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In wet flue gas desulfurization (WFGD) process, the pH value of the absorption tower slurry is a crucial factor to the efficiency of desulfurization system. Aiming at the nonlinearity and large lag of the pH change in WFGD process, a predictive control strategy based on Hammerstein–Wiener inverse model compensation is proposed. During the calculation of optimal control, an anti-model of Wiener nonlinearity unit is adopted to transform the output setting values and sampling values. Similarly in the control process, the controller output is applied to the actual controlled object after inverse transformation of the static nonlinear Hammerstein model. Through the above two inverse transformations, the controller output is identical with the input of linear link in the closed-loop system. In this article, the inverse model compensation method is utilized to transform nonlinear process control into linear system control, avoiding the large computation of nonlinear model optimization. Finally, the feasibility and effectiveness of the proposed scheme are verified by simulation.
Xiaoli Li; Jiawei Dong; Kang Wang. Constrained nonlinear model predictive control of pH value in wet flue gas desulfurization process. Optimal Control Applications and Methods 2021, 1 .
AMA StyleXiaoli Li, Jiawei Dong, Kang Wang. Constrained nonlinear model predictive control of pH value in wet flue gas desulfurization process. Optimal Control Applications and Methods. 2021; ():1.
Chicago/Turabian StyleXiaoli Li; Jiawei Dong; Kang Wang. 2021. "Constrained nonlinear model predictive control of pH value in wet flue gas desulfurization process." Optimal Control Applications and Methods , no. : 1.
The adaptive hinging hyperplane (AHH) model is a popular piecewise linear representation with a generalized tree structure and has been successfully applied in dynamic system identification. In this article, we aim to construct the deep AHH (DAHH) model to extend and generalize the networking of AHH model for high-dimensional problems. The network structure of DAHH is determined through a forward growth, in which the activity ratio is introduced to select effective neurons and no connecting weights are involved between the layers. Then, all neurons in the DAHH network can be flexibly connected to the output in a skip-layer format, and only the corresponding weights are the parameters to optimize. With such a network framework, the backpropagation algorithm can be implemented in DAHH to efficiently tackle large-scale problems and the gradient vanishing problem is not encountered in the training of DAHH. In fact, the optimization problem of DAHH can maintain convexity with convex loss in the output layer, which brings natural advantages in optimization. Different from the existing neural networks, DAHH is easier to interpret, where neurons are connected sparsely and analysis of variance (ANOVA) decomposition can be applied, facilitating to revealing the interactions between variables. A theoretical analysis toward universal approximation ability and explicit domain partitions are also derived. Numerical experiments verify the effectiveness of the proposed DAHH.
Qinghua Tao; Jun Xu; Zhen Li; Na Xie; Shuning Wang; Xiaoli Li; Johan A. K. Suykens. Toward Deep Adaptive Hinging Hyperplanes. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -15.
AMA StyleQinghua Tao, Jun Xu, Zhen Li, Na Xie, Shuning Wang, Xiaoli Li, Johan A. K. Suykens. Toward Deep Adaptive Hinging Hyperplanes. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-15.
Chicago/Turabian StyleQinghua Tao; Jun Xu; Zhen Li; Na Xie; Shuning Wang; Xiaoli Li; Johan A. K. Suykens. 2021. "Toward Deep Adaptive Hinging Hyperplanes." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-15.
The PM
Jihan Li; Xiaoli Li; Kang Wang; Guimei Cui. Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter. Atmosphere 2021, 12, 607 .
AMA StyleJihan Li, Xiaoli Li, Kang Wang, Guimei Cui. Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter. Atmosphere. 2021; 12 (5):607.
Chicago/Turabian StyleJihan Li; Xiaoli Li; Kang Wang; Guimei Cui. 2021. "Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter." Atmosphere 12, no. 5: 607.
Task Primitive Library (TPLib) consists of task primitives which places dynamic or probabilistic movement primitives in chronological order. A teaching and learning system is formed with TPLib, from which task trajectories can be saved with less parameters and generated in a new situation. TPLib is built by learning from a industrial task dataset which contains 8 tasks from simple to complex in this work. The dataset is collected by Kinect2.0 depth camera with visual motion capture algorithm. To reduce error, Savizkg-Golag smoothing algorithm is utilized after the collection. To test the performance of our TPLib, we simulated on ROS platform. The result shows that specific task trajectories can be finished through this library. The property of temporal modification is also proved in simulation. In the purpose of modifying a exact trajectory in a task without removing all the knowledge learned before, a correlation update rule is proposed. It also can meet the expectation in the simulation on ROS platform.
Ailin Xue; Xiaoli Li; Chunfang Liu; Xiaoyue Cao. Movement Primitive Libraries Learning for Industrial Manipulation Tasks. Communications in Computer and Information Science 2021, 287 -300.
AMA StyleAilin Xue, Xiaoli Li, Chunfang Liu, Xiaoyue Cao. Movement Primitive Libraries Learning for Industrial Manipulation Tasks. Communications in Computer and Information Science. 2021; ():287-300.
Chicago/Turabian StyleAilin Xue; Xiaoli Li; Chunfang Liu; Xiaoyue Cao. 2021. "Movement Primitive Libraries Learning for Industrial Manipulation Tasks." Communications in Computer and Information Science , no. : 287-300.
This paper is concerned with the secure consensus problem of multiagent systems under switching topologies. The studied multiagent systems are affected by both denial-of-service (DoS) attacks and external disturbances. To solve the secure H∞ consensus problems, some modified definitions are presented. Some graph-based Lyapunov functions, which are based on the solutions of some Lyapunov equations and the graph information, are also designed for the H∞ performance analysis. Moreover, graph-based frequency and durations have also been presented for attaining the expected system performance. The stabilization controllers are also developed based on the solutions to some Lyapunov equations and an algebraic Riccati equation (ARE), which are easy to acquire. Some simulations are provided to validate the feasibility of the proposed scheme.
Shengli Du; Yuee Wang; Lijing Dong; Xiaoli Li. Secure consensus of multiagent systems with DoS attacks via a graph-based approach. Information Sciences 2021, 570, 94 -104.
AMA StyleShengli Du, Yuee Wang, Lijing Dong, Xiaoli Li. Secure consensus of multiagent systems with DoS attacks via a graph-based approach. Information Sciences. 2021; 570 ():94-104.
Chicago/Turabian StyleShengli Du; Yuee Wang; Lijing Dong; Xiaoli Li. 2021. "Secure consensus of multiagent systems with DoS attacks via a graph-based approach." Information Sciences 570, no. : 94-104.
State estimate of traffic flow of a freeway segment is considered in this paper by using the method of boundary observer design. The observer control and measurements are all located at the boundaries. The non-equilibrium traffic flow dynamics are modeled as a Markov jump linear hyperbolic system with phase transitions. Based on the Lyapunov techniques, the sufficient conditions of matrix inequalities are derived for the exponentially mean-square static as well as the dynamic boundary observers. Using real data of vehicle trajectories from the NGSIM project, the developed stochastic phase transition model of non-equilibrium traffic flow is calibrated and validated. Moreover, some simulation results illustrate the effectiveness of the boundary observers for traffic estimate.
Liguo Zhang; Xiaoli Li; Jianru Hao; Junfei Qiao. Boundary Observer Design for Stochastic Phase Transition Models of Non-equilibrium Traffic Flow. IEEE Transactions on Automatic Control 2021, PP, 1 -1.
AMA StyleLiguo Zhang, Xiaoli Li, Jianru Hao, Junfei Qiao. Boundary Observer Design for Stochastic Phase Transition Models of Non-equilibrium Traffic Flow. IEEE Transactions on Automatic Control. 2021; PP (99):1-1.
Chicago/Turabian StyleLiguo Zhang; Xiaoli Li; Jianru Hao; Junfei Qiao. 2021. "Boundary Observer Design for Stochastic Phase Transition Models of Non-equilibrium Traffic Flow." IEEE Transactions on Automatic Control PP, no. 99: 1-1.
The control method based on the equivalent‐input‐disturbance (EID) estimator and the Luenberger state observer has received much attention in recent years. However, the property of EID‐based control systems is still not well investigated. A number of design procedures were proposed but lacked sufficient theoretic justification. In this article, the two‐degree‐of‐freedom nature of an EID‐based control system is revealed. Specifically, the reference‐tracking is determined by an outer loop, while the disturbance rejection and robustness is determined by an inner loop consisting of the EID estimator and state observer. The bandwidth constraints on the inner loop are analyzed for plants having zeros or poles in the open right‐half plane by using the Bode and the Poisson integral formulas. These analyses provide a theoretic justification for conventional design procedures. Further, a coordinated design algorithm is provided for the EID‐based uncertain control systems. In addition, a comprehensive comparison of the EID‐based and the disturbance observer (DOB) based control systems is conducted in the system design aspect. Lastly, comparative studies of the EID‐based and DOB‐based control methods are given for various types of plants to validate the developed design algorithm.
Pan Yu; Kang‐Zhi Liu; Xudong Liu; Xiaoli Li; Min Wu; Jinhua She. Analysis of equivalent‐input‐disturbance‐based control systems and a coordinated design algorithm for uncertain systems. International Journal of Robust and Nonlinear Control 2021, 31, 1755 -1773.
AMA StylePan Yu, Kang‐Zhi Liu, Xudong Liu, Xiaoli Li, Min Wu, Jinhua She. Analysis of equivalent‐input‐disturbance‐based control systems and a coordinated design algorithm for uncertain systems. International Journal of Robust and Nonlinear Control. 2021; 31 (5):1755-1773.
Chicago/Turabian StylePan Yu; Kang‐Zhi Liu; Xudong Liu; Xiaoli Li; Min Wu; Jinhua She. 2021. "Analysis of equivalent‐input‐disturbance‐based control systems and a coordinated design algorithm for uncertain systems." International Journal of Robust and Nonlinear Control 31, no. 5: 1755-1773.
Multi-objective evolutionary algorithms (MOEAs) have proven their effectiveness in solving two or three objective problems. However, recent research shows that Pareto-based MOEAs encounter selection difficulties facing many similar non-dominated solutions in dealing with many-objective problems. In order to reduce the selection pressure and improve the diversity, we propose achievement scalarizing function sorting strategy to make strength Pareto evolutionary algorithm suitable for many-objective optimization. In the proposed algorithm, we adopt density estimation strategy to redefine a new fitness value of a solution, which can select solution with good convergence and distribution. In addition, a clustering method is used to classify the non-dominated solutions, and then, an achievement scalarizing function ranking method is designed to layer different frontiers and eliminate redundant solutions in the environment selection stage, thus ensuring the convergence and diversity of non-dominant solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems with 3, 5, 8, 10 objectives. Experimental studies demonstrate that the proposed algorithm shows very competitive performance.
Xin Li; Xiaoli Li; Kang Wang; Shengxiang Yang; Yang Li. Achievement scalarizing function sorting for strength Pareto evolutionary algorithm in many-objective optimization. Neural Computing and Applications 2020, 33, 6369 -6388.
AMA StyleXin Li, Xiaoli Li, Kang Wang, Shengxiang Yang, Yang Li. Achievement scalarizing function sorting for strength Pareto evolutionary algorithm in many-objective optimization. Neural Computing and Applications. 2020; 33 (11):6369-6388.
Chicago/Turabian StyleXin Li; Xiaoli Li; Kang Wang; Shengxiang Yang; Yang Li. 2020. "Achievement scalarizing function sorting for strength Pareto evolutionary algorithm in many-objective optimization." Neural Computing and Applications 33, no. 11: 6369-6388.
It is hoped that the robot could interact with the human when the robots help us in our daily lives. And understanding humans’ specific intention is the first crucial task for human-robot interaction. In this paper, we firstly develop a multi-task model for recognizing humans’ intention, which is composed of two sub-tasks: human action recognition and hand-held object identification. For the front subtask, an effective ST-GCN-LSTM model is proposed by fusing the Spatial Temporal Graph Convolutional Networks and Long Short Term Memory Networks. And for the second subtask, the YOLO v3 model is adopted for the hand-held object identification. Then, we build a framework for robot interacting with the human. Finally, these proposed models and the interacting framework are verified on several datasets and the testing results show the effectiveness of the proposed models and the framework.
Chunfang Liu; Xiaoli Li; Qing Li; Yaxin Xue; Huijun Liu; Yize Gao. Robot recognizing humans intention and interacting with humans based on a multi-task model combining ST-GCN-LSTM model and YOLO model. Neurocomputing 2020, 430, 174 -184.
AMA StyleChunfang Liu, Xiaoli Li, Qing Li, Yaxin Xue, Huijun Liu, Yize Gao. Robot recognizing humans intention and interacting with humans based on a multi-task model combining ST-GCN-LSTM model and YOLO model. Neurocomputing. 2020; 430 ():174-184.
Chicago/Turabian StyleChunfang Liu; Xiaoli Li; Qing Li; Yaxin Xue; Huijun Liu; Yize Gao. 2020. "Robot recognizing humans intention and interacting with humans based on a multi-task model combining ST-GCN-LSTM model and YOLO model." Neurocomputing 430, no. : 174-184.
Sulphur dioxide, as one of the most common air pollutant gases, brings considerable numbers of hazards on human health and environment. For the purpose of reducing the detrimental effect it brings, it is of urgent necessity to control emissions of flue gas in power plants, since a substantial proportion of sulphur dioxide in the atmosphere stems from flue gas generated in the whole process of electricity generation. However, the complexity and nondeterminism of the environment increase the occurrences of anomalies in practical flue gas desulphurization system. Anomalies in industrial desulphurization system would induce severe consequences and pose challenges for high-performance control with classical control strategies. In this article, based on process data sampled from 1000 MW unit flue gas desulphurization system in a coal-fired power plant, a multimodel control strategy with multilayer parallel dynamic neural network (MPDNN) is utilized to address the control problem in the context of different anomalies. In addition, simulation results indicate the applicability and effectiveness of the proposed control method by comparing with different cases.
Xiaoli Li; Quanbo Liu; Kang Wang; Fuqiang Wang; Guimei Cui; Yang Li. Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System. Complexity 2020, 2020, 1 -17.
AMA StyleXiaoli Li, Quanbo Liu, Kang Wang, Fuqiang Wang, Guimei Cui, Yang Li. Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System. Complexity. 2020; 2020 ():1-17.
Chicago/Turabian StyleXiaoli Li; Quanbo Liu; Kang Wang; Fuqiang Wang; Guimei Cui; Yang Li. 2020. "Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System." Complexity 2020, no. : 1-17.
In recent years, the China’s economy has developed rapidly. The people’s living standard has been improved. The number of cars has been increasing, and the pollutant NO has been produced continuously, which leads to the formation of NO\(_{2}\). These harmful particles have an impact on human health. Thus, the effective and accurate NO\(_{2}\) concentration prediction model plays an effective role in people’s health and prevention. For this problem, this paper presents a prediction model based on the long short-term memory (LSTM) method to predict NO\(_{2}\) concentration. Firstly, the PM\(_{10}\), SO\(_{2}\), NO\(_{2}\), CO, O\(_{3}\), temperature in a campus monitoring point in Beijing is collected as the research object in this paper. Then, the LSTM prediction model and BP (back propagation) neural network prediction model are established respectively. Finally, the accuracy of the two prediction models for the prediction of NO\(_{2}\) concentration is compared. The results show that the prediction model based on LSTM method is superior to BP neural network model, and the prediction accuracy is more accurate.
Jihan Li; Xiaoli Li; Jian Liu; Kang Wang. Air Pollutants NO$$_{2}$$ Concentration Prediction Based on LSTM Neural Network method. Lecture Notes in Electrical Engineering 2020, 801 -808.
AMA StyleJihan Li, Xiaoli Li, Jian Liu, Kang Wang. Air Pollutants NO$$_{2}$$ Concentration Prediction Based on LSTM Neural Network method. Lecture Notes in Electrical Engineering. 2020; ():801-808.
Chicago/Turabian StyleJihan Li; Xiaoli Li; Jian Liu; Kang Wang. 2020. "Air Pollutants NO$$_{2}$$ Concentration Prediction Based on LSTM Neural Network method." Lecture Notes in Electrical Engineering , no. : 801-808.
In limestone-gypsum wet flue gas desulfurization process, the change process of pH value of slurry in absorption tower is a typical nonlinear system with time delay and various uncertainties, so it is difficult to establish an accurate mathematical model of slurry pH control process. According to the pH control process of the slurry of wet flue gas desulfurization process, a model-free adaptive control algorithm based on compact form dynamic linearization (CFDL-MFAC) is designed to realize the tracking control of the pH value of the slurry. Due to various interference factors in the pH control process of slurry in absorption tower, it is easy to cause jump change of control system parameters and even structure. Therefore, a model-free adaptive control algorithm based on switching strategy is proposed in this paper. According to different working conditions, several model-free adaptive controllers are established. The stability of the algorithm is analyzed for the two cases of fixed system parameters and jumping system parameters. It was found that the model-free adaptive controller based on the switching strategy can switch multiple controllers under the condition of system parameter jump, so as to realize the fast tracking control of the slurry pH value of the system absorption tower under different working conditions. Through this method, the overshoot can be reduced and the control quality can be improved.
Jian Liu; Xiaoli Li; Jihan Li; Kang Wang; Fuqiang Wang; Guimei Cui. Model-Free Adaptive Control of pH Value of Wet Desulfurization Slurry under Switching of Multiple Working Conditions. Complexity 2020, 2020, 1 -10.
AMA StyleJian Liu, Xiaoli Li, Jihan Li, Kang Wang, Fuqiang Wang, Guimei Cui. Model-Free Adaptive Control of pH Value of Wet Desulfurization Slurry under Switching of Multiple Working Conditions. Complexity. 2020; 2020 ():1-10.
Chicago/Turabian StyleJian Liu; Xiaoli Li; Jihan Li; Kang Wang; Fuqiang Wang; Guimei Cui. 2020. "Model-Free Adaptive Control of pH Value of Wet Desulfurization Slurry under Switching of Multiple Working Conditions." Complexity 2020, no. : 1-10.
Flue gas emission is an inevitable procedure in the course of electricity generation, which would pose a severe threat to human health, and has an adverse effect on our environment. Due to the fact that the environment in practical flue gas desulfurization system fluctuates frequently, system parameters tend to vary constantly during the operating process, thus control performance with traditional strategies tends to be suboptimal in most cases. To address this problem, some insight into operating conditions must be gained prior to taking proper control strategy. Therefore, in this paper, based on actual measurements in 1000 MW Unit Wet Limestone FGD System for a coal-fired power plant, a kind of intelligent operating condition partition method is combined with the multi-model adaptive control strategy. Specifically, analysis and partition of operating condition is carried out in the first place, then adaptive multi-model control is implemented with the combination of parallel dynamic neural network and partition results. Additionally, the applicability of proposed control mode is investigated through different simulation examples. At the same time, to further enhance the flexibility of multi-model control structure, some possible improvements on it is also discussed.
Xiaoli Li; Quanbo Liu; Kang Wang; Fuqiang Wang; Guimei Cui; Yang Li. Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization. IEEE Access 2020, 8, 149301 -149315.
AMA StyleXiaoli Li, Quanbo Liu, Kang Wang, Fuqiang Wang, Guimei Cui, Yang Li. Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization. IEEE Access. 2020; 8 (99):149301-149315.
Chicago/Turabian StyleXiaoli Li; Quanbo Liu; Kang Wang; Fuqiang Wang; Guimei Cui; Yang Li. 2020. "Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization." IEEE Access 8, no. 99: 149301-149315.
Slag powder is a process with characters of multivariables, strongly coupling and nonlinearity. The material layer thickness plays an important role in the process. It can reflect the dynamic balance between the feed volume and discharge volume in the vertical mill. Keeping the material layer thickness in a suitable range can not only improve the quality of powder, but also save electrical power. Previous studies on the material layer thickness did not consider the relationship among the material layer thickness, quality and yield. In this paper, the yield and quality factors are taken into account and the variables that affect the material layer thickness, yield and quality are analyzed. Then the models of material layer thickness, yield and quality are established based on generalized regression neural network. The production process demands for highest yield, best production quality and smallest error of material layer thickness at the same time. From this point of view, the slag powder process can be regarded as a multi-objective optimization problem. To improve the diversity of solutions, a CT-NSGAII algorithm is proposed by introducing the clustering-based truncation mechanism into solution selection process. Simulation shows that the proposed method can solve the multi-objective problem and obtain solutions with good diversity.
Xiaoli Li; Shiqi Shen; Shengxiang Yang; Kang Wang; Yang Li. Analysis and multi-objective optimization of slag powder process. Applied Soft Computing 2020, 96, 106587 .
AMA StyleXiaoli Li, Shiqi Shen, Shengxiang Yang, Kang Wang, Yang Li. Analysis and multi-objective optimization of slag powder process. Applied Soft Computing. 2020; 96 ():106587.
Chicago/Turabian StyleXiaoli Li; Shiqi Shen; Shengxiang Yang; Kang Wang; Yang Li. 2020. "Analysis and multi-objective optimization of slag powder process." Applied Soft Computing 96, no. : 106587.
Modelling and predicting the suspect activity trajectory are of great importance for preventing and fighting crime in the food safety area. Combing artificial intelligence and the multiple U-model algorithm, this paper represents a novel approach to predict the suspect activity trajectory. Based on social text data, emotional assessment is conducted using the LSTM network to detect food safety criminal suspects. Activity trajectories of criminal suspects are clustered using the graphic clustering method based on the GPS data. U-model with the sliding window algorithm is proposed to model activity trajectories. Further, the multiple U-model strategy is proposed to predict the activity trajectory based on the accumulated model error of previous positions and multiple clustered trajectories. The simulation study shows that the proposed scheme can detect food safety criminal suspects and predict their activity trajectories effectively.
Kang Wang; Kun Bu; Yipeng Zhang; Xiaoli Li. Prediction of Suspect Activity Trajectory in Food Safety Area Based on Multiple U-Model Algorithm. Mathematical Problems in Engineering 2020, 2020, 1 -11.
AMA StyleKang Wang, Kun Bu, Yipeng Zhang, Xiaoli Li. Prediction of Suspect Activity Trajectory in Food Safety Area Based on Multiple U-Model Algorithm. Mathematical Problems in Engineering. 2020; 2020 ():1-11.
Chicago/Turabian StyleKang Wang; Kun Bu; Yipeng Zhang; Xiaoli Li. 2020. "Prediction of Suspect Activity Trajectory in Food Safety Area Based on Multiple U-Model Algorithm." Mathematical Problems in Engineering 2020, no. : 1-11.
We endeavor to investigate the H∞ synthesis problem of switched fuzzy systems composed by several discrete-time subsystems. By designing a novel QTD-MD Lyapunov function, novel criteria assuring stability as well as an expected H∞ performance are first developed. Based on these sufficient conditions, some other conditions for solving the fuzzy H∞ controllers are derived subsequently. Furthermore, the criteria are given as LMIs, thus the implementations are easy to execute. Simulations are also provided to confirm the feasibility of the presented strategy.
Shengli Du; Xiaoli Li; Shen Sun; Xu Li. Stability analysis and stabilization of discrete-time switched Takagi–Sugeno fuzzy systems. ISA Transactions 2020, 105, 1 .
AMA StyleShengli Du, Xiaoli Li, Shen Sun, Xu Li. Stability analysis and stabilization of discrete-time switched Takagi–Sugeno fuzzy systems. ISA Transactions. 2020; 105 ():1.
Chicago/Turabian StyleShengli Du; Xiaoli Li; Shen Sun; Xu Li. 2020. "Stability analysis and stabilization of discrete-time switched Takagi–Sugeno fuzzy systems." ISA Transactions 105, no. : 1.
For the issue of relocalization, this paper proposed a deep-learning-based method for outdoor large-scale environment. In the first step, we projected a 3D Light Detection and Ranging(LiDAR) scan onto three 2D images from top to bottom. Then a densenet-based neural network structure was designed to regress a 4-DOF robot pose. These images are then stacked together, fed into the proposed DCNN architecture, and the output is the predicted robot pose. Extensive experiments have been conducted in practice with a real mobile robot, verifying the effectiveness of the proposed strategy. Our network can obtain approximately 3.5m and 4◦ accuracy outdoors.
Shikuan Yu; Fei Yan; Wenzhe Yang; Xiaoli Li; Yan Zhuang. Deep-Learning-based Relocalization in Large-Scale outdoor Environment. IFAC-PapersOnLine 2020, 53, 9722 -9727.
AMA StyleShikuan Yu, Fei Yan, Wenzhe Yang, Xiaoli Li, Yan Zhuang. Deep-Learning-based Relocalization in Large-Scale outdoor Environment. IFAC-PapersOnLine. 2020; 53 (2):9722-9727.
Chicago/Turabian StyleShikuan Yu; Fei Yan; Wenzhe Yang; Xiaoli Li; Yan Zhuang. 2020. "Deep-Learning-based Relocalization in Large-Scale outdoor Environment." IFAC-PapersOnLine 53, no. 2: 9722-9727.
This article is concerned with the exponential stability and ℓ₁-gain performance analysis of discrete-time switched positive systems (DTSPSs) under mode-dependent average dwell time (MDADT) switching. A novel linear copositive Lyapunov function, which is both quasi-time-dependent and mode-dependent, is designed for the stability and performance analysis. Stability conditions are developed such that the considered DTSPS is exponentially stable and also attains an attenuation performance. The solved conditions for the controllers design are presented in terms of linear programming (LP), and are both quasi-time-dependent and mode-dependent. Two examples are organized to validate the effectiveness of the proposed scheme finally.
Xiaoli Li; Sheng-Li Du; Xudong Zhao. Stability and ℓ1-Gain Analysis for Switched Positive Systems With MDADT Based on Quasi-Time-Dependent Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2019, 51, 5846 -5854.
AMA StyleXiaoli Li, Sheng-Li Du, Xudong Zhao. Stability and ℓ1-Gain Analysis for Switched Positive Systems With MDADT Based on Quasi-Time-Dependent Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019; 51 (9):5846-5854.
Chicago/Turabian StyleXiaoli Li; Sheng-Li Du; Xudong Zhao. 2019. "Stability and ℓ1-Gain Analysis for Switched Positive Systems With MDADT Based on Quasi-Time-Dependent Approach." IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, no. 9: 5846-5854.
Most multi-objective particle swarm optimization algorithms, which have demonstrated their good performance on various practical problems involving two or three objectives, face significant challenges in complex problems. For overcoming this challenges, a multi-objective particle swarm optimization algorithm based on enhanced selection(ESMOPSO) is proposed. In order to increase the ability of exploration and exploitation, enhanced selection strategy is designed to update personal optimal particles, and objective function weighting is used to update global optimal particle adaptively. In addition, R2 indicator is incorporated into the achievement scalarizing function to layer particles in archive, which promotes the archive update. Besides, Gaussian mutation strategy is designed to avoid particles falling into local optimum, and polynomial mutation is applied in archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental results demonstrate that ESMOPSO algorithm shows very competitive performance when dealing with complex MOPs.
Xin Li; Xiao-Li Lia; Kang Wang; Yang Li. A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection. IEEE Access 2019, 7, 168091 -168103.
AMA StyleXin Li, Xiao-Li Lia, Kang Wang, Yang Li. A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection. IEEE Access. 2019; 7 (99):168091-168103.
Chicago/Turabian StyleXin Li; Xiao-Li Lia; Kang Wang; Yang Li. 2019. "A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection." IEEE Access 7, no. 99: 168091-168103.
Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5is the main particulate matter in air pollution. Therefore, how to predict PM2.5accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5prediction, and it is effective.
Jihan Li; Xiaoli Li; Kang Wang. Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach. Advances in Meteorology 2019, 2019, 1 -11.
AMA StyleJihan Li, Xiaoli Li, Kang Wang. Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach. Advances in Meteorology. 2019; 2019 ():1-11.
Chicago/Turabian StyleJihan Li; Xiaoli Li; Kang Wang. 2019. "Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach." Advances in Meteorology 2019, no. : 1-11.