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Chinese Automation Congress (CAC)

作者： Jingjian Yang Yuning Amy Xie Hongyan Ma College of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China The Madeira School McLean VA United States Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Accompanying the constant development of data mining technology and neural network, the application of computer technology in energy consumption prediction of large public buildings is the trend of green buildings and intelligent life in the field of engineering. In this paper, a new energy consumption prediction algorithm based on MLR-BP is proposed, which solves the issue of current energy consumption algorithm: the massive amount of calculation and the low prediction accuracy. Firstly, factors that affect building energy consumption are categorized as external factors (meteorological factors) and internal factors. In addition, the multiple linear regression is used to model the external factors, and the influencing factors with high correlation are screened out. Then, the selected external factors and internal factors are combined into the training of BP neural network to obtain a new energy consumption prediction model. The simulation results show that in comparison with the BP neural network, the proposed prediction algorithm in this paper can predict the building energy consumption more accurately.

关键词： Energy consumption Buildings Neural networks Predictive models Temperature Linear regression Mathematical model

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Improved VGG Model for Road Traffic Sign Recognition

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Computers, Materials & Continua 2018年第10期57卷 11-24页

作者： Shuren Zhou Wenlong Liang Junguo Li Jeong-Uk Kim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science&TechnologyChangsha410114China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Energy Grid Sangmyung UniversitySeoul110743Korea

Road traffic sign recognition is an important task in intelligent transportation *** neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign *** this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural *** natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG *** IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training *** paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification *** uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the *** IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.

关键词： Intelligent transportation traffic sign deep learning GTSRB data augmentation

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Underwater Mixed Spatial Attention Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Qiang Pan Yueting Yang Chuansheng Yang Chao Wang Anhui Tan School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China

High-quality underwater images play an important role in obtaining and understanding underwater information. However, raw underwater images have many problems, such as low contrast, chromatic aberration, blur and low light, which seriously restrict the progress of other underwater tasks. In this paper, we propose an Underwater Mixed Spatial Attention Network (UMSAN) for underwater image enhancement. The evaluation on the EUVP dataset and some other real underwater images demonstrates that our network is sufficient against several of the advanced models.

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Air Quality Inference with Deep Convolutional Conditional Random Field

Air Quality Inference with Deep Convolutional Conditional Ra...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Zhe Luo You Yu Daijun Zhang Shijie Feng Huapeng Yu Yongxin Chang Wei Shen School of Electrical Engineering and Information Southwest Petroleum University Chengdu China College of Environment and Ecology Chongqing University Big Data Application Center of Ecology and Environment of Chongqing Chongqing China College of Environment and Ecology Chongqing University State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing China College of Information Science and Engineering Chengdu University Chengdu China Chongqing Zhixing Shulian Intelligent Technology Co. Ltd Chongqing China

ISBN: (数字)9781728166612

ISBN: (纸本)9781728112350

Conditional Random Field is a discriminative model for time series data. In this paper, we propose an improved CRF and apply it to the task of air quality inference. Different from the classical CRF, our linear chain CRF is a supervised learning based on the deep convolution neural network, which has a strong learning ability and fast processing speed for the engineering big data. Specifically, we model the state feature function and the state transition feature function with deep convolutional neural network. The parameter space can store more feature expressions learned from a large number of data. For the state transition feature function of linear conditional random field, we add the influence of input sequence on this function. Through the modelling and learning both vertex features and edge features from data, we obtain a more powerful and more efficient CRF. Experiments on natural language and air quality data show our CRF can achieve higher accuracy.

关键词： Air quality Convolution Neural networks Indexes Electronic mail data models Biological system modeling

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The Redundancy Problem in Composition of Parallel Finite Automata and Its Optimization Method 4th

The Redundancy Problem in Composition of Parallel Finite Aut...

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4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Tang, Zheng-Yi Wang, Jin-Shui Chen, Yi Xue, Xing-Si School of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications FuzhouFujian350118 China

ISBN: (纸本)9783319685267

Parallel finite automata is a formal model which can describe the complex control structure, but its composition model may exist redundant elements. This problem will increase the complexity of model. This paper researched the redundancy problem in the Split-Join composition of parallel finite automata and gave the condition which lead to produce redundant location and transfer. Furthermore, the algorithms which are used to eliminate the redundant location and transfer also was given. Finally, it proved the equivalence of parallel finite automata and finite automata. This conclusion ensures that he verification technology of system which base on the finite automata can be used to parallel finite automata by extension. © 2018, Springer International Publishing AG.

关键词： Finite automata

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Simplification of 3D City Model with CityGML 22

Simplification of 3D City Model with CityGML

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22nd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018

作者： Huang, Baoyu Mao, Bo College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance Economics Nanjing China

ISBN: (纸本)9781538614822

Many applications demand simplified 3D building models for fast visualization and analysis purposes. However, due to the huge spatial data, it intensifies the load of the computer in the visualization process, and the redundancy of the data also affects the user experience. So in order to solve the above problems, this paper presents a method of 3D city model simplification, it can preserve the location and the overall model shape using the semantic filtering and edge search combining to simplify the model, reduce data redundancy, extracting model of shell. The proposed method mainly uses the semantic filtering to extract the wall and roof structure, and remove the doors and windows, then through the proposed adjacent search algorithm to extract the model to simplify the removal of the thickness of the wall and roof, leaving only the outermost model. The approach has been implemented and tested on a number of 3D buildings. Experiments show that the simplified model, when there is no significant difference in the visualization of the case, by calculating, the simplified model is compared with the original model, reducing the number of about ninety-seven percent, the size of the model is reduced to about two percent of the original. © 2018 IEEE.

关键词： Redundancy

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On efficient and robust metrics for RANSAC hypotheses and 3D rigid registration

arXiv

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arXiv 2020年

作者： Yang, Jiaqi Huang, Zhiqiang Quan, Siwen Zhang, Qian Zhang, Yanning Cao, Zhiguo National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University Xi’an710129 China School of Electronic and Control Engineering Chang’an University Xi’an710064 China School of Resources and Environment Hubei University Wuhan430062 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a de-facto choice to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration in overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that the proposed metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Deterioration

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The Second-order h-type Indicators for Identifying Top Units

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data and Information Management 2018年第1期2卷 49-56页

作者： Ye, Fred Y. Bornmann, Lutz School of Information Management Nanjing University 210023 Nanjing China Jiangsu Key Laboratory of Data Engineering and Knowledge Service 210023 Nanjing China Administrative Headquarters of the Max Planck Society Division for Science and Innovation Studies Hofgartenstr. 8 D-80539 Munich Germany

The second-order h-type indicators are suggested to identify top units in scientometrics. Basically, the re-ranking of h-type series leads to the second-order h-type indicator. The second-order h-type indicators provide an interesting and natural method to identify top units, yielding fixed h-top. Differentiating from the series of artificially defined highly cited percentile classes, the h-top contributes a natural definite top in the series of highly cited classes. When studying theoretically, the second-order h-index concerns 3% of the h-top whereas the first-order h-index refers to 10% of the h-core. The ratio of the first- and second-order h-index, hT/h, is 30%. When studying empirically, the ratio of the first- and second-order h-index, hT/h, is © 2018 © 2018 Fred Y. Ye, Lutz Bornmann, published by Sciendo

关键词： Indexing (of information)

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Hyperspectral image classification with attention aided CNNs

arXiv

引用

arXiv 2020年

作者： Hang, Renlong Li, Zhu Liu, Qingshan Ghamisi, Pedram Bhattacharyya, Shuvra S. School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer Science and Electrical Engineering University of Missouri Kansas CityMO64110 United States Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Exploration FreibergD-09599 Germany Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States

Convolutional neural networks (CNNs) have been widely used for hyperspectral image classification. As a common process, small cubes are firstly cropped from the hyperspectral image and then fed into CNNs to extract spectral and spatial features. It is well known that different spectral bands and spatial positions in the cubes have different discriminative abilities. If fully explored, this prior information will help improve the learning capacity of CNNs. Along this direction, we propose an attention aided CNN model for spectral-spatial classification of hyperspectral images. Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively. Both of them are based on the traditional CNN model, and incorporate attention modules to aid networks focus on more discriminative channels or positions. In the final classification phase, the spectral classification result and the spatial classification result are combined together via an adaptively weighted summation method. To evaluate the effectiveness of the proposed model, we conduct experiments on three standard hyperspectral datasets. The experimental results show that the proposed model can achieve superior performance compared to several state-of-the-art CNN-related models. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Sledge: Towards Efficient Live Migration of Docker Containers

Sledge: Towards Efficient Live Migration of Docker Container...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： Cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

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