all-zero signal 中文意思是什麼
all-zero signal
解釋
全零信號-
The traditional detection algorithm, based on zero - crossing or energy, will not acquire ideal effect when the signal - to - noise is low or the signal is weaker. therefore, to resolve the real problem in the real environment that all kinds of random noise and speech signal exit together, some new algorithm must be put forward. account for the complexity of real noise, we integrate the wavelet transform and high - order statistics and advance a new algorithm ; the algorithm can effectively separate the speech signal and the non - gauss noise
基於過零率和能量的傳統檢測演算法，在噪聲環境比較復雜的情況下效果很不穩定，尤其是信噪比較低或者語音信號較弱時，檢測效果很不理想，因此，在多種語言和噪聲隨機出現、噪聲和語音強弱不一的實際噪聲環境下，必須利用新的演算法提取有用信號和噪聲信號的有效特徵，才能解決實際的問題。 -
We reconstructed the phase space and calculated the nonlinear parameters such as correlation dimension, the largest lyapunov exponent, approximate entropy, and l - z complexity of the data. it can be conclud from the results that the reconstruction of heart beat rate signal is strange, its correlation dimension is between 5 to 7 and have the character of fractal dimension, its largest lyapunov exponent is larger than zero, its approximate entropy and l - z complexity are obviously differ from noise. we can draw a conclusion from all above : the heart beat rate signal is n ' t simple noise, it is high dimensional chaos obeys certain dynamical law
我們還對信號進行了相空間重構，計算了信號的關聯維數、最大lyapunov指數、近似熵和復雜度這幾個非線性特徵量，我們發現，心率信號的吸引子是奇怪吸引子，關聯維數介於5到7之間，具有分維的特徵，其最大lyapunov指數大於0 ，其近似熵值和復雜度值明顯區別于噪聲，這說明心率信號不是隨機噪聲，它是服從確定性動力學規律的高維混沌信號。 -
The result of fault diagnosis simulation tests indicates that the fault diagnosis system could make training error reach to the aim value quickly and efficaciously for all pulverizing system fault samples. at the same time, simulation tests prove that the fault samples with the signal of zero or one of pulverizing system and bp neural network model are correct, and this system faults can be diagnosed exactly and quickly. obviously, this research is successful and lay the foundation for the development of pulverizing fault diagnostic system
其故障診斷模擬實驗結果表明，應用本文所開發研究的制粉系統各故障樣本及其相關故障樣本訓練時均能快速有效地收斂於一個設定的系統誤差值；同時其故障診斷的模擬實驗證明了本文所建立的以0 、 1為徵兆量的制粉系統故障樣本和bp神經網路模型是正確的，且能快速、較準確地對故障情況作出判斷，顯然，本文的工作是有成效的，為制粉系統故障系統進一步開發奠定了基礎。
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