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Based on the status of tower crane safety monitoring for construction project, the Internet of things technology is applied for tower crane safety monitoring and early warning system, the WSN nodes are designed for displacement, stress and distance monitoring function. Through the wireless transceiver module CC2530, field monitoring data are uploaded timely and accurately to the monitoring center PC, for real-time display and processing. If the monitoring data exceeds the alarm value, the alarm signal will be sent out immediately, to prevent tower crane accidents effectively, and ensure the safe of crane operation. By the network debugging, WSN node performance is reliable, and the measuring data is accurate. It has high performance to price ratio and application value.

According to general piezoelectric acceleration sensor measurement circuit existing problems in the dynamic testing field, the paper proposes the modular, integration design method, using the integrated chips CA3140 to design the signal disposal circuit, in order to reduce the cost and improve the circuit SNR, enhance the stability and reliability of the circuit. This circuit includes a charge amplifier, conditional amplifier, low-pass filter, high-pass filter and output amplifier. The test shows that the circuit functions are complete, it is practical and reliable. It has a higher performance price ratio, and can be widely used in the dynamic monitoring and the diagnosis of fault or defect about the equipment and structure.

To overcome the difficult in modeling the mode theory for magnetic flywheel, by non-parametric frequency domain identification method for identification of system model, this paper select the PID neural network nonlinear intelligent control methods based on GA. Used a method based on iterative process to suppress the resonance, combined with zero-pole theory, this research designed a GA-based PID neural network identification controller to address the modal vibration suppression problems in the external rotor flywheel .The experiments for this paper proved the effectiveness of iterative process, and the controller can restrain the system mode vibration well.

The magnetic flywheel is widely used in energy storage and spacecraft, and it needs to reduce the system’s power consumption effectively when designing the controller. Always, the magnetic bearing system’s performance depends on the tracking performance of the desired force signal. In order to balance the two goals between reducing magnetic system’s power consumption and guaranteeing its performance, a current power allocation way was proposed, and a static current optimization was given based on the way. According to the simulation and experimental results, this method can reduce the magnetic bearing system’s power consumption significantly and keep the tracking performance of force signal well on the same time.

Electric vehicle is an important developing trend of the vehicle industry and the power and technique field. But nowadays, there still exist some problems in this field which cant be solved with mature solutions, such as long time of charging, high cost of replacing and charging station and large areas it covers, low efficiency and so on. By building an effective model, this paper brings up a conceptual design of an automatic system of replacing and charging batteries for electric vehicles with the late-model design of the multi-station device. By observing the effect of the experimental device, it can solve the problems mentioned above, but more should be done to improve it. This design, the demo system, mainly includes electric vehicle model system, replacing and charging station system and GPS navigation system, and it performs excellent in experiment.

According to the characteristics of jumping locomotion, a solution of Tetra-state Hopping Robot (for short: THR) actuated by Shape Memory Alloy Spring (for short: SMAS) is presented in this paper. This article establishes a mechanical model of the jumping robot and analyzes the kinematics and the dynamics of the mechanical model. Lagrange method is applied to analyze the dynamics the mechanical model. Meanwhile, a preliminary explanation of the THR flipping phenomenon during the take-off and flight process is conducted from the perspective of momentum moment.

An idea of self-helpingly exchanging and charging batteries is proposed to solve some Battery Exchanging and Charging (BEC, for short) problems of Electric-Networked Vehicles (short for, EN-V). Large scale BEC stations occupy large working area and need high equipment cost; common charge piles have disadvantages of long charging time. As a result, a physical demonstration system, which encompasses intelligent vehicle system, BEC stake system and GPS (Global Positioning System) simulation system, is established to validate the feasibility of the idea.

Based on the principle of axial active magnetic bearing rotor-system with single degree of freedom, magnetic rotor-system vibration mode was described. A 3D finite element model of active magnetic bearing rotor-system was established by ANSYS11. Initial six order natural frequencies and vibration mode were calculated by the module of modal analysis of ANSYS11, and compared with the results of system identification. Analysis shows that there was a certain difference between the results, but the difference was small. The analysis result indicates that it is an effective method to apply FEA in the design of the active magnetic bearing. It provided the foundation for structure optimal design of the system and theoretical basis for flexible system design.

In order to overcome the system nonlinear instability and uncertainty inherent in magnetic bearing systems, two PID neural network controllers (BP-based and GA-based) are designed and trained to emulate the operation of a complete system. Through the theoretical deduction and simulation results, the principles for the parameters choice of two neural network controllers are given. The feasibility of using the neural network to control nonlinear magnetic bearing systems with un-known dynamics is demonstrated. The robust performance and reinforcement learning capability in controlling magnetic bearing systems are compared between two PID neural network controllers.