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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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A Coupled Transformer-CNN Network: Advancing Sea Surface Temperature Forecast Accuracy

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Tao Lin, Pengfei Liu, Hailong Wang, Pengfei Wang, Ya Xu, Kai Zheng, Weipeng Li, Yiwen Jiang, Jinrong Zhao, Lian Chen, Jian State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China College of Earth and Planetary Sciences University of Chinese Academy of Sciences Beijing100049 China Laoshan Laboratory Qingdao266237 China Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China Earth System Numerical Simulation Science Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Ocean Sciences China University of Geosciences Beijing100083 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Geo-Information Engineering Xi'an710054 China

Sea surface temperature (SST) is critically important for understanding ocean dynamics and supporting various marine activities, making accurate short-term SST forecasting highly significant. However, accurately modeling the multi-scale variability of SST remains challenging for existing deep learning (DL) models. This study introduces the Coupled Transformer-CNN Network (CoTCN), a hybrid architecture designed to leverage the multi-scale variability of SST. The CoTCN combines the strengths of Transformers and convolutional neural networks (CNNs), significantly enhancing SST forecasts' spatial continuity and predictive accuracy. Compared to five state-of-the-art DL models based on Transformer or CNN that include ConvLSTM, ConvGRU, AFNO, PredRNN, and SwinLSTM, CoTCN demonstrates superior performance in global and local areas of SST forecasting. At 1-day lead time, CoTCN reduces the global average root mean square error (RMSE) by over 15%, with forecast errors ranging from 0.20°C to 0.53°C across 1-10 day lead times. Moreover, the CoTCN effectively mitigates the checkerboard artifacts inherent to the Vision Transformer architecture. These findings highlight the effectiveness of CoTCN in capturing SST's multi-scale features and underscore the promising potential of hybrid architectures for future DL models. © 1980-2012 IEEE.

关键词： Convolutional neural networks

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Hierarchical convolutional features for end-to-end representation-based visual tracking

Hierarchical convolutional features for end-to-end represent...

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作者： Zhu, Suguo Fang, Zhenying Gao, Fei Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China

Recently, deep learning is widely developed in computer vision applications. In this paper, a novel simple tracker with deep learning is proposed to complete the tracking task. A simple fully convolutional Siamese network is applied to capture the similarity between different frames. Nevertheless, the detailed information from lower layers, which is also important for locating the target object, is not considered into the tracking task. In this paper, the detailed information from two lower layers is considered into the response map to improve the performance and not to increase much time spent. This leads more significant improvement for feature representation and localization of the target object. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the baseline and the state-of-the-art trackers. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 729 LNNS卷 v-vi页

作者： Vasant, Pandian Weber, Gerhard-Wilhelm Arefin, Mohammad Shamsul Rodriguez-Aguilar, Roman Panchenko, Vladimir Munapo, Elias Thomas, J. Joshua Faculty of Electrical and Electronics Engineering Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Chittagong University of Engineering and Technology Chittagong Bangladesh Laboratory of Non-traditional Energy Systems Department of Theoretical and Applied Mechanics Federal Scientific Agroengineering Center VIM Russian University of Transport Moscow Russia Department of Computer Science UOW Malaysia KDU Penang University College George Town Malaysia Faculty of Engineering Management Poznań University of Technology Poznan Poland School of Economics and Decision Sciences North West University Mmabatho South Africa Facultad de Ciencias Económicas y Empresariales School of Economic and Business Sciences Universidad Panamericana Mexico City Mexico

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Differential evolution based on node strength

Differential evolution based on node strength

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作者： Skanderova, Lenka Fabian, Tomas Zelinka, Ivan Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB – Technical University of Ostrava Ostrava Czech Republic Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh Viet Nam

In this paper, three novel algorithms for optimisation based on the differential evolution algorithm are devised. The main idea behind those algorithms stems from the observation that differential evolution dynamics can be modelled via complex networks. In our approach, the individuals of the population are modelled by the nodes and the relationships between them by the directed lines of the graph. Subsequent analysis of non-trivial topological features further influence the process of parent selection in the mutation step and replace the traditional approach which is not reflecting the complex relationships between individuals in the population during evolution. This approach represents a general framework which can be applied to various kinds of differential evolution algorithms. We have incorporated this framework with the three well-performing variants of differential evolution algorithms to demonstrate the effectiveness of our contribution with respect to the convergence rate. Two well-known benchmark sets (including 49 functions) are used to evaluate the performance of the proposed algorithms. Experimental results and statistical analysis indicate that the enhanced algorithms perform better or at least comparable to their original versions. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Complex networks

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modeling the Tertiary Structure of a Multi-domain Protein: Structure Prediction of Multi-domain Proteins 18

Modeling the Tertiary Structure of a Multi-domain Protein: S...

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9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018

作者： Almsned, Fahad Gogovi, Gideon Bracci, Nicole Kehn-Hall, Kylene Blaisten-Barojas, Estela Shehu, Amarda Krasnow Institute for Advanced School of Systems Biology George Mason University United States Center for Simulation and Modeling Dept of Computational and Data Sciences George Mason University United States National Center for Biodefense and Infectious Diseases School of Systems Biology George Mason University United States Dept of Computer Science Dept of Bioengineering Center for Simulation and Modeling School of Systems Biology George Mason University United States

ISBN: (纸本)9781450357944

Due to the central role that tertiary structure plays in determining protein function, resolving protein tertiary structures is an integral research thrust in both wet and dry laboratories. Dry laboratories have primarily focused on small- to medium-size proteins. However, proteins central to human biology and human health are often quite complex, containing multiple domains and consisting of thou- sands of amino acids. Such proteins are challenging for various reasons, including the inability to crystallize. We present a case study of structure determination for the Rift Valley fever virus L-protein, a a large, multi-domain protein with currently no available tertiary structure. We employ this case study as an emerging paradigm and demonstrate how to leverage the rich and diverse landscape of bioinformatics tools for building tertiary structure models for multi-domain proteins with thousands of amino acids. © 2018 ACM.

关键词： Proteins

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A Grid based Approach for Handwritten Text Segmentation

A Grid based Approach for Handwritten Text Segmentation

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IEEE Southeastcon

作者： Soumalya Ghosh Umesh Kumar Gupta Uttam Ghosh Sachin Shetty School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Electrical Engineering & Computer Science Vanderbilt University Nashville TN USA Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk VA USA

ISBN: (数字)9781728101378

ISBN: (纸本)9781728101385

The hand-written text is subject to inconsistency and variability. To achieve the accuracy and consistency in recognizing handwritten characters, the present work discusses an efficient approach to segment the handwritten text into individual characters. In order to achieve this, the writing space is divided into three different zones and then the zones are subdivided tiny square shape grids, which are distributed evenly. After this, we analyze to find the presence of handwritten character in a selected grid by performing horizontal scanning method. Moreover, we propose an approach of segmenting the character set of a single line text by vertical scanning and curve pattern analysis on the touching zones of the selective grids. The efficacy of the approach has proven through rigorous testing on hand-written texts of both professional as well as non-professional users. The testing set consists of 106 text samples including both single line text phrases and short sentences. The proposed approach achieves around 100% accuracy for word level text segmentation on the handwritten texts of both professional and non-professional users. In addition, the average character level segmentation accuracy on texts written by professional and non-professional users are found to be 98.4% and 96.2% respectively. Thus, the average character level segmentation accuracy is improved by 97.3%.

关键词： Writing Connectors Pattern analysis Image segmentation Computational modeling Analytical models Shape

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Oscillation Detection and Diagnosis in Process Industries by Pattern Recognition Technique

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IFAC-PapersOnLine 2019年第1期52卷 299-304页

作者： Jônathan W.V. Dambros Marcelo Farenzena Jorge O. Trierweiler Group of Intensification Modeling Simulation Control and Optimization of Processes (GIMSCOP) Chemical Engineering Department Federal University of Rio Grande do Sul (UFRGS) R. Eng. Luiz Englert s/n Campus Central Porto Alegre RS Brazil Department of Computer Science University of Kaiserslautern 67663 Kaiserslautern Deutschland

Oscillation in control loops is a frequent problem faced in process industries. It deviates the process variables from their desired condition, affecting negatively plant productivity. To guarantee profitability, oscillation must be detected, diagnosed, and, finally, eliminated. Dozens of automatic detection and diagnosis techniques have been proposed over the last years. However, the application to real industrial data reveals low efficiencies, which indicates that the techniques require improvement. This work presents a new method for oscillation detection and diagnosis. The technique classifies the loops based on the shape of the PV(OP) diagram by a pattern recognition approach, where the model is trained with simulated examples that cover a large range of processes and different conditions found in industrial data, such as noise and disturbance presence. The performance of the proposed technique is compared to well-established oscillation detection and diagnosis methods, returning better results.

关键词： Oscillation detection diagnosis process control fault detection diagnosis pattern recognition process monitoring

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Singularity structure simplification of hexahedral mesh via weighted ranking

arXiv

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arXiv 2019年

作者： Xua, Gang Ling, Ran Zhang, Yongjie Jessica Xiao, Zhoufang Ji, Zhongping Rabczuk, Timon School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou310018 China Department of Mechanical Engineering Carnegie Mellon University United States Institute of Structural Mechanics Bauhaus-Universitat Weimar Germany

In this paper, we propose an improved singularity structure simplification method for hexahedral (hex) meshes using a weighted ranking approach. In previous work, the selection of to-be-collapsed base complex sheets/chords is only based on their thickness, which will introduce a few closed-loops and cause an early termination of simplification and a slow convergence rate. In this paper, a new weighted ranking function is proposed by combining the valence prediction function of local singularity structure, shape quality metric of elements and the width of base complex sheets/chords together. Adaptive refinement and local optimization are also introduced to improve the uniformity and aspect ratio of mesh elements. Compared to thickness ranking methods, our weighted ranking approach can yield a simpler singularity structure with fewer base-complex components, while achieving comparable Hausdorff distance ratio and better mesh quality. Comparisons on a hex-mesh dataset are performed to demonstrate the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Aspect ratio

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PDG Pressure Estimation in Offshore Oil Well: Extended Kalman Filter vs. Artificial Neural Networks

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IFAC-PapersOnLine 2019年第1期52卷 508-513页

作者： Andressa Apio Jônathan W.V. Dambros Fabio C. Diehl Marcelo Farenzena Jorge O. Trierweiler Group of Intensification Modeling Simulation Control and Optimization of Processes (GIMSCOP) Chemical Engineering Department Federal University of Rio Grande do Sul (UFRGS) R. Eng Luiz Englert s/n Campus Central Porto Alegre RS Brazil Department of Computer Science University of Kaiserslautern 67663 Kaiserslautern Deutschland Research and Development Center Petrobras 21941915 Rio de Janeiro Brazil

The Permanent Downhole Gauge (PDG) pressure measurement is of great importance for offshore oil well modeling and control since it is measured close to the bottom hole. The PDG is installed in a remote undersea environment, which makes expensive the maintenance in case of fault. For this reason, PDG measurements are frequently unavailable. To overcome this limitation, the PDG pressure can be estimated using other available measurements. The estimation is not a simple task since, depending on process operational conditions, the multiphase flow might present limit cycles. In this work, Artificial Neural Network (ANN) and Extended Kalman Filter (EKF) are proposed as potential techniques for the PDG pressure estimation. The comparison of the results shows that ANN returns precise estimation for a short-time window after the failure, but fails when a different process operating condition is applied, while EKF returns good estimation in all the cases.

关键词： Modelling System Identification Data Analytics Machine Learning Inferential sensing State Estimation Sensor Development

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