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IEEE International Conference on systems, Man and Cybernetics

作者： Wei Hu Yong Dong Fang Liu Qiang Jiao College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China School of Computer Science Wuhan University Wuhan China Wuhan City College Wuhan China

ISBN: (纸本)9781665442084

Deep convolutional neural network (DNN) has been widely used in image recognition, target detection, and natural language processing. Unfortunately, the previous state-of-the-art convolutional neural network (CNN) increase model accuracy by increasing the number of layers of the network, which leads to the problem of large models and complex calculations. Previous work has demonstrated that network compression can be achieved by weight pruning approaches, and that specialized hardware can be used to speed up reasoning. However, the format of compressed sparse rows (CSR) or compressed sparse columns (CSC) is generally used in model pruning. The approach leads to coding operations before calculation and decoding operations during calculation by processing units on specialized hardware. And the approach degrades the performance of the *** this paper, we propose a combination of hardware and software to address above bottlenecks. Firstly, we propose a novel software-based structured pruning, called vector-pruning. Secondly, we design a novel dataflow for our pruning method, called Vector-Sparse, our method eliminates complex coding and decoding Then, we design the corresponding hardware architecture for vector-prune. Finally, we evaluate the performance of our pruning approach for AlexNet on Xilinx xcvu19p-fsva3824-2-e. Experiments show that compared with a state-of-the-art convolution network accelerator, we proposed a software/hardware co-design approach that is 1.01x and 1.3x better in performance, effectively improving the inference speed of the model on Field Programmable Gate Array (FPGA).

关键词： Computational modeling Object detection Throughput Hardware Software Encoding Decoding

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Recovering 3D facial shape via coupled 2D/3D space learning

Recovering 3D facial shape via coupled 2D/3D space learning

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International Conference on Automatic Face and Gesture Recognition

作者： Annan Li Shiguang Shan Xilin Chen Xiujuan Chai Wen Gao Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing China System Research Center Nokia Research Center Beijing China Institute of Digital Media Peking University Beijing China

This paper presents a method for recovering 3D facial shape from single image via learning the relationship between the 2D intensity images and the 3D facial shapes. With a coupled training set, the intensity images and their corresponding facial shapes make up two vector spaces respectively. But only the correlated components in both spaces are useful for inference, so there must be embedded hidden subspaces in each space which preserve the inter-space correlation information. Thus by learning the projection onto hidden subspaces based on maximum correlation criteria and optimizing the linear transform between the hidden spaces, 3D facial shape is inferred from the intensity image. The effectiveness of the method is demonstrated on both synthesized and real world data.

关键词： Content addressable storage Humans Face Lighting Light sources Statistics Deformable models Image reconstruction Active shape model Space technology

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HDEC-TFA: An unsupervised learning approach for discovering physical scattering properties of single-polarized SAR image

TechRxiv

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TechRxiv 2020年

作者： Huang, Zhongling Datcu, Mihai Pan, Zongxu Qiu, Xiaolan Lei, Bin Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Wessling82234 Germany

Knowing the physical properties and scattering mechanisms contributes to Synthetic Aperture Radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the’image’ domain. Based on TFA theory, we propose to generate the sub-band scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering algorithm based on time-frequency analysis (HDEC-TFA) is proposed to learn the embedded features and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single-polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen-3 quad-polarized SAR data for experiments and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex-valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications. © 2020, CC BY.

关键词： Synthetic aperture radar

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A hybrid MILP and IPM for dynamic economic dispatch with valve point effect

arXiv

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arXiv 2017年

作者： Pan, Shanshan Jian, Jinbao Yang, Linfeng College of Electrical Engineering Guangxi University Nanning530004 China Guangxi Colleges Universities Key Laboratory Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China College of Computer Electronics and Information Guangxi University Nanning530004 China

Dynamic economic dispatch with valve-point effect (DED-VPE) is a non-convex and non-differentiable optimization problem which is difficult to solve efficiently. In this paper, a hybrid mixed integer linear programming (MILP) and interior point method (IPM), denoted by MILP-IPM, is proposed to solve such a DED-VPE problem, where the complicated transmission loss is also included. Due to the non-differentiable characteristic of DED-VPE, the classical derivative-based optimization methods can not be used any more. With the help of model reformulation, a differentiable non-linear programming (NLP) formulation which can be directly solved by IPM is derived. However, if the DED-VPE is solved by IPM in a single step, the optimization will easily trap in a poor local optima due to its non-convex and multiple local minima characteristics. To exploit a better solution, an MILP method is required to solve the DED-VPE without transmission loss, yielding a good initial point for IPM to improve the quality of the solution. Simulation results demonstrate the validity and effectiveness of the proposed MILP-IPM in solving DED-VPE. Copyright © 2017, The Authors. All rights reserved.

关键词： Wave transmission

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Classification of large-scale high-resolution SAR images with deep transfer learning

arXiv

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arXiv 2020年

作者： Huang, Zhongling Dumitru, Corneliu Octavian Pan, Zongxu Lei, Bin Datcu, Mihai Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Wessling82234 Germany

The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes' problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What's more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%. Copyright © 2020, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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A Multi-Point Two-Hop Neighbor Relay Based OLSR Routing Protocol Design Method for Self-organized Multi-agent Communication*

A Multi-Point Two-Hop Neighbor Relay Based OLSR Routing Prot...

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control Conference (ANZCC), Australian and New Zealand

作者： Zhenlei Wang Chunfeng Li Xiongjun Wu The 54th Research Institute of China Electronics Technology Group Corporation (CETC54) Hebei Province China The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

The OLSR routing protocol is a proactive Linkstate routing protocol in Ad hoc networks. It uses MPR nodes to spread TC messages across the network, reducing the flooding of protocol control messages in the network. However, all MPR nodes used to propagate a TC message in the whole network are not optimal, and there are a lot of redundancy, which results in unnecessary forwarding of TC messages and increases the overhead of control protocol. To overcome this limitation, the concepts of multi-point one hop neighbor relay (MPOHNR) and multi-point two hop neighbor relay (MPTHNR) are proposed, and the node selection algorithms of MPOHNR and MPTHNR are given. By using MPOHNR and MPTHNR to forward TC messages alternately, the number of forwarding nodes is reduced, the control protocol overhead is reduced, the effective bandwidth of the link is greatly increased, and the end-to-end average delay is reduced. Numerical simulations by using the EXATA software verifies that, the improved OLSR routing protocol is a potential design method for optimizing OLSR routing protocol.

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network control Plane Traffic

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Min Wang Peng Li Zhang Cheng Wenmao Liu Lei Nie Haizhou Bao Qin Liu Kai Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China NSFOCUS Technologies Group Co. Ltd School of Cyber Science and Engineering Wuhan University China

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance.

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Engineering-oriented simulation platform for laminar cooling process of hot rolled strips

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International Journal of Simulation and Process Modelling 2015年第4期10卷 327-333页

作者： Pian, Jinxiang Wang, Zhen Zhu, Yunlong Chai, Tianyou Li, Jiejia Faculty of Information and Control Engineering Shenyang Jianzhu University 9 Hunnan East Road Hunnan New District Shenyang Liaoning110168 China Key Laboratory of Networked Control System CAS Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang Liaoning110016 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University No. 3-11 Wenhua Road Heping District Shenyang Liaoning110819 China

Because the new approach cannot be applied directly in the hot-rolled strip laminar cooling process, a simulation is necessary to verify the new approach effectively which can improve the strip quality and production yields. In order to make sure simulation platform is similar to the practical industrial process as much as possible, we proposed an engineering-oriented distributed simulation platform for laminar cooling process, integrating part of physical equipment (such as PLC). Additionally, there is physical signals exchange among the controller and virtual sensors in the platform. The simulation platform consists of four parts, including virtual object, virtual instruments and actuators, loop controller and process monitoring computer, and setting controller. The virtual object reflects the actual input and output characteristics of laminar cooling process. Virtual instruments and actuators can simulate the field instruments and actuators. Loop controller and process monitoring show the device operating conditions, the control performance, and give alarm signals in time. Setting controller performs setting control procedures. The experimental results indicate that simulation platform has powerful ability to evaluate the new approach in light of the engineering, which can reduce the live-fire testing cost and meet the requirements of repeated trials. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： controllers

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A Bilayer Segmentation-Recombination Network for Accurate Segmentation of Overlapping C. elegans

arXiv

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arXiv 2024年

作者： Ding, Mengqian Liu, Jun Luo, Yang Tang, Jinshan Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China AI in Medicine Lab College of Public Health George Mason University FairfaxVA22312 United States

Caenorhabditis elegans (C. elegans) is an excellent model organism because of its short lifespan and high degree of homology with human genes, and it has been widely used in a variety of human health and disease models. However, the segmentation of C. elegans remains challenging due to the following reasons: 1) the activity trajectory of C. elegans is uncontrollable, and multiple nematodes often overlap, resulting in blurred boundaries of C. elegans. This makes it impossible to clearly study the life trajectory of a certain nematode;and 2) in the microscope images of overlapping C. elegans, the translucent tissues at the edges obscure each other, leading to inaccurate boundary segmentation. To solve these problems, a Bilayer Segmentation-Recombination Network (BR-Net) for the segmentation of C. elegans instances is proposed. The network consists of three parts: A Coarse Mask Segmentation Module (CMSM), a Bilayer Segmentation Module (BSM), and a Semantic Consistency Recombination Module (SCRM). The CMSM is used to extract the coarse mask, and we introduce a Unified Attention Module (UAM) in CMSM to make CMSM better aware of nematode instances. The Bilayer Segmentation Module (BSM) segments the aggregated C. elegans into overlapping and non-overlapping regions. This is followed by integration by the SCRM, where semantic consistency regularization is introduced to segment nematode instances more accurately. Finally, the effectiveness of the method is verified on the C. elegans dataset. The experimental results show that BR-Net exhibits good competitiveness and outperforms other recently proposed instance segmentation methods in processing C. elegans occlusion images. Copyright © 2024, The Authors. All rights reserved.

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A dynamic multi-objective evolutionary algorithm based on Niche prediction strategy

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Applied Soft Computing 2023年 142卷

作者： Zheng, Jinhua Zhang, Bo Zou, Juan Yang, Shengxiang Hu, Yaru Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

In reality, many multi-objective optimization problems are dynamic. The Pareto optimal front (PF) or Pareto optimal solution (PS) of these dynamic multi-objective problems (DMOPs) changes as the environment change. Therefore, solving such problems requires an optimization algorithm that can quickly track the PF or PS after an environment change. Prediction-based response mechanism is a common method used to deal with environmental changes, which is commonly known as center point-based prediction. However, if the predicted direction of the center point is inaccurate, the predicted population will be biased towards one side. In this paper, we propose a niche prediction strategy based on center and boundary points (PCPB) to solve the dynamic multi-objective optimization problems, which consists of three steps. After environmental changes are detected, the first step is to divide the niche, dividing different individuals in the PS into different niche populations. The second step is to independently predict different niches, and select individuals with good convergence and distribution in the niche to predict the individuals that will produce the next generation. Finally, some different individuals are randomly generated in the next possible PS area to ensure the diversity of the population. To verify whether our proposed strategy is effective and competitive, PCPB was compared with five state-of-the-art strategies. The experimental results show that PCPB performed competitively in solving dynamic multi-objective optimization problems, which proves that our algorithm has good competitiveness. © 2023 Elsevier B.V.

关键词： Forecasting

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