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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： complex networks

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To enhance power distribution network management of local power service enterprise by using cloud platform

To enhance power distribution network management of local po...

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International Conference on Information science and technology (ICIST)

作者： Gang Xiong Timo R. Nyberg Pekka Hämäläinen Xisong Dong Yuan Liu Jiachen Hou Cloud Computing Center Chinese Academy of Sciences Dongguan CHINA Department of Industrial Engineering and Management Aalto University Aalto FINLAND Tekla Corporations Metsänpojankuja Espoo FINLAND Key Laboratory of Complex System Intelligent Science Chinese Academy of Sciences Beijing CHINA Nanjing Smart Energy Information Technology Co. LTD Nanjing CHINA Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing CHINA

The availability of new technologies in the areas of digital electronics, communications, internet, and computer technologies opens a door to build a smart grid, which can increase significantly the capacity, services and intelligence of power systems. This paper proposes a power distribution network management cloud platform based on Tekla Xpower. Proposed system can support the implementation of current power grid and/or the coming smart grid. Utilizing the existing computing and storage installations, cloud platform can integrate the existing resources to improve the computation and storage capacities, the data security of the entire system, and can increase application intelligence, service quality and decision-making capability as well. The four layer architecture of cloud platform is designed, and a pilot case study for DMS is provided.

关键词： Computer network reliability Reliability Databases Business Electronic mail Handheld computers

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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A Co Concentration Prediction Method for Electronic Nose Based on Trellisnet with Gated Recurrent Unit and Dilated Convolution

SSRN

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SSRN 2023年

作者： Zhu, Zhengyang Jiang, Qingming Wang, Mingxiang Xu, Min Zhang, Yiyi Shuang, Feng Jia, Pengfei School of Electrical Engineering Guangxi University Nanning China College of Food Science and Bioengineering Xihua University Chengdu China Guangxi Key Laboratory of Power System Optimization and Energy Technology China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi University Nanning China

As one of the harmful substances in the atmosphere, carbon monoxide (CO) is harmful to human beings. With the wide application of gas sensors and machine learning algorithms, the accuracy of concentration predicting of various gas such as CO is constantly improving. Now we apply TrellisNet, a network utilizing both recurrent and convolutional techniques, to gas concentration prediction, which is also a time-series prediction task, with the aim of improving its performance. To enhance TrellisNet’s ability to retain long time series information, we replaced the activation units in each layer of the model with GRU. Compared to using LSTM as the activation unit, our approach has lower computational complexity and offers a more stable model. Additionally, we introduced dilated convolutions in each layer, allowing the model to establish connections with as many past time steps as possible at a given time point, even with fewer layers. This further enhances the preservation of long time series information. We named our improved technique trellis convolutional dilated network (TrelliSense). Due to the injection of the same input values in each layer, TrelliSense also exhibits superior training stability. Experimental results demonstrate that TrelliSense outperforms other time prediction networks, including TCN, LSTM, and GRU, in terms of all error metrics (MAE, RMSE, SMAPE). Therefore, we argue that TrelliSense is a better method for predicting CO concentration. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Machine-learning-enabled vectorial opto-magnetization orientation

arXiv

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arXiv 2021年

作者： Yan, Weichao Nie, Zhongquan Zeng, Xunwen Dai, Guohong Cai, Mengqiang Shen, Yun Deng, Xiaohua Institute of Space Science and Technology Nanchang University Nanchang330031 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan030024 China Department of Physics School of Science Nanchang University Nanchang330031 China Information Engineering School Nanchang University Nanchang330031 China

Manipulation of light-induced magnetization has become a fundamentally hot topic with a potentially high impact for atom trapping, confocal and magnetic resonance microscopy, and data storage. The control of the magnetization orientation mainly relies on the direct methods composed of amplitude, phase and polarization modulations of the incident light under the tight focusing condition, leaving the achievement of arbitrary desirable three-dimensional (3D) magnetization orientation complicated, inflexible and inefficient. Here, we propose a facile approach called machine learning inverse design to achieve expected vectorial opto-magnetization orientation. This pathway is time-efficient and accurate to produce the demanded incident beam for arbitrary prescribed 3D magnetization orientation. It is highlighted that the machine learning method is not only applied for magnetization orientations, but also widely used in the control of magnetization structures. © 2021, CC BY.

关键词： Polarization

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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Pinning noise-induced stochastic resonance

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Physical Review E 2013年第6期87卷 062920-062920页

作者： Yang Tang Huijun Gao Wei Zou Jürgen Kurths Institute of Physics Humboldt University Berlin Berlin D-12489 Germany Potsdam Institute for Climate Impact Research Telegraphenberg Potsdam D-14415 Germany Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150080 China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen AB24 3FX Scotland United Kingdom

This paper proposes the concept of pinning noise and then investigates the phenomenon of stochastic resonance of coupled complex systems driven by pinning noise, where the noise has an α-stable distribution. Two kinds of pinning noise are taken into account: partial noise and switching noise. In particular, we establish a connection between switching noise and global noise when Gaussian noise is considered. It is shown that switching noise can not only achieve a stronger resonance effect, but it is also more robust to induce the resonance effect than partial noise.

关键词： NOISE STOCHASTIC resonance RANDOM noise theory COUPLINGS (Gearing) PHYSICS MATHEMATICS

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Global context aware RCNN for object detection

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Xie, Haoyu Zhu, Mai Tie, Ming Chen, Junxin School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Science and Technology on Space Physics Laboratory Beijing100076 China College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China

RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map. However, these cropped feature maps of local receptive fields will heavily lose global context information. To tackle this problem, we propose a novel end-to-end trainable framework, called Global Context Aware (GCA) RCNN, aiming at assisting the neural network in strengthening the spatial correlation between the background and the foreground by fusing global context information. The core component of our GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively. Specifically, we leverage the dense connection to improve the information flow of the global context at different stages in the top-down process of FPN, and further use the attention mechanism to refine the global context at each level in the feature pyramid. In the end, we also present a lightweight version of our method, which only slightly increases model complexity and computational burden. Experimental results on COCO benchmark dataset demonstrate the significant advantages of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Stabilizing oscillation death by multicomponent coupling with mismatched delays

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Physical Review E 2012年第3期86卷 036210-036210页

作者： Wei Zou D. V. Senthilkumar Yang Tang Jürgen Kurths School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Institute of Physics Humboldt University Berlin Berlin D-12489 Germany Potsdam Institute for Climate Impact Research Telegraphenberg Potsdam D-14415 Germany Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150080 China Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen AB24 3FX United Kingdom

The dynamics of a symmetric network of oscillators that are mutually coupled via multiple dynamical components with mismatched delays is studied. We find that networked oscillators experience oscillation death (OD) over a much larger domain of parameters when their different dynamical components are linked with mismatched delays than with only one delay. In particular, if the delays are mismatched by retaining a certain bias, OD is proved to be linearly stable even for very large delays for an arbitrary symmetric network. Further, we show that the minimal value of the intrinsic frequency necessary to induce OD decreases as the degree of mismatch in the coupling delays increases. The stabilizing effect of multicomponent coupling with mismatched delays is shown to be valid in networked chaotic oscillators also. The proposed coupling strategy can possibly be applied in controlling several pathological activities in neuronal systems and in engineering applications.

关键词： TIME delay systems ELECTRIC oscillators PARAMETER estimation intelligent control systems ANALYTICAL mechanics STABILITY theory

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