ICP-AES分析信号的变动 Ⅰ.载气的影响

本文使用多道ICP光谱仪,在不同载气流速下同时观测14条常用的,具有不同特性的谱线信号,研究它们相关性和变动特征。结果表明:就选用一个内标元素作为具有不同特性谱线的内标,对提高测定精度而言,最佳载气流速应是低载气流速。

ICP-AES分析信号的变动——Ⅱ.观测高度和冷却气的影响

本文使用多道ICP发射光谱仪研究了观测高度和冷却气流速对7条物理特性差异很大的谱线信号间的相关性的影响。结果表明,载气流速的大小影响观测高度和各谱线信号间的相关性;冷却气流速大小对相关性似乎也有一定的影响。本实验似乎还表明,就用一条内标线提高具有不同特性谱线测定精度而言,最佳测定条件应是低载气流速、低冷却气流速,稍偏低的观测高度。

Approach for epileptic EEG detection based on gradient boosting

The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.

Investigation on the automatic parameters extraction of pulse signals based on wavelet transform

This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prerequisite is to detect the corners of pulse signals correctly. Up to now,the pulse parameters are mostly acquired by marking the pulse corners manually,which is an obstacle to modernize pulse diagnosis. Therefore,a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately,which can be expected to facilitate the modernization of pulse diagnosis.

dentification of blasting vibration and coal-roc fracturing microseismic signals

A new method based on variational mode decomposition （VMD） is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.

Identification of diesel front sound source based on continuous wavelet transform

Acoustic signals from diesel engines contain useful information but also include considerable noise components To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for the characterization of engine acoustics. This paper first reviews CWT characteristics represented by short duration transient signals. Wavelet selection and CWT are then implemented and wavelet transform is used to analyze the major sources of the engine front's exterior radiation sound. The research provides a reliable basis for engineering practice to reduce vehicle sound level. Comparison of the identification results of the measured acoustic signals with the identification results of the measured surface vibration showed good agreement.

A novel stabilization approach for small signal disturbance of power system with time-varying delay

Small signal instability may cause severe accidents for power system if it can not be dear correctly and timely. How to maintain power system stable under small signal disturbance is a big challenge for power system operators and dispatchers. Time delay existing in signal transmission process makes the problem more complex. Conventional eigenvalue analysis method neglects time delay influence and can not precisely describe power system dynamic behaviors. In this work, a modified small signal stability model considering time varying delay influence was constructed and a new time delay controller was proposed to stabilize power system under disturbance. By Lyapunov-Krasovskii function, the control law in the form of nonlinear matrix inequality （NLMI） was derived. Considering synthesis method limitation for time delay controller at present, both parameter adjustment method by using linear matrix inequality （LMI） solver and iteration searching method by solving nonlinear minimization problem were suggested to design the controller. Simulation tests were carried out on synchronous-machine infinite-bus power system. Satisfactory test results verify the correctness of the proposed model and the feasibility of the stabilization approach.

Diagnosis of Valve-Slap of Diesel Engine with EEMD-EMD-AGST Approach

A hybrid of ensemble empirical mode decomposition and empirical mode decomposition (EEMD-EMD) is introduced to diagnose the valve-slap vibration signal,which is relative to the dominant combustion knock vibration signal given out by a diesel engine around the top dead center (TDC).The time-frequency representations of intrinsic mode functions (IMFs) decomposed by EEMD-EMD are obtained by adaptive generalized S transform (AGST).A type 493 diesel engine was used for the experiment,and the result indicates that the valve-slap of the diesel engine is serious,and the vibration frequencies are higher than the combustion knock.With EEMD-EMD-AGST approach,the valve-slap can be identified by the vibration analysis of the diesel engine.

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.

Nonlinear Dynamics of Permanent-magnet Synchronous Motor with v/f Control

The nonlinear dynamics of permanent-magnet synchronous motor(PMSM) with v/f control signals is investigated intensively.First,the equilibria and steady-state characteristics of the system are formulated by analytical analysis.Then,some of its basic dynamical properties,such as characteristic eigenvalues,Lyapunov exponents and phase trajectories are studied by varying the values of system parameters.It is found that when the values of the system parameters are smaller,the PMSM operates in stable domains,no matter what the values of control gains are.With the values of parameters increasing,the unstability appears and PMSM falls into chaotic operation.Furthermore,the complex dynamic behaviors are verified by means of simulation.