headlines, their classification results are usually determined by several specific keywords, which makes traditional method ineffective. In this paper, we propose new identify keywords in expand features from sentence level word respectively, finally use convolutional neural networks (CNN) extract classify features. The proposed...