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2nd International Seminar on Computer Science and engineering technology, SCSET 2020

作者： Guo, Changjian Wu, Xiaoxuan School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University Hefei China

WSN(Wireless Sensor Network) is intended for obtaining sensory data so that the query processing technology of high efficiency is the precondition of widely used WSN. Existing query processing technology assumes that the WSN deployed in two-dimensional or three-dimensional free space are not suitable for those deployed in such limited circumstances as roads, pipelines and indoor environments. This paper explores the problems of existing query processing technology, does research on perception data query processing method and technology, and seeks out the existing problem and its solution by means of using the WSN actually deployed in a building as an experimental platform. © Published under licence by IOP Publishing Ltd.

关键词： Query processing

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Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting data Augmentation Method

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Computers, Materials & Continua 2024年第4期79卷 927-941页

作者： Le Zou Linsong Hu Yifan Wang Zhize Wu Xiaofeng Wang Anhui Provincial Engineering Laboratory of Big Data Technology Application forUrban Infrastructure School ofArtificial Intelligence and Big DataHefei UniversityHefei230601China Institute of Applied Optimization School of Artificial Intelligence and Big DataHefei UniversityHefei230601China

Depth estimation is an important task in computer *** data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth ***,data augmentation is crucial for this *** data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge *** this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth *** method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge *** validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation ***,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed *** results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data *** the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to *** the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

关键词： Perpendicular depth estimation data augmentation

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Research on Leak Location Method of Water Supply Network based on Deep Neural Network Model 3

Research on Leak Location Method of Water Supply Network bas...

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3rd International Seminar on Computer Science and engineering technology, SCSET 2022

作者： Wu, Xiaoxuan Zhang, Chen Hefei University School of Artificial Intelligence and Big Data Hefei China Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University Hefei China

ISBN: (纸本)9781665478762

The water supply network is one of the important infrastructure in urban construction. It has strong theoretical and practical significance to realize the real-time monitoring and leak location of the water supply network. In this paper, based on the similarity of water supply network node pressure, fuzzy C-means clustering algorithm is used to realize the selection of finite monitoring points. On this basis, a depth neural network model is constructed according to the pressure changes of the monitoring points before and after the leakage of the water supply network, so as to locate the leakage points. In the experimental part, hydraulics simulation was conducted by using EPANETH pipe network adjustment software according to the layout structure of water supply network, and the pressure of all nodes was obtained. A deep neural network model was established by Keras in Tensorflow framework. After model training and testing, the training error was controlled within the effective range of 5 %. Finally, the model is applied to the actual leakage problem of underground water supply network in Langxi County of Xuancheng City, and the accurate location of the leakage point is realized. The experiment proves the feasibility and accuracy of the method proposed in this paper. © 2022 IEEE.

关键词： Neural network models

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Fan sound fault detection algorithm based on the fusion of convolutional neural network and gated recurrent unit network 6

Fan sound fault detection algorithm based on the fusion of c...

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6th International Conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2023

作者： Qiu, Feng Chen, Shengbing Jin, Dou Lu, Anqi School of Artificial Intelligence and Big Data Hefei University Hefei230601 China Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University Hefei230601 China

ISBN: (纸本)9781510668249

Fans are the important equipment in the manufacturing industry, which are easy to cause faults because of the complex working conditions and high speed. Various fault detection algorithms based on electrical, vibration and video have been proposed, but these algorithms are difficult to meet the detection requirements of enterprises. In this paper, a novel fan fault detection algorithm is presented based on sound signal detection technology. According to the different characteristics of the fan sound signal in the time and frequency domains, we combined the convolutional neural networks (CNN, which is sensitive to spatial information) and the gated recurrent unit (GRU, which is sensitive to timing information), and proposed a fan sound fault detection algorithm. Firstly, according to the variation characteristics of the acoustic signal in different states, the spectrogram of sound signal containing spatial and temporal characteristics is extracted, and the histogram equalization is used to enhance the spectrogram. On this basis, CNN and GRU are combined to train the Log-Fbank spectrogram respectively, so that it can better model the different characteristics of time domain and frequency domain information, so as to obtain a fan state model with high confidence and realize the sound fault detection of the fan. Experiments on MIMII dataset show that the algorithm has high fault recognition accuracy and fast convergence speed, which has good application value. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Fault detection

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Leakage Location Method of Water Supply Pipe Network Based on Integrated Neural Network

Leakage Location Method of Water Supply Pipe Network Based o...

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2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Wu, Xiaoxuan Zhang, Chen Hefei University School of Artificial Intelligence and Big Data Hefei230601 China Hefei University Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei230601 China

ISBN: (纸本)9781665409025

Concerning the problem that selecting the monitoring points and locating the leakage points accurate in the water supply network, a method of locating leakage points based on integrated neural network model is proposed. Based on the similarity of water supply pipe network node pressure, a fuzzy C-means clustering fusion algorithm (FCMCF) was used to select the limited monitoring points, instead of randomly selecting monitoring points;on this basis, according to the distribution structure of water supply pipe network, the hydraulic simulation was carried out by using EPANET pipe network adjustment software, and the water pressure of all nodes before and after leakage was obtained 24hours, the integrated neural network combining the convolutional neural network (CNN) and long and short time memory network (LSTM) was used to locate the leakage points of water supply pipe network. In the experimental part, the application of a model LangXi County Xuan City, an area of underground water supply pipe network leakage problem, through FCMCF selected five monitoring stations, using EPANET software simulate the 396 kinds of fault state, through the integrated neural network model training and testing, finally realizes the accurate positioning of the leak, model accuracy reached 90.25% and loss rate of the model is up to 20.24%. Compared with the empirical mode decomposition (EMD) combined with LSTM structure, the accuracy of the model is significantly improved, while the loss rate is significantly reduced. The experimental results show that the leakage location method of water supply pipe network based on integrated neural network can effectively solve the problem of actual leakage location. © 2022 IEEE.

关键词： Water supply

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The Research on Clustering Ensembles Selection Algorithm based on Semi-supervised K-means Clustering 2

The Research on Clustering Ensembles Selection Algorithm bas...

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2nd International Seminar on Computer Science and engineering technology, SCSET 2020

作者： Wu, Xiaoxuan Guo, Changjian Hu, Tanglei School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University Hefei China Institute of Business Intelligence Hefei University of Technology Hefei China

Selective clustering ensemble algorithm can eliminate the inferior quality clustering member's influence and can achieve a better clustering solution relative to the clustering ensemble algorithm. For high dimensional data clustering, in this paper, a novel selective ensemble algorithm based on semi-supervised K-means clustering is proposed. In this paper, through a large number of experiments to verify the validity of the proposed algorithm for dealing with high dimensional data clustering. The new algorithm can achieve statistically significant performance improvement over other clustering algorithms. © Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

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Prediction of Chemical Oxygen Demand in Sewage Based on Support Vector Machine and Neural Network 1

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Zhou, Jian Huang, Qian-Jing Wang, Xiao-Feng Zou, Le Department of Environmental Engineering Hefei University HefeiAnhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China

ISBN: (数字)9783030267636

ISBN: (纸本)9783030267629

Aiming at the problem that the detection accuracy of effluent COD (chemical oxygen demand) in sewage treatment needs to be further improved, a combined model based on support vector machine and neural network is proposed to predict effluent COD. It can reduce the influence of local optimum on the global scope so as to improve the accuracy of prediction. Firstly, the sample data are divided into two categories by support vector machine. Then the BP neural network model and the Echo State Network (ESN) model are established on two sub-samples respectively. Compared with single neural network model, the mean absolute error and root mean square error of combined model are both reduced. Besides, the proposed model has better comprehensive prediction performance and can meet the actual demand of effluent COD prediction in sewage treatment. © 2019, Springer Nature Switzerland AG.

关键词： Support vector machines

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Lightweight Neural Network Based Garbage Image Classification Using a Deep Mutual Learning 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Liu, Xiao Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Xu, Li-Xiang Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui230601 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

With the construction and development of civilized cities, image based garbage classification has gradually become an important concern in computer vision community. During the algorithms for image classification, the strong ability of Convolution Neural Networks (CNNs) in feature learning makes it the most successful approach at the moment. However, the parameters of CNNs model are very huge, and its training usually depends on a large amount of samples. In this article, we tackle the problem of lightweight neural network based garbage image classification, which aims to learn classifier with a small number of model parameters. Specifically, we utilize the MobileNetV2 for the backbone of feature extraction network and jointly train such two nets in a way of deep mutual learning. It realizes the information distillation between the teacher and the student. With this, we can significantly improve the learning ability of the MobileNetV2 based lightweight neural network. The experimental results on a self-assembled dataset show that our proposal effectively classifies the garbage and achieves a classification effect batter than the state of the arts in terms of testing accuracy, time and model size. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Household Garbage Classification: A Transfer Learning Based Method and a Benchmark 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Cheng, Qian Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Li, Huan-Yi Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui23060 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

Household garbage images are highly diverse in color, texture and geometry, which poses significant challenges to garbage classification. Deep convolutional neural network (DCNN) have recently achieved remarkable progress due to their ability to learn high-level feature representations. It usually requires a large number of labelled image data for training a DCNN model. However, there are few public and mature data sets concerned on household garbage images. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for household garbage image classification. This data set is called 30 Types of Household Garbage Images (HGI-30), which contains 6′000 images of 30 household garbage types, with complex backgrounds, different resolutions, and complicated variations in sample, pose, illumination and background. The publicly available HGI-30 data set allows researchers to develop more accurate and robust methods for both household garbage image processing and interpretation analysis of household garbage object. We further study the classification problem on this data set and propose a transfer learning based method, also provide a performance analysis, which serves as baseline result on this benchmark. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Industrial Smoke Image Segmentation Based on a New Algorithm of Cross-Entropy Model 16th

Industrial Smoke Image Segmentation Based on a New Algorithm...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Huang, Qian-Jing Zou, Le Wu, Zhi-Ze Li, Huan-Yi Wang, Xiao-Feng Chen, Yan-Ping Department of Environmental Engineering Hefei University HefeiAnhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University HefeiAnhui230601 China

ISBN: (纸本)9783030607982

Smoke segmentation from the industrial images is a key concern of environmental monitoring. As the similarities between the gray value of the background and the smoke, the existing segmentation algorithms are difficult to accurately segment the target smoke. In this paper, we construct a cross-entropy based industrial smoke image segmentation by integrating the iterative convolution-thresholding. Specially, we use the iterative convolution-thresholding to implicitly represent the interface of each image domain through a characteristic function. We further perform the combination of a regularization term and a fidelity term in the cross-entropy model. In the proposed algorithm, the fidelity term is first converted into the product of the characteristic function and the cross-entropy function. Then the functional of the characteristic function is used to obtain the regularization term by the approach of thermonuclear convolution approximation. The experimental results demonstrate that our proposal has a more accurate segmentation effect and higher segmentation efficiency. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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