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第37届中国控制会议

作者： Limin Quan Xudong Ye Cuili Yang Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Huludao Power Co. Ltd.Liaoning Power Co. Ltd.

Due to the complex dynamic behavior in wastewater treatment process, online measurement of ammonia nitrogen value is very difficult. In this paper, a case-based reasoning（CBR） prediction model based on a feedforward neural network（FNN） is introduced to predict the effluent ammonia nitrogen value. First, easily measured feature variables which have great effect on effluent ammonia nitrogen value were selected. Next, the prediction model was established, and attribute weights in case retrieval were determined by the connection weights of a trained ***, based on the data in a real wastewater treatment process, simulation experiments were carried out. The results show that the prediction model using FNN-based CBR is effective and has better prediction accuracy than some other methods.

关键词： effluent ammonia nitrogen case-based reasoning feedforward neural network wastewater treatment process

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Auto-LEE: A novel autonomous lower extremity exoskeleton for walking assistance

Auto-LEE: A novel autonomous lower extremity exoskeleton for...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： He, Yong Wang, Chao Li, Nan Fu, Ruiqing Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781538680698

Wearable exoskeletons have been proven to be efficacious in aiding walking for individuals suffering from lower limb mobility disorder. However, the application of most existing devices is limited to inconvenience of usage, e.g., complicated training and unnatural gait. This paper presents a novel autonomous lower extremity exoskeleton, Auto-LEE, for the purpose of improving the practicality of walking assistive devices as well as simplifying their application process. The developed exoskeleton consists of two robotic legs, and each of them has 5 active degrees-of-freedom (DOFs) to independently control the rotations of hip, knee and ankle joints in the sagittal and coronal planes, which enables the device to possess self-balancing ability and flexible gait. The modular design concept is introduced into the structure and hardware development of Auto-LEE, making it more convenient to be assembled and maintained. In order to validate the self-balancing walking ability, virtual prototype simulation and preliminary experiment on flat terrain are implemented. © 2018 IEEE.

关键词： Exoskeleton (Robotics)

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Temperature Prediction and Evaluation of Mill Based on Neural Network

Temperature Prediction and Evaluation of Mill Based on Neura...

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第30届中国控制与决策会议

作者： Jie Gao Xiaoli Li Yang Li Shiqi Shen Faculty of Information Technology Beijing University of Technology Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Communication University of China

The inlet and outlet temperature of the mill is an important index in the process of slag powder production. The specific technological process of the production of slag powder is introduced, and the factors that influence the inlet and outlet temperature of the mill are analyzed. The actual data of production process is preprocessed by the Pauta criterion. The forecasting model of inlet and outlet temperature of mill is established to predict the temperature in an hour by using the basic BP neural network and BP neural network optimized by mind evolutionary algorithm. Through the simulation of Matlab software, the prediction effect diagram, prediction error chart, prediction mean square error, mean absolute error, mean absolute percentage error, and coefficient of decision of the two algorithms are compared. The results show that the BP neural network optimized by mind evolutionary algorithm is better than the original BP neural network in the prediction accuracy.

关键词： temperature of mill Pauta criterion mind evolutionary algorithm BP neural network prediction effect

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A New Method of Bearing Fault Diagnosis Based on LMD and Wavelet Denoising 29

A New Method of Bearing Fault Diagnosis Based on LMD and Wav...

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第29届中国控制与决策会议

作者： Gao-xuejin Wen-huanran Wang-pu Faculty of Information Technology Beijing University of Technology The Ministry of Education P.R.C.Engineering Research Center of Digital Community Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781509046584

The impulse feature from an early diagnosis of bearing fault is often drowned by the noise background, and is usually very difficult to extract. To solve the problem, a new method was presented here, which was based on the Local Mean Decomposition(LMD) and wavelet de-noising. The LMD was used to decompose the original signal of the bearing into serval PF components which were retained using the principle of maximum kurtosis and cross-correlation coefficients to keep only the reasonable ones. Compared to the traditional PF component selection process, our new method captured more fault impulse features in the selected PF components. For these retained PF components were first de-noised by a db10 wavelet of 5 layers, and then were used to reconstruct the high frequency signal of each component layer by the method of superposition. Finally, the envelope spectrum analysis was applied to the derived the spectral kurtosis to give the result of rolling bearing fault diagnosis. A test experiment was conducted with our bearing fault simulation platform. The collected data, including signal from bearing outer ring, inner ring and ball, was analyzed using the method proposed in this paper. The result shown that the new method can effectively enhance the impulse features in the signal, also improve the fault diagnosis efficiency.

关键词： Local Mean Decomposition wavelet de-noising spectral kurtosis bearing fault diagnosis

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An Online Method Based on Random Forest for Air Pollutant Concentration Forecasting

An Online Method Based on Random Forest for Air Pollutant Co...

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第37届中国控制会议

作者： Jiangeng Li Xingyang Shao Huihong Zhao College of Automation Faculty of Information TechnologyBeijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Electromechanical Engineering Dezhou University Clean Energy Research and Technology Promotion Center Dezhou University

With rapid modernization, air quality is becoming gradually deteriorate. To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. The factors relevant to air pollutant concentration forecasting contain simultaneously numeric type（temperature etc.） and non-numeric type（wind direction etc.）. Random Forest has many advantages, which includes it can deal with numeric features and non-numeric features. In this study, an online forecasting method based on Random Forest is proposed to predict the concentrations of three kinds of air pollutants（PM2.5, NO2,SO2）, 24 hours in advance. The sliding window is used to take the recent data to retrain Random Forest prediction model and the well-trained models is used to predict the dependent variable at target moment. Before prediction model is trained, a variable selection method based on Random Forests（VSURF） is used to select the factors that are relevant to the forecast of air pollutant concentrations. We evaluate our method with dataset from Microsoft Research. Comparison with baseline methods shows that our method achieve state-of-art performance on air pollutant concentration forecasting. Experimental results also indicate that the features we selected using VSURF method are most important predictors for the prediction of three kinds of air pollutant concentrations.

关键词： Random Forest（RF） air quality forecasting online forecasting feature selection

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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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第37届中国控制会议

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

This paper is concerned with the semi-global output synchronization problem of a heterogeneous network under selftriggered 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 selftriggered rule is also presented in the paper.

关键词： Output synchronization input saturation selftriggered control multiagent system

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Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition

arXiv

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arXiv 2018年

作者： Li, Zhiyuan Teng, Nanjun Jin, Min Lu, Huaxiang CAS Center for Excellence in Brain Science and Intelligence Technology Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Lab of Artificial Networks Institute of Semiconductors CAS University of Chinese Academy of Sciences

Deep convolutional neural networks based methods have brought great breakthrough in images classification, which provides an end-to-end solution for handwritten Chinese character recognition(HCCR) problem through learning discriminative features automatically. Nevertheless, state-of-the-art CNNs appear to incur huge computation cost, and require the storage of a large number of parameters especially in fully connected layers, which is difficult to deploy such networks into alternative hardware devices with limited computation amount. To solve the storage problem, we propose a novel technique called Global Weighted Arverage Pooling for reducing the parameters in fully connected layer without loss in accuracy. Besides, we implement a cascaded model in single CNN by adding mid output layer to complete recognition as early as possible, which reduces average inference time significantly. Experiments were performed on the ICDAR-2013 offline HCCR dataset, and it is found that our proposed approach only needs 6.9ms for classfying a chracter image on average, and achieves the state-of-the-art accuracy of 97.1% while requires only 3.3MB for storage. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Fast stochastic ordinal embedding with variance reduction and adaptive step size

arXiv

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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Train Speed Trajectory Optimization using Dynamic Programming with speed modes decomposition

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IOP Conference Series: Materials Science and Engineering 2019年第4期569卷

作者： Pu Wang Yi Peng Xue-jin Gao Hui-hui Gao Faculty of Information Technology Beijing University of Technology Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China

When applying a dynamic programming algorithm to train speed trajectory optimization, there is a problem of too many discrete points which leading to dimension disaster. Different from using uniform discretization of the time and space by previous research, In this paper, a train operation model is proposed based on conditions (speed limit and slope change) and described in the network diagram. Armed with this model, through the pre-planning of the line, the optimization model of train operation is established, and the dynamic programming algorithm is utilized to find the global optimal value of train traction energy consumption. Taking beijing Yizhuang line as a simulation case, the validity of the proposed model is verified by comparing common optimization algorithms. The results demonstrate that the proposed model can effectively reduce the calculation time of the dynamic programming algorithm by 95.06%, and has better optimization effect. The energy-saving effect reaches 6.0806%.

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

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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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