检索结果-内蒙古大学图书馆

FAGC: Free Space Fragmentation Aware GC Scheme based on Observations of Energy Consumption

学校读者我要写书评

暂无评论

FAGC: Free Space Fragmentation Aware GC Scheme based on Obse...

Design, Automation and Test in Europe Conference and Exhibition

作者： Lihua Yang Zhipeng Tan Fang Wang Yang Xiao Wei Zhang Biao He Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology College of Science and Technology National University of Defense Technology China Huawei Technology Co. Ltd China

Smartphones are everyday necessities with limited power supply. Charging a smartphone twice a day or more affects user experience. Flash friendly file system (F2FS) is a widely-used log-structured file system for smartphones. Free space fragmentation of F2FS consists of invalid blocks mainly causing performance degradation. F2FS reclaims invalid blocks by garbage collection (GC). We explore the energy consumption of GC and the effect of GC on reducing free space fragments. We observe the energy consumption of one background GC is large but its effect on reducing free space fragments is limited. These motivate us to improve the energy efficiency of GC. We reassess how much free space is a free space fragment based on data analysis, use the free space fragmentation factor to measure the degree of free space fragmentation quickly. We suggest the free space fragmentation aware GC scheme (FAGC) that optimizes the selection for victim segments and the migration for valid blocks. Experiments on real platform show that FAGC reduces GC count by 82.68% and 74.51% respectively than traditional F2FS and the latest GC optimization of F2FS, ATGC. FAGC reduces the energy consumption by 164.37 J and 100.64 J compared to traditional F2FS and ATGC respectively for a synthetic benchmark.

关键词：

Numerical Simulation Study on Supercritical Co2 Flooding and Storage Applied in a Complex Tight Oil Reservoir

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Li, Huan Tan, Yongsheng Zhou, Xiang Jiang, Qi Pu, Hongbin He, Jie Petroleum Engineering Department Southwest Petroleum University Sichuan Chengdu610500 China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil Mechanics Chinese Academy of Sciences Hubei Wuhan430071 China Schulich School of Engineering University of Calgary CalgaryABT2N 1N4 Canada Data Company of Xinjiang Oilfield Company Xinjiang Karamay834000 China Huainan Mining Group Coal Bed Methane Development and Utilization Co. Ltd Anhui Huainan232046 China

In this study, a heterogeneous numerical simulation model was developed to study the supercritical CO2（ScCO2） flooding process applied in tight oil reservoir. The sensitivity analysis was conducted to ensure the developed numerical model is suitable for the production prediction process. By comparing the production performances, concentration C30+, distribution of ScCO2 in the reservoir, etc., the effects of injection parameters such as injection pressure, injection rate, injection volume and injection method were studied and optimized. The mechanisms of light components extraction, miscible flooding in the targeted reservoir was analyzed. The results indicated that ScCO2-EOR is an effective approach to boost tight oil recovery factor in the targeted oil reservoirs, and the mechanisms shew important influences in this flooding process. Using the numerical simulation method, the optimization of the injection parameters was implemented, the oil recovery factor was increased up to 46.37% after pressure depletion and water flooding processes under the optimized injection parameters. In order to reduce the influence of gas channeling, the ScCO2-WAG process was also developed. Compared with ScCO2 flooding process, with ScCO2-WAG application the light components extraction and miscible flooding became weaker, thus lower oil recovery factors were obtained. The CO2 storage was a potential benefit in the ScCO2EOR approach applied in the tight formation. The fruits of this study can be applied to guide the field application. © 2024, The Authors. All rights reserved.

关键词： Carbon dioxide

Weather classification method based on spiking neural network

学校读者我要写书评

暂无评论

Weather classification method based on spiking neural networ...

Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Meng Tian Xuefei Chen Hongkuo Zhang Peng Zhang Kejing Cao Ruiyi Wang School of Computer and Information Engineering Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9781665406314

People's life and production activities are directly or indirectly affected by the weather. It is very necessary to accurately and quickly predict weather conditions. At present, the weather prediction system needs a series of sensors and manual assistance, but it cannot be arranged in high density due to high cost, which leads to inaccurate weather prediction. Computer vision technology can classify weather conditions through images, which reduces the cost and can be arranged in high density to ensure the accuracy of weather prediction. Because the training and reasoning of traditional p Convolutional Neural Network has very large energy consumption, while Spiking Neural Network has the characteristics of ultra-low energy consumption, which can further reduce the energy cost. In this paper, a shallow Spiking Neural Network for weather classification is constructed, which is trained and tested on a dataset containing four categories (cloudy, rainy, sunny and sunrise). Experiments show that the classification accuracy of the model is 93.45%, which is higher than that of the Convolutional Neural Network based on vgg19. In addition, the computational complexity of Spiking Neural Network and Convolutional Neural Network are analyzed to show the advantages of Spiking Neural Network in energy consumption.

关键词： Training Energy consumption Costs Neural networks Weather forecasting Cognition Sensor systems

Boosting Fine-Grained Visual Anomaly Detection with Coarse-knowledge-Aware Adversarial Learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Fang, Qingqing Su, Qinliang Lv, Wenxi Xu, Wenchao Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Artificial Intelligence Sun Yat-sen University Guangdong China Pazhou Lab Guangzhou510330 China

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder’s reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

A Secure Authentication Framework to Guarantee the Traceability of Avatars in Metaverse

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yang, Kedi Zhang, Zhenyong Tian, Youliang Ma, Jianfeng The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The Institute of Cryptography & Data Security GuiZhou University Guiyang550025 China The School of Cyber Engineering Xidian University Xi’an710126 China

Metaverse is a vast virtual environment parallel to the physical world in which users enjoy a variety of services acting as an avatar. To build a secure living habitat, it’s vital to ensure the virtual-physical traceability that tracking a malicious player in the physical world via his avatars in virtual space. In this paper, we propose a two-factor authentication framework based on chameleon signature and biometric-based authentication. First, aiming at disguise in virtual space, we propose a chameleon collision signature algorithm to achieve the verifiability of the avatar’s virtual identity. Second, facing at impersonation in physical world, we construct an avatar’s identity model based on the player’s biometric template and the chameleon key to realize the verifiability of the avatar’s physical identity. Finally, we design two decentralized authentication protocols based on the avatar’s identity model to ensure the consistency of the avatar’s virtual and physical identities. Security analysis indicates that the proposed authentication framework guarantees the consistency and traceability of avatar’s identity. Simulation experiments show that the framework not only completes the decentralized authentication between avatars but also achieves the virtual-physical tracking. Copyright © 2022, The Authors. All rights reserved.

关键词： Authentication

Joint User Association and Transmission Scheduling in Integrated mmWave Access and Terahertz Backhaul Networks

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Lei Ai, Bo Niu, Yong Jiang, Haiyan Mao, Shiwen Zhong, Zhangdui Wang, Ning The State Key Laboratory of Advanced Rail Autonomous Operation The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China

Terahertz wireless backhaul is expected to meet the high-speed backhaul requirements of future ultra-dense networks using millimeter-wave (mmWave) base stations (BSs). In order to achieve higher network capacity with limited resources and meet the quality of service (QoS) requirements of more users in the integrated mmWave access and terahertz backhaul network, this paper formulates a problem of maximizing the number of users successfully served in both the access and backhaul links. Since the problem is a non-linear integer optimization problem, a minimum rate ratio user association and transmission scheduling algorithm is proposed to obtain a suboptimal solution. The proposed algorithm takes the minimum rate ratio as the user association criterion and schedules first the users with fewer backhaul transmission slots. In addition, the algorithm will update the number of access transmission slots allocated to users and the access scheduling results after the backhaul scheduling phase. Numerical results show that the proposed algorithm outperforms several benchmark algorithms in terms of the number of served users and system throughput, and it can cope with a large number of bursty user requests. Copyright © 2023, The Authors. All rights reserved.

关键词： Millimeter waves

DeepMerge: Deep-learning-based Region-Merging for Image Segmentation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lv, Xianwei Persello, Claudio Li, Wangbin Huang, Xiao Ming, Dongping Stein, Alfred School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University Qinhuangdao066004 China Dept. of Earth Observation Science Faculty ITC University of Twente Enschede7500AE Netherlands State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China The Department of Geosciences University of Arkansas FayettevilleAR United States Beijing100083 China

Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods. © 2023, CC BY.

关键词： Image segmentation

Dual Contrastive Learning Model for Representation Learning and Background Debiasing in SAR Object Classification

学校读者我要写书评

暂无评论

Dual Contrastive Learning Model for Representation Learning ...

SAR in Big data Era: Models, Methods and Applications (BIGSARdata)

作者： Jing Wang Sirui Tian Xiaolin Feng Bo Zhang Fan Wu Chao Wang School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

As a popular representation learning framework, contrastive learning has been widely used in SAR image interpretation for feature extraction. Recent studies have shown that some features obtained by the model are related to the background information due to the spurious correlation of the backgrounds during the SAR data collection. As a result, the generalization ability of the representation learning model will be greatly affected by the background information and cannot satisfy the requirements of practical applications. This paper constructed a multi-branch dual contrastive learning model that simultaneously compares the similarities between objects and backgrounds of various images. The motivation is to alleviate the impact of the spurious correlation between background information and the object category. The experimental results and interpretable Shapley value demonstrated that the proposed method significantly improved the generalization performance of the SAR object classification algorithm.

关键词：

Differential Evolution With Multivariate Gaussian Sampling For Sensor Arrangement

学校读者我要写书评

暂无评论

Differential Evolution With Multivariate Gaussian Sampling F...

China Semiconductor Technology International Conference (CSTIC)

作者： Kuiling Du Gang Tang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang China China Construction Fifth Engineering Bureau Ltd Changsha Hunan China

Sensor arrangement problems with the unknown number of sensors and obstacles in an area to be covered by sensors, which are complicated for common evolutionary algorithms to search for the global optimum. This paper presented a new evolutionary algorithm to solve the problem, which utilized hybrid mutation and a specific crossover strategy. Sampling the best individual of the population using the multivariate Gaussian distribution during the mutation process, and the individuals generated by multivariate Gaussian sampling would skip the crossover to selection. It has been proved that the new algorithm has better solution quality and faster convergent speed than classic differential evolution (DE).

关键词：