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Prediction of effluent total phosphorus using PLSR-based adaptive deep belief network

Huagong Xuebao/CIESC Journal 2017年第5期68卷 1987-1997页

作者： Wang, Gongming Li, Wenjing Qiao, Junfei Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

Considered high nonlinearity and large transient variation, a PLSR-adaptive deep belief network (PLSR-ADBN) was proposed for prediction of total phosphorus (TP) in effluent of wastewater treatment process (WWTP). The PLSR-ADBN was an improved DBN, a deep learning model. First, an adaptive learning rate was introduced into the unsupervised pre-training stage of DBN so as to accelerate convergence rate. Secondly, PLSR was used to replace gradient fine-tuning method in conventional DBN for improving prediction accuracy. Meanwhile, a Lyapunov function was constructed to prove convergence of the PLSR-ADBN learning process. Finally, the proposed PLSR-ADBN was applied to an actual TP prediction in WWTP. The experimental results show that the method has a fast convergence rate and a high prediction accuracy, which can meet the demands for TP detection accuracy and WWTP operating efficiency. © All Right Reserved.

关键词： Forecasting

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Knowledge-based intelligent Optimal Control for Wastewater Biochemical Treatment Process

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Zidonghua Xuebao/Acta Automatica Sinica 2017年第6期43卷 1038-1046页

作者： Qiao, Jun-Fei Han, Gai-Tang Zhou, Hong-Biao Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

In order to solve the problems of excessive energy consumption and serious water quality in wastewater treatment process, a wastewater treatment process intelligent optimization control method based on knowledge is proposed. Knowledge model of environment variable parameters and optimal solutions are built by memorizing the dynamic processing information of the multi-objective intelligent optimization algorithm. The optimization algorithm is guided by the non-dominated solution in the knowledge base, and combines the oriented local area search and the stochastic global search strategy to improve the convergence of the algorithm and obtains a higher quality solution. Finally, experiment verification is performed on the international common simulation platform BSM1. Results show that the proposed method can reduce energy consumption under the premise of ensuring the quality of the effluent. Copyright © 2017 Acta Automatica Sinica. All rights reserved.

关键词： Energy utilization

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Multiple Demonstration of Trajectory Imitation Learning Based on Multi Constrained Optimization Method

Multiple Demonstration of Trajectory Imitation Learning Base...

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IEEE International Conference on Cyber Technology in Automation, Control, and intelligent systems

作者： Jianjun Yu Yijia Zheng Xiaogang Ruan Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781538604915

On the basis of trajectory imitation learning, aiming at the problem of the poor learning effect caused by the fluctuations of the single demonstration data, this paper presents a kind of imitation learning method based on multiple demonstrations. The multi constraint optimization idea is introduced for trajectory optimization. Multiple demonstrations data are coded by GMM separately, and the probability interval parameters are set as the optimization constraints. The intersection of the constraint conditions is to obtain the interval overlap probability. The probability of superior data is higher, and the probability of inferior data is lower. Parameter's set for multi constrained optimal trajectory are solved based on Gaussian distribution product form, and then obtain a multi constrained optimal trajectory through GMR. This method reduces the probability of bad data in the demonstration. It effectively avoids the fluctuation of the reproduction trajectory caused by the inferior demonstration data. The simulation results show the effectiveness of the method.

关键词： Education Gaussian distribution Indexes Robots Trajectory optimization

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New Big Data Collecting Method Based on Compressive Sensing in WSN

New Big Data Collecting Method Based on Compressive Sensing ...

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International Conference on Computer Communications and Networks (ICCCN)

作者： De-gan Zhang Xiao-hua Liu Yu-ya Cui Hong-tao Peng School of Computer Science &Engineering Tianjin University of Technology Tianjin China Computing & Novel software Technology Tianjin Key Lab of Intelligent Tianjin China Ministry of Education Key Laboratory of Computer Vision and System(TJUT) Tianjin China National Petroleum Corporation (CNPC) Managers Training Institute Beijing China

Considered the wireless sensor network clustering structure, a new big data collecting method based on compressive sensing is proposed. The collection process is as follows: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network, and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector, and collect data through compressive sensing technology; Among clusters, clusters forward measurement values to sink node along multi-hop routing tree which we built before. Performance analyzing and comparison of results show that this method is superior to other methods regardless of in a cluster or inter-cluster.

关键词： Wireless sensor networks Compressed sensing Sparse matrices Big Data Routing Energy consumption Training

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Self-triggered control of heterogeneous multiagent systems with input saturation

Self-triggered control of heterogeneous multiagent systems w...

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Chinese Control Conference (CCC)

作者： Shengli Du Di Wu Yongfeng Gao Xu Li Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing P. R. China School of Control Science and Engineering Dalian University of Technology Dalian P. R. China School of Aeronautics and Astronautics Dalian University of Technology Dalian P. R. China

This paper is concerned with the semi-global output synchronization problem of a heterogeneous network under self-triggered control. A distributed self-triggered control scheme in which only local information is used to compute the triggering instants is proposed. In addition, the Zeno behavior can be excluded in the designed scheme. By adopting the low-gain technique, it is shown that the input saturation nonlinearity can be avoided if the parameter of the parameterized feedback gain is chosen small enough. The algorithm for implementing the self-triggered rule is also presented in the paper.

关键词： Multi-agent systems Synchronization Eigenvalues and eigenfunctions Control systems Laplace equations Symmetric matrices Riccati equations

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Compression of Conditional Deep Learning Network for Fast and Low Power Mobile Applications

Compression of Conditional Deep Learning Network for Fast an...

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2017 the 2nd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2017)

作者： Lijie Li Yan Zhang Pengfei Wang Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology

ISBN: (纸本)9781510840683

CDLN(Conditional Deep Learning Network)is a structure of convolution neural network with multiple *** could improve the speed for the task of classification while the module of the network is still too large for mobile *** address this issue,a method for compressing CDLN,which is named one-shot whole network compression *** the experiments,the module size and time cost are significantly reduced while the accuracy of the network losses a little.

关键词： CDLN one-shot whole network compression scheme module size

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Prediction of effluent total phosphorus based on self-organizing fuzzy neural network

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Kongzhi Lilun Yu Yingyong/Control Theory and Applications 2017年第2期34卷 224-232页

作者： Qiao, Jun-Fei Zhou, Hong-Biao Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China Faculty of Automation Huaiyin Institute of Technology Huai'anJiangsu223003 China

A novel online self-organizing fuzzy neural network (FNN) based on the improved Levenberg-Marquardt (ILM) learning algorithm and singular value decomposition (SVD) is proposed to predict the effluent total phosphorus (TP) in a wastewater treatment process. The centers and widths of membership functions and weights of output layer are trained by ILM learning algorithm. Meanwhile, the output matrix of the rule layer is decomposed with SVD, which is implemented by one-sided Jacobi's transformation. The neurons of rule layer are adjusted dynamically with growing and pruning algorithms, which are based on the singular values. In addition, the convergence of the proposed ILM--SVDFNN has been proved both in the structure fixed phase and the structure adjusting phase. Finally, the validity and practicability of the model are illustrated with three examples, including typical nonlinear system identification, Mackey-Glass time series prediction, and prediction of effluent TP. Simulation results demonstrate that the proposed ILM--SVDFNN generates a fuzzy neural network automatically and effectively with a highly accurate and compact structure, and it can well satisfy the detection accuracy and real-time requirements of the prediction of effluent TP. ©2017, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

关键词： Effluents

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Semantic Segmentation Based on Deep Convolution Neural Network

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Jichao Shan Xiuzhi Li Songmin Jia Xiangyin Zhang Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China.

Semantic segmentation using full convolutional neural network (FCN) avoids the problems of repeated calculation and storage due to using of pixel blocks. However, the results obtained by FCN are still not precise enough, and the results of upsampling are still relatively fuzzy and smooth. It is not sensitive to the details such as small object in the image. Therefore, this paper proposes an image segmentation method based on simplified deep residual network. A simplified Deep Residual Network (DRN) is proposed to replace the VGG-16 network in the original FCN framework. The net combines deep residual network architecture with 30% lesser parameters than the VGG model and the method of skip connection that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentation results. The results showed that the effect of segmentation is more refined and the recognition and segmentation of small objects are significantly improved by the improved network model.

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An Improved Levenberg-Marquardt Algorithm with Adaptive Learning Rate for RBF Neural Network 35

An Improved Levenberg-Marquardt Algorithm with Adaptive Lear...

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第35届中国控制会议

作者： AN Ru LI Wen Jing Han Hong Gui QIAO Jun Fei College of Electronic and Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781509009107

In this paper,an improved Levenberg-Marquardt(LM) algorithm with adaptive learning rate is proposed to optimize the learning process of RBF neural ***,an improved LM algorithm is adopted using a quasi-Hessian matrix and gradient vector which are computed *** with the conventional LM algorithm,Jacobian matrix multiplication and storage are not required in the improved LM algorithm,which can reduce computation cost and solve the problem of memory ***,the adaptive learning rate is integrated into the improved LM algorithm in order to accelerate the convergence speed of training algorithm and improve the network performance of nonlinear system ***,several experiments are conducted and the results show that the proposed method has faster convergence speed and better prediction performance.

关键词： Improved LM algorithm RBF neural network adaptive learning rate fast convergence speed

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