The interpolation method of discrete spectrum is applied to the engine excitation force identification. The frequency, amplitude, and phase of the vibration response of each measuring point on cylinder surface are obtained accurately based on the interpolation method. Combination with the inertia parameters and the dynamic properties of the mounts, the excitation force, and moment at the center of gravity of engine can be exactly retrieved. The nonlinear problem caused by the imprecise phase of the measurement points is avoided, and the identified method is simplified. Simulation results confirm the importance of the interpolation method on the accurate identification of the excitation force. Influences on the excitation force identification are analyzed quantitatively. The reasons causing the errors are analyzed and the avoidance methods are given too. Through the multi-rigid-body dynamics model simulation, the excitation force identification method is confirmed. Then, the proposed method is carried out on an engine.
Misfiring creates a unique pattern attributed to a particular cylinder. When a misfire occurs, the balance of the engine is destroyed, and the generalized force at the centre of gravity (C.G.) of the engine is changed. In this paper, a new misfire detection method is presented based on the identification of the generalized force at the engine centre of gravity. Based on the engine acceleration signals at the mounts, through the use of the discrete spectrum interpolation method, the accurate amplitudes and phases of the acceleration signals are extracted, and then, the generalized force at the centre of gravity is calculated. Through analysing the main harmonic orders of the generalized force, the misfire features are accurately extracted and classified. Both the simulation examples and test bed results prove the effectiveness of the present method in detecting misfire faults in combustion engines.INDEX TERMS Engine, misfire detection, the centre of gravity, the generalized force.
The traditional Morning jog attendance in colleges and universities is limited to manual supervision by swiping card attendance, which is prone to queuing and sign-in on behalf of others. In addition, the lack of quantitative records of exercise data makes supervision difficult. Location-based attendance systems represented by Dingding time attendance, or simple face recognition attendance systems cannot achieve accurate attendance. The campus happy running system based on face recognition and trajectory tracking innovatively combines face recognition and trajectory tracking, and students achieve accurate attendance certification within the designated “geo-fence”. Practice shows that the system can meet the special needs of colleges and universities for Morning jog attendance and data recording, and it effectively improves the accuracy of identity verification.
Face recognition is a popular biometric technology, it is based on facial features, face image recognition to identify the identity of the corresponding person. Face identity identification, has been widely used in banking, transportation, company attendance and other fields. This paper puts forward a face recognition system based on LBS, in which students use the mobile terminal face recognition function to complete the classroom attendance independently, and the data of the mobile terminal and the face recognition are compared to the server data to generate the attendance. The system effectively avoids manual attendance efficiency low, cheating on Attendance, the error rate is high, cannot do to the study process comprehensive supervision, cannot form the statistical data and so on.
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