With the extensive use of distributed generation, the traditional demand response analysis cannot meet the current requirements. This paper proposes a NSGA-II based peak load shifting optimization method for customers...
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With the extensive use of distributed generation, the traditional demand response analysis cannot meet the current requirements. This paper proposes a NSGA-II based peak load shifting optimization method for customers with distributed generators considering time-of-use price. Firstly, a fuzzy classification method divides the daily power into three time segments, peak hours, flat hours and valley hours. Secondly, on the basis of the time-of-use price, a peak load shifting optimization model is built with constraints and the objectives of minimizing the peak load, maximizing the valley load, and minimizing the peak-valley difference. Then, a NSGA-II based optimization method solves the optimal model and obtains the optimal electricity prices of different time segments to shift the peak load and the valley load. Finally, the simulation results shows the effectiveness of the proposed method.
For speech emotion recognition (SER), emotional feature set with high dimension may produce redundant features and influence the recognition rate. To solve this problem, feature selection of speaker-independent speech...
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For speech emotion recognition (SER), emotional feature set with high dimension may produce redundant features and influence the recognition rate. To solve this problem, feature selection of speaker-independent speech based on genetic algorithm (GA) is proposed, which can obtain optimal feature subset. And a four-level emotional classification method based on support vector machine (SVM) is proposed according to the confusion degree among different emotional categories. A framework of speaker-independent SER is presented and the classification experiments based on proposed methods by using Chinese speech database from institute of automation of Chinese academy of sciences (CASIA) are performed, where the speaker-independent features selected by the proposed feature selection method and Spearman correlation analysis are used for emotion recognition, respectively. The experimental results show that the proposal achieved 77.6% recognition rate on average, which is about 1.2% higher than other recognition methods. By proposal, it would be efficient to distinguish the emotional states of different speakers from speech.
This paper presents an all-solid porous Ag/AgCl electric field sensor with ultralow-potential drift for detecting the seafloor electric field signals. The superiority of porous electrode compared with flat electrode o...
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This paper presents an all-solid porous Ag/AgCl electric field sensor with ultralow-potential drift for detecting the seafloor electric field signals. The superiority of porous electrode compared with flat electrode on the polarization stability is expounded, and the technological process using solid state agglomeration in developing the all-solid porous Ag/AgCl electrode core are described. Moreover, a new sensor shell is proposed. Numerous parameters, e.g., polarization resistance, self-potential, drift, etc., of the proposed electrode are tested. The experimental results show that the self-potential is less than ±0.1 mV, the source resistance is less than 0.01H, and the drift potential is less than ±5μV/24h, which indicate the superior performance of the proposed Ag/AgCl electric field sensor for marine electric field exploration.
This paper studies mean square and almost sure consensus of discrete-time second-order multi-agent systems with time-delays and multiplicative noises in the information exchange with *** using the stochastic stability...
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ISBN:
(纸本)9781509046584
This paper studies mean square and almost sure consensus of discrete-time second-order multi-agent systems with time-delays and multiplicative noises in the information exchange with *** using the stochastic stability theorem of discrete-time stochastic delay systems,we find sufficient conditions for mean square and almost sure consensus explicitly related to the network and control *** is shown that if the network graph is balanced and strongly connected,then the weighted-average type control protocol can be properly designed to ensure mean square and almost sure consensus for any given time-delays and noise intensity coefficients.
Overhauser magnetometer is a weak magnetic measuring instrument based on the dynamic nuclear polarization of free radical species. It has the characteristics of high precision, high sensitivity, low power consumption ...
Overhauser magnetometer is a weak magnetic measuring instrument based on the dynamic nuclear polarization of free radical species. It has the characteristics of high precision, high sensitivity, low power consumption and continuous measurement. It is widely used in geophysics, aeromagnetic surveying, satellite Magnetic measuring, military and various engineering fields. Aiming at the problem that the measurement accuracy of the magnetometer prototype is low and the measurement effect is poor when the angle between the probe axis and the geomagnetic field is 0°, several optimization methods are proposed and the magnetometer is optimized by adding the frequency measurement channel on the basis of the prototype. Based on the standard deviation of GSM-19 measurement data, comparing the experimental data that use the multi -channel frequency measurement algorithm, the standard deviation of prototype data decreases from 0.397 to 0.203 when the angle between probe axial and geomagnetic field is 45°, the standard deviation of the prototype data decreases from 0.631 to 0.371 when the angle between probe axial and geomagnetic field is 0°, which provides an important reference for the follow-up optimization of the magnetometer.
This paper studies synchronization tracking problems of heterogeneous robotic systems in the presence of kinematic uncertainties, dynamic uncertainties and communication delays. To solve the above problems, this paper...
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Three-phase grid-connected converters are widely used in renewable and electric power system applications. Due to system nonlinearity and time-variant characteristic, there are limitations in standard decoupled d-q ve...
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Three-phase grid-connected converters are widely used in renewable and electric power system applications. Due to system nonlinearity and time-variant characteristic, there are limitations in standard decoupled d-q vector control mechanism. To mitigate these limitations, a RNN vector controller trained with Levenberg-Marquardt and FATT(Forward accumulation through time) algorithm is designed. The simulation is researched by using MATLAB software, and the results show that training neural-network algorithm is effective and the system using RNN vector control method outperforms the system using conventional PI control method under low sampling rate conditions.
How to stabilize the output voltage while ensuring the maximum charging efficiency of the system is a problem faced by the development of wireless power transfer(WPT). Especially in the dynamic environment, the change...
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How to stabilize the output voltage while ensuring the maximum charging efficiency of the system is a problem faced by the development of wireless power transfer(WPT). Especially in the dynamic environment, the change of system coupling coefficient or load resistance will have a huge impact on the system. In this paper, a two-stage DC-DC converter control mode is used to control the system with the goal of dynamic charging demand. Among them, the system achieves impedance matching through the receiver DC-DC converter to ensure the transmission efficiency of the system, and in order to match the impedance, the voltage and current information in the coil is used to calculate the coupling coefficient, then, the coupling coefficient can be used to achieve maximum efficiency. The system output voltage is stabilized by using the transmitter DC-DC converter. The simulation experiment shows that the control mode can stabilize the output voltage well, and is more efficient than the single receiver closed loop control(SRCLP) system.
In the blast furnace,due to the different changing frequency of different operations,and the different reaction time of gas,liquid and solid materials,there exists multi-timescale characteristics in the iron-making **...
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ISBN:
(纸本)9781538629185
In the blast furnace,due to the different changing frequency of different operations,and the different reaction time of gas,liquid and solid materials,there exists multi-timescale characteristics in the iron-making ***,not enough attention has been paid to this characteristic,most of the analyses on the blast furnace state before are under a fixed time ***,this paper makes a analysis of influencing factors on carbon monoxide utilization rate of blast furnace based on multi-timescale ***,the factors that affect the carbon monoxide utilization rate are analyzed from different time ***,in the short time scale,the individual influencing time scale of the permeability index,total pressure difference and top temperature is found by the support vector machine(SVM) *** in the long time scale,the influencing time scale of burdening is *** the validity of each model is verified by the field data of the blast furnace.
This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties...
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ISBN:
(纸本)9781538629185
This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties and the frequency of the vibration feeder. First, a two dimensional(2D) weighing model was established based on the analysis of the dynamic characteristics of alloy weighing process. Second, a control scheme is proposed for the 2D model of alloy weighing ***, a robust iterative learning controller is developed and a stability condition of the 2D system is derived through linear matrix inequality(LMI) obtained by a 2D Lyapunov-Krasovskii function. Finally, the simulation results show that the proposed method can sufficiently improve the control precision of the alloy weighing process.
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