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This paper presents a measurement scheme of the motor shaft power based on the phase detection using the improved all phase fast Fourier transform (APFFT). This method can measure the phase difference and frequency of the output signals from the torque-speed sensor connected to the motor, which are proportional to the torque and revolving speed, respectively. A signal processing circuit is proposed to stabilize the amplitude of the output signals from the torquespeed sensor which fluctuates with the change of operating states of the motor. In addition, STM32 single-chip computer is applied to sample the output signals, and an improved APFFT method is performed on this microprocessor, which can detect the phase with a high measurement accuracy. It is proved that the improved APFFT method is immune to the interference of the harmonic and direct current noise, and it has a high accuracy when the Gaussian noise is not server. Experiment results are shown to prove the validity of the proposed scheme.

To realize the multi-targets detection, tracking and identification by the dissimilar sensors fusion in the air early-warning and surveillance system, the multi-targets joint detection, tracking and recognition strategy based on the random finite set model for the dissimilar sensors fusion is studied. Through the unified description of the target’s kinematics state and target recognition attribute state, the multi-targets state is modeled as a global state described by a random finite set. By analyzing the kinematic sensor and attribute sensor models, the various sensors are modeled as a global sensor, and then each dissimilar sensor measurement is modeled as a global measurement described by a random finite set. According to the global state and the global measurement model, the process of multi-targets joint detection, tracking and recognition by dissimilar sensors fusion is described as a Bayesian filtering process, and the corresponding strategy of multi-targets joint detection, tracking and recognition has been proposed. Simulated experiment is given to prove the correctness and effectiveness of the proposed strategy.

Considering two temperature layers model of 5 kW SOFC (solid oxide fuel cell) stack not only dramatically reduces complexity of the model, but also shows the higher precision, which should be preferred in the controller design. Taylor series for the model are adopted, and then based on the static output power point, the constrained predictive controller is designed considering the fuel exhaustion and thermal constraints. The temperature and its gradient, output power, and fuel exhaustion are emphatically analyzed, which can ensure the fast power tracking and thermal safety simultaneously. Besides, the control standards of the fuel exhaustion show more rigorous compared to operating safety, which is comprehensively considered during controller design. The result shows that although power tracking process accelerated with the increase of the current regulation rate, the risk coefficient of the operating safety is increasing too.

As a high order and non-linear complex system, a control-oriented reduced order model is first designed for the 5kW solid oxide fuel cell (SOFC) system, and then its dynamic and static performances with the four temperature layers, three temperature layers, and two temperature layers are respectively discussed. Although the simulation results show some theoretical design error with the different operating points in the different temperature layer system model, including the electrical static output characteristics (V-P characteristic curve, current density distribution), thermal static output characteristic (solid layer temperature, temperature distribution at the air side), and fluid static output characteristics (H 2 O > H 2 and O 2 ), the reduced order system model can reflect the static characteristics of the SOFC stand-alone system pretty accurately. As the response curve and response time scale of dynamic output characteristics (current density, fuel cell output voltage, and system output power) tend the same in the different temperature layers model, thus it can be argued that the characteristics of the original model are very effectively retained in the reduced order model. Finally, two temperature layers model not only dramatically reduces the complexity of the model, but also shows the higher precision, which should be preferred in the controller design.

It is of great concern to relevant users to accurately describe the tolerance of sensitive equipment to voltage sag. Based on this, this paper presents a description of the voltage sag tolerance of the actual sensitive process by using the process immunity time and the corresponding indexes. The production process controlled by the sensitive equipment is regarded as the sensitive process, and the immunity time is obtained by the change of physical parameters which can be monitored as well as quantitatively reflect the process affected by the voltage sag. Then an interval characterizing the voltage sag tolerance of the sensitive process can be obtained by analyzing and calculating the proposed indexes. The rationality and feasibility of the proposed method are verified by modeling and simulating the speed regulation process of the motor controlled by AC-DC in MATLAB/Simulink.

Semiconductor quantum dots (QDs) have drawn considerable attention since its emergence because they have high brightness and a long luminous time. Our aim is to develop a new assay based on QDs to determine Alpha fetal protein (AFP) by immunofluorescence assay (IFA). Two different monoclonal antibodies of anti-AFP were labeled by QDs and the magnetic beads. After combination with AFP antigen, the fluorescence intensity of QDs was detected by the fluorospectrophotometer. The method was linear to 400 ng/mL AFP with a detection limit of 1.2 ng/mL. The intra-assay imprecision of two different level serums were 5.81% and 5.53%; the inter-assay imprecision values were 6.26% and 6.15%. The proposed method may be applied in the clinical lab.