Heating and thermal squeezing in parametrically-driven oscillators with added noise

In this paper we report a theoretical model based on Green functions, Floquet theory and averaging techniques up to second order that describes the dynamics of parametrically-driven oscillators with added thermal noise. Quantitative estimates for heating and quadrature thermal noise squeezing near and below the transition line of the first parametric instability zone of the oscillator are given. Furthermore, we give an intuitive explanation as to why heating and thermal squeezing occur. For small amplitudes of the parametric pump the Floquet multipliers are complex conjugate of each other with a constant magnitude. As the pump amplitude is increased past a threshold value in the stable zone near the first parametric instability, the two Floquet multipliers become real and have different magnitudes. This creates two different effective dissipation rates (one smaller and the other larger than the real dissipation rate) along the stable manifolds of the first-return Poincare map. We also show that the statistical average of the input power due to thermal noise is constant and independent of the pump amplitude and frequency. The combination of these effects cause most of heating and thermal squeezing. Very good agreement between analytical and numerical estimates of the thermal fluctuations is achieved.Comment: Submitted to Phys. Rev. E, 29 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:1108.484

Analisis Yuridis Atas Turunnya Kekuatan Pembuktian Akta Notaris Menurut Uujn No. 2 Tahun 2014 Tentang Jabatan Notaris

The result of the research showed that the lower level of the force of evidence of Notarial deeds was based on Article 16, paragraph 1, paragraph 7, and paragraph 8, Article 41 with reference to Article 38, Article 39, Article 40, Article 44, Article 48, Article 50, and article 52 of Notarial Act No. 2/2014. Violating these Articles will cause an authentic deed to be lower so thast it turned to an underhanded deed. These articles do not legally protect the position of a Notary because they are not needed through a judge's verdict to prove the force of evidence of a Notarial deed. Verification for a Notarial deed which has the force of evidence as an underhanded deed must be done through a claim to a District Court to prove whether it violates the Articles above or not. If a Notary violates the provision under the Articles above, through the court's verdict, he will be given a written warning, suspension, honorable discharge, and dishonorable discharge; according to the civil law, he has to reimburse the cost, the compensation, and interest

Neural Network Detection of Fatigue Crack Growth in Riveted Joints Using Acoustic Emission

The purpose of this research was to demonstrate the capability of neural networks to discriminate between individual acoustic emission (AE) signals originating from crack growth and rivet rubbing (fretting) in aluminum lap joints. AE waveforms were recorded during tensile fatigue cycling of six notched and riveted 7075-T6 specimens using a broadband piezoelectric transducer and a computer interfaced oscilloscope. The source of 1,311 signals was identified based on triggering logic, amplitude relationships, and time of arrival data collected from the broad-band transducer and three additional 300 Hz resonant transducers bonded to the specimens. The power spectrum of each waveform was calculated and normalized to correct for variable specimen geometry and wave propagation effects. In order to determine the variation between individual signals of the same class, the normalized spectra were clustered onto a two-dimensional feature space using a Kohonen self organizing map (SOM). Then 132 crack growth and 137 rivet rubbing spectra were used to train a back-propagation neural network to provide automatic pattern classification. Although there was some overlap between the clusters mapped in the Kohonen feature space, the trained back-propagation neural network was able to classify the remaining 463 crack growth signals with a 94% accuracy and the 367 rivet rubbing signals with a 99% accuracy