The weighted complementarity problem is an extension of the standard finite dimensional complementarity problem. It is well known that the smoothing-type algorithm is a powerful tool of solving the standard complement...
The weighted complementarity problem is an extension of the standard finite dimensional complementarity problem. It is well known that the smoothing-type algorithm is a powerful tool of solving the standard complementarity problem. In this paper, we propose a smoothing-type algorithm for solving the weighted complementarity problem with a monotone function, which needs only to solve one linear system of equations and performs one line search at each iteration. We show that the proposed method is globally convergent under the assumption that the problem is solvable. The preliminary numerical results indicate that the proposed method is effective and robust for solving the monotone weighted complementarity problem.
The relationship of state parameters and burden distribution is uncertain in blast furnaces. In the present industry,burden operation mainly relies on the experiences of the workers. So it is difficult to control burd...
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The relationship of state parameters and burden distribution is uncertain in blast furnaces. In the present industry,burden operation mainly relies on the experiences of the workers. So it is difficult to control burden operation. To solve these problems, this paper presents a model to adjust the burden distribution using fuzzy C-means(FCM) and wavelet analysis. First,multiple condition states of blast furnaces are analysed through data processing, the state parameters are clustered based on the similarity, and then this paper searches for the corresponding burden parameters from the history data. Finally, for different state clusters, best burden parameters are selected to adjust the conditions. Simulation results show that the burden distribution adjustments based on the state parameters clustering are efficient.
This study addresses the uniformly globally asymptotically stability (UGAS) problem of switched nonlinear delay systems (SNDSs) with sampled-data inputs (SDIs). By using multiple Lyapunov functionals (MLFs) method, mo...
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This study addresses the uniformly globally asymptotically stability (UGAS) problem of switched nonlinear delay systems (SNDSs) with sampled-data inputs (SDIs). By using multiple Lyapunov functionals (MLFs) method, mode-dependent average dwell times, and the total activating time length of MLFs, some stability criteria are explicitly obtained for SNDSs with SDIs. Meanwhile, the UGAS property for SNDSs with some or all unstable modes is investigated. For unstable modes and stable modes, we adopt different switching signals. Besides, we establish some sufficient stability conditions in the form of an upper bound on the sum of dwell times and sampling intervals. Simulation examples are adopted to illustrate and verify the effectiveness of our proposed methods.
Bifurcation and stability analysis for a fractional-order gene regulatory networks with time delay is researched. Firstly, taking time delay as the bifurcation parameter, the Hopf bifurcation and stability conditions ...
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The evaluation and decision support system are widely used in agriculture with the development of information technology. In this paper, a Web-based comprehensive evaluation and decision support system(WCEDSS) is de...
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The evaluation and decision support system are widely used in agriculture with the development of information technology. In this paper, a Web-based comprehensive evaluation and decision support system(WCEDSS) is designed for flue-cured tobacco cultivation process. Using the artificial neural networks and some image processing methods, WCEDSS can predict various indicators of tobacco such as yield and flue-cured score, or give decisions at different stages of tobacco cultivation. As the system is developed by the Browser/Server(B/S) architecture, it is possible to make full use of the Internet resources and to facilitate users at any place with access to the Internet. Test results indicates that WCEDSS can achieve good performance. With other auxiliary functions, the system shows potential and extensive practicality in agricultural information management and guidance.
The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production process. To find ways of reducing the energy consumption, ...
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The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production process. To find ways of reducing the energy consumption, it is necessary to predict the carbon efficiency. The value of CO/CO_2 in the carbon emission can reflect the utilization of carbon combustion in sintering process. In this study, the CO/CO_2 is taken to be a measure of carbon efficiency and a hierarchical model is built to predict it. Firstly, the physical and chemical reactions and the carbon flow mechanism in the sintering process are analyzed, and the process parameters that affect the CO/CO_2 are determined. Then, the gray relational analysis method is used to analyze the influence factors to determine the relationship between the parameters, and a hierarchical predictive model for CO/CO_2 is established based on the relationship between the parameters. The hierarchical predictive model is divided into two parts: the predictive models for the thermal state parameters and the predictive model for CO/CO_2. The inputs of the predictive models for the thermal state parameters are the raw material parameters and the operating parameters, and the inputs of the predictive model for CO/CO_2 are the predicted values of the predictive models for the thermal state parameters. Finally, the simulation results verify the effectiveness of the proposed modeling method. This method can provide a theoretical basis for the optimization and control of carbon efficiency in the sintering process.
Maintaining the quality of network coverage is a major concern in visual sensor networks. In this paper, we study the angle coverage problem in visual sensor networks, considering the target is very large, and each ca...
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Maintaining the quality of network coverage is a major concern in visual sensor networks. In this paper, we study the angle coverage problem in visual sensor networks, considering the target is very large, and each camera node can only monitor a portion of its perimeter. The goal of the proposed work is to schedule camera nodes to achieve maximum angle coverage in different period. Firstly, we establish a novel coverage model and formally prove that the problem is NP-hard in ***, we present a scheduling scheme based on greedy algorithm, to schedule camera nodes into disjoint cover sets working in ***, we conclude that the approximate ratio of the proposed algorithm is k, theoretically, and the time complexity of the algorithm is O(n). Finally, extensive simulations have been conducted to evaluate the performance of the proposed algorithm.
With the increased number of traffic accidents, the research and development of smart cars have been *** detection of street objects has become one of the important research topics. Generic Model detection algorithm b...
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With the increased number of traffic accidents, the research and development of smart cars have been *** detection of street objects has become one of the important research topics. Generic Model detection algorithm based on Convolution Neural Network(CNN) need to design the training model, while the training and testing of the model will take a lot of time. Transfer Learning is used to fine-tune the pre-trained models, using the Image task datasets of COCO, transferring a generic deep learning model to specific one with different weights and outputs. Furthermore, the CNN structure is adjusted to improve overall performance, and the street environment is trained to the special scene. We compare the results of experiments,and the results showed that the network which is fine-tuned is effective.
This paper proposed an adaptive neural admittance control strategy for collision avoidance in human-robot collaborative tasks. In order to ensure that the robot end-effector can avoid collisions with surroundings, rob...
This paper proposed an adaptive neural admittance control strategy for collision avoidance in human-robot collaborative tasks. In order to ensure that the robot end-effector can avoid collisions with surroundings, robot should be operated compliantly by human within a constrained task space. An impedance model and a soft saturation function are employed to generate a differentiable reference trajectory. Then, adaptive neural network control with position constraint, based on integral barrier Lyapunov function (IBLF), is designed to achieve precise tracking while guaranteeing constrained satisfaction. Utilizing Lyapunov stability principles, we prove that semi-globally uniformly bounded stability is guaranteed for all states of the closed-loop system. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experimental platform. Collisions with surroundings can be avoided in human-robot collaborative tasks.
Since the operating temperature of absorption chamber affects the sensitivity of the optically pumped cesium magnetometer(OPCM) directly, it is necessary to control the temperature precisely. In this paper, by using...
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Since the operating temperature of absorption chamber affects the sensitivity of the optically pumped cesium magnetometer(OPCM) directly, it is necessary to control the temperature precisely. In this paper, by using the positive temperature coefficient(PTC) and the integral separation proportional-integral-derivative(PID) algorithm, a high-precision thermostatic heating system is designed for OPCM. Firstly, the PTC heating device and the TSic506 temperature sensor are used to form a closed loop control system. Then, the three parameters of P, I and D are adjusted for different temperature, and the temperature control system is realized by STM32 microcontroller. Finally, the integral separation PID algorithm is used to eliminate overshoot. Experiments show that the effective temperature control ranges are 45?C5?C, the accuracy is less than ±0.2?C, and the system stability time is 300 s. It is obviously that the designed system has reference value and guidance significance for OPCM.
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