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A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN 17th

A Stepwise Path Selection Scheme Based on Multiple QoS Param...

17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liu, Lin Zhou, Jian-Tao Xing, Hai-Feng Guo, Xiao-Yong College of Computer Science Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China

ISBN: (纸本)9783030926342

Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based on the current network status and the performance criteria of the service requirements. However, it is difficult for proposed solutions to obtain all the available paths owing to not fully considering all the QoS parameters of paths. In this paper, we propose SWQoS, a novel, universal, and stepwise QoS guarantee scheme based on multiple QoS parameter evaluation for selecting the available paths including preferred paths, satisfied paths and reluctant paths in SDN. The experiments show that SWQoS can select all the available paths that meet the performance criteria of the service requirements and have better QoS parameter performance compared with other path selection methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Quality of service

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Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud

Security-as-a-Service with Cyberspace Mimic Defense Technolo...

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2021国际计算机前沿大会

作者： Junchao Wang Jianmin Pang Jin Wei State Key Laboratory of Mathematical Engineering and Advanced Computing School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University

Users usually focus on the application-level requirements which are quite friendly and direct to them. However, there are no existing tools automating the application-level requirements to infrastructure provisioning and application deployment. Although some security issues have been solved during the development phase, the undiscovered vulnerabilities remain hidden threats to the application's security. Cyberspace mimic defense(CMD) technologies can help to enhance the application's security despite the existence of the vulnerability. In this paper,the concept of SECurity-as-a-Service(SECaaS) is proposed with CMD technologies in cloud environments. The experiment on it was implemented. It is found that the application's security is greatly improved to meet the user's security and performance requirements within budgets through SECaaS. The experimental results show that SECaaS can help the users to focus on application-level requirements(monetary costs,required security level, etc.) and automate the process of application orchestration.

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MFE-Net: A Novel Multi-scale Feature Enhancement Network for SAR Ship Instance Segmentation

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Liu, Li Zhang, Shuo Hu, Mingtao North China Electric Power University Hebei Key Laboratory of Knowledge Computing for Energy and Power Department of Computer Baoding071000 China Ministry of Education Engineering Research Center of Intelligent Computing for Complex Energy Systems Department of Computer North China Electric Power University Baoding071000 China University of Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

Deep learning has achieved significant progress in ship instance segmentation for synthetic aperture radar (SAR) im-ages. However, due to the challenges posed by inshore scenes, such as dense clustering, arbitrary arrangement orientation, and multi-scale targets, the performance of existing methods still falls short of expectations. To overcome the dilemma, we propose MFE-Net, a one-stage instance segmentation network based on oriented bounding box (OBB). First, we design an Orientation-aware Seg-mentation Head (OSeg Head) to enhance the network's perception of directional features while mitigating interference from complex background. Additionally, a Feature Aggregation Diffusion Pyra-mid (FADP) is proposed, consisting of the Multi-level Feature Alignment Module (MAM) and the Multi-scale Feature Enhance-ment Module (MFEM). The FADP aggregates global semantic and detailed information, and effectively enhances the feature repre-sentations of both deep and shallow layers through multi-scale fea-ture extraction. Experimental results on two public benchmark datasets (HRSID, SSDD) and a self-built dataset (HRSID_IS) demonstrate that MFE-Net achieves an ideal trade-off between ac-curacy and efficiency, respectively reaching 58.9%, 63.0%, and 46.9% AP with parameters of 12.36M. © 2004-2012 IEEE.

关键词： Deep learning

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Content-Noise Feature Fusion Neural Network for Image Denoising in Magnetic Particle Imaging∗

Content-Noise Feature Fusion Neural Network for Image Denois...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tan Wang Liwen Zhang Zechen Wei Yusong Shen Jie Tian Hui Hui CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Molecular Imaging Beijing China School of Computer Science and Engineering Southeast University Nanjing China Ministry of Industry and Information Technology of the People’s Republic of China Key Laboratory of Big Data-Based Precision Medicine (Beihang University) Beijing People’s Republic of China

Magnetic particle imaging (MPI) is a tomographic imaging method that quantitatively determines the distribution of magnetic nanoparticles (MNPs). However, the performance of MPI is primarily limited by the noise in the receive coil and electronic devices, which causes quantification errors for MPI images. Existing methods cannot efficiently eliminate noise while preserve structural details in MPI images. To address this problem, we propose a Content-Noise Feature Fusion Neural Network equipped with tailored modules of noise learning and content learning. It can simultaneously learn content and noise features of raw MPI images. Experimental results show that the proposed method outperforms the state-of-the-art methods on structural details preservation and image noise reduction of different levels.

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Extended state observer-based sliding mode control for epilepsy

Extended state observer-based sliding mode control for epile...

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Chinese Intelligent Systems Conference, CISC 2020

作者： Wei, Wei Li, Ping Zuo, Min School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (纸本)9789811584497

Closed-loop neuromodulation has great potentials in epilepsy and other neurological diseases. Given the complexity and particularity of epilepsy, the control strategy should be robust enough to those uncertainties and disturbances. To reduce the dependence on model information and ensure the closed-loop regulation of epilepsy, an extended state observer-based sliding mode control scheme is designed to suppress epileptic spikes produced by the Jansens neural mass model. Simulation results confirm the extended state observer-based sliding mode control in suppressing epilepsy. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Sliding mode control

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SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yuan, Zhenlong Yang, Zhidong Cai, Yujun Wu, Kuangxin Liu, Mufan Zhang, Dapeng Jiang, Hao Li, Zhaoxin Wang, Zhaoqi Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China School of Electrical Engineering and Computer Science Australia Hunan Police Academy Information Technology Department Changsha410100 China Shanghai Jiao Tong University Cooperative MediaNet Innovation Center Shanghai200240 China Lanzhou University DSLAB School of Information Science and Engineering 730000 China Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big Data 100081 China

Recently, patch-deformation methods have exhibited significant effectiveness in multi-view stereo owing to the deformable and expandable patches in reconstructing textureless areas. However, existing approaches neglect to address the problem of deformation instability caused by easily overlooked edge-skipping, potentially leading to matching distortions, thus leaving room for further improvement. To fill this gap, we propose SED-MVS, which adopts panoptic segmentation and multi-trajectory diffusion strategy for segmentation-driven and edge-aligned patch deformation. Specifically, to prevent unanticipated edge-skipping, we first employ SAM2 for panoptic segmentation as depth-edge guidance to guide patch deformation, followed by multi-trajectory diffusion strategy to ensure patches are comprehensively aligned with depth edges. Moreover, to avoid potential inaccuracy of random initialization, we combine both sparse points from LoFTR and monocular depth map from DepthAnything V2 to restore reliable and realistic depth map for initialization and supervised guidance. Finally, we integrate the segmentation image with the monocular depth map to exploit inter-instance occlusion relationship, then further regard them as occlusion map to implement two distinct edge constraint, thereby facilitating occlusion-aware patch deformation. Extensive results on ETH3D, Tanks & Temples, BlendedMVS, Strecha and DL3DV-10K datasets validate the state-of-the-art performance and robust generalization capability of our proposed method. © 1991-2012 IEEE.

关键词： Image segmentation

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Potential Geographical Distributions and Spatial Shifts Trends of Ecological Tea Plantations of Camellia Sinensis VAR. Assamica in Yunnan Province, China

Potential Geographical Distributions and Spatial Shifts Tren...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Peirou Yang Weiheng Xu Xingyong Liu Leiguang Wang College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University Kunming China Institute of Quality Standard and Testing Technology for Agro-Products Yunnan Academy of Agricultural Sciences Kunming China College of Landscape Architecture and Horticulture Sciences Southwest Forestry University Kunming China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Camellia sinensis *** is a unique product of Geographical Indication in Yunnan province. Centennial tea plantations within plantations of *** *** can yield high-quality ecological tea. However, the limited production of ecological tea is insufficient to meet the widespread demand for high-quality tea leaves. The optimized MaxEnt model exhibits better accuracy and reliability in predicting the potential suitable distribution of small-sample species. Therefore, this study based on 80 sample data and 16 key environmental variables, optimize the MaxEnt model using the ENMeval package. The distribution and changes of the potential suitable areas of ecological tea plantations of *** *** in Yunnan province were extracted for the periods 1981-2010(History), 2011-2040(Current), and 2041-2070(Future). The research results indicate that the performance of the MaxEnt model has been enhanced, with the 10P value reduced to 0.1125 and Training AUC and Test AUC reaching 0.9516 and 0.9422, respectively. In the three periods, the potential suitable areas are mainly concentrated in the western, the south and southwest regions of Yunnan Province. The area of high suitability has decreased by a total of 259 km 2 , while the area of moderate suitability has increased by 4031 km 2 . There is potential for developing ecological tea plantations of *** *** in the western, the southern and southwestern parts of Yunnan Province. The managers and planners of tea plantations can further plan for the transformation and evaluation of tea gardens based on local policies. The results of this study will contribute to driving tea industry in Yunnan Province towards a more sustainable development path.

关键词： Industries Training Reactive power Biological system modeling Plantations Production Predictive models

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AMGFormer: A multilevel GNSS recording autoencoder for agricultural machinery trajectory operation mode identification

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Computers and Electronics in Agriculture 2025年 237卷

作者： Pan, Chenyu Zhai, Weixin School of Science China University of Mining and Technology Beijing 100083 China College of Information and Electrical Engineering China Agricultural University Beijing 100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural Affairs Beijing 100083 China

Leveraging the spatiotemporal features contained in massive amounts of trajectory data to identify agricultural machinery operation modes and assigning corresponding semantic labels to each unknown trajectory point are important basic tasks in research on spatiotemporal trajectory processing in the agricultural field. However, existing methods ignore the intrapoint feature regulation and fail to fully explore the correlation and dependency between agricultural machinery trajectories. To overcome the above defects, we propose a tailored identification architecture named AMGFormer to model various long-range dependencies in end-to-end agricultural machinery trajectory processing. First, to explore the potential information in agricultural machinery trajectories fully, we propose a data enhancement scheme based on the statistical distribution enriching the expression of trajectories. Second, we developed a multilevel GNSS recording autoencoder (MGRA) for agricultural machinery trajectories,which consists of an information-aware context module and a semi-adaptive spatial aggregation context module to aggregate features across spatial dimensions in a dual manner between and within points of agricultural machinery trajectories to extract contextual information. Finally, we design a dynamic interaction module to cross-model the complex information flows from different modules and filter out irrelevant contexts between various modalities, thereby achieving comprehensive and discriminative feature representation. To evaluate the effectiveness of our model, we conducted experiments on agricultural trajectory samples provided by the key laboratory of Agricultural Machinery Operation Monitoring and Big data Application, Ministry of Agriculture and Rural Affairs, and compared our results with those of advanced agricultural machinery trajectory operation mode identification methods. Our model achieved 91.30% accuracy and a 91.28% F1 score, which is 4.57% higher accuracy and a 4.6

关键词： Agricultural machinery trajectory operation mode identification data enhancement Dynamic interaction module Information awareness context Semi-adaptive spatial aggregation context

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One-Hot Coding Similarity of Vehicle Routing Particle Swarm Optimizer 8th

One-Hot Coding Similarity of Vehicle Routing Particle Swarm ...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： He, CHao Li, Zhi Su, Shoubao Xu, Liukai Wang, Chishe School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China Provincial Engineering Research Center of Digital Technology for Industrial Economic Jinling Institute of Technology Nanjing211169 China School of Computer Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783031067662

Aiming at the real-time problem of vehicle routing planning, an improved particle swarm optimization method, called AVSPSO, is proposed in this paper. In the method, the path similarity of vehicle routing is calculated by adopting based on the One-Hot coding vector and by improving the constraint processing through the combination of coding mode and fixed penalty, in which dynamically adjust the inertia weight through the evolution state is to balance the ability of local mining and global development of particles. Experimental results on two practical cases of vehicle routing optimization problems and comparisons with other methods have shown that the proposed method is feasible and stable with better performances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicle routing

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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