Advanced Materials Research Vols. 588-589

Abstract: Central heating in some of the cities in northern China has become the main way of heating in winter. In order to save energy, reduce home heating costs and improve comfort by controlling the room temperature, it is necessary to study the central heating, to increase the degree of automation of the heating system. In this paper, a mathematical model of heating system is presented. The generalized predictive control system for heating system is developed. Then Matlab was used to simulate the method. The simulation result shown that the generalized predictive control has made very good control effect on the type of this kind of model.

Abstract: The system based on the working principle of MAX1978 and TEC, using multi-point at different frequency measurement method, and establishing corresponding mathematical model. Confirmed the pole-zero plot of the PID, according to the amplitude-frequency and phase-frequency characteristics of the mathematical model, and then, adjusted the corresponding resistance and capacitance of the circuit to make the accuracy of the temperature adjustment to 1E-3centigrade.

Abstract: In this paper we present a novel HCI (human-computer interaction) system and develop a prototype based on the characteristics of the power control system. This system includes multi-touch input module, laser pointer input module and arc projection-based tiled display module. Users can use their fingers, gestures and laser pointers to control the multi-touch platform; they can also use laser pointers to control the arc projection display system and see the feedback results through the multi-touch projection display and arc projection display. The system can make the power control system more effective and convenient because of its natural and comfortable interaction mode as well as its multi-user feature.

Abstract: As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

Abstract: Engine has a high chance of failure, it usually accounts for about 40% of vehicle failures. Study expert system of engine fault diagnosises that it can locate fault timely and accurately, and enhance efficiency. However, the traditional expert system has shortcomings so as inefficient inference and poor self-learning capability. The fuzzy logic and traditional neural networks are combined to form fuzzy neural networks, they are established a model of fuzzy neural network (FNN) of fault diagnosis, and that the model is applied to engine fault diagnosis, complementary advantages, to effectively enhance efficiency of inference and self-learning ability, its performance is higher than the traditional BP network.

Abstract: For controlling emergency escaping by a bus driver in the case of air control door cannot be opened normally, the paper presented a multi-manual emergency escaping system of pneumatic bus door. Bus driver and passenger can control doors expediently. The pneumatic circuit of the system and its overall arrangement is discussed. A manual control device with special valve core structure is designed. The working performance of the manual shift directional valve is analyzed. The results indicate that driver and passengers can control the bus doors under the state of abnormal condition via manual control devices in series connection rapidly. Driver and passengers control is without mutual interference. Manual shift directional valves are convenient, reliable and fast.

Abstract: For the unstructured extreme environment of coal mine underground accidents, this paper puts forward a multi section crawler search robots. Based on the analysis of the working environment of the robot, underground search robot of crawler - ground model is built , overall configuration of robot that can cross obstacles flexibly in the complicated unstructured environment is put forward, high mobility of walking mechanism and joint module are designed and simulate by simulation software MATLAB, which provides the basis for establishing systematic engineering prototype and completing engineering test result.

Abstract: Based on the analysis about the front and rear braking force distribution curve and the motor anti-drag braking characteristic, the Regenerative Braking Control Strategy which can maintain the capacity of the motor braking energy recovery and make the front and rear braking force distribution closer to the ideal distribution state is proposed. Create a control model. It is simulated by AVL-cruise. The results show that the new control strategies can improve the utilization of ground adhesion coefficient and braking stability.

Abstract: This paper aims to solve the optimization power allocation problem based on cognitive radio network system. We propose a Hybrid Spectrum Access (HSA) method which considers the total transmit power constraint, the peak power constraint and the primary users’ tolerance. In order to solve this combinational optimization problem and achieve the global optimal solution, we derived a Simulated Annealing-Hopfield neural networks (SA-HNN). The simulation results of the optimized ergodic capacity shows that the proposed optimization problem can be solved more efficiently and better by SA-HNN than HNN or Simulated Annealing (SA), and the proposed HSA method by SA-HNN can achieve a better ergodic capacity than the traditional methods.

Abstract: When hydraulic torque converter is applied in hydraulic transmission-vehicle, control precision in buffer locking process of hydraulic torque converter was easily disturbed by friction plate's abrasion, changed buffer slope and other factors, which accordingly caused Impact to transmission system of vehicle. In this paper, adaptive control techniques was applied in buffer locking process as a solution to improve control precision, on basis of Back Propagation (BP) Neural Networks and Genetic Algorithm (GA). In the research, the BP Neural Networks and PID control algorithm was designed to control buffer locking process and Genetic Algorithm was applied to optimize the neural network parameters. Based on AMEsim and Matlab/simulink, joint simulation was carried out. The simulation result shows that Adaptive Control Techniques based on GA-BP can control the locking process fast and accurately.