如何自学武术

自学武术,只要树立学好的信心与决心,持之以恒付诸实践,就会“有志者事竟成”。 一、从基本训练入手 学什么都得讲个顺序,武术学习顺序一般是:基本功、基本动作——初级拳术——初级器械——中高级拳械套路——对练。基本训练是基础,象造楼的根基,根基打得扎实、牢固,楼才造得又高又好。当然武术的基本训练还不完全同于造楼,不一定等到基本训练全部掌握好了再开始学习套路。

Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.

Short-term photovoltaic power prediction using combined K-SVD-OMP and KELM method

For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.

Interaction of cetyltrimethylammonium bromide with drug in aqueous/electrolyte solution:A combined conductometric and molecular dynamics method study

Interaction between beta-lactum antibiotic drug ciprofloxacin hydrochloride(CFH)and cationic surfactant cetyltrimethylammonium bromide(CTAB)was performed conductometrically in aqueous as well as in the occurrence of different salts(NaCl,KCl as well as NH_4Cl)over the temperature range of 298.15–323.15 K at the regular interval of 5 K.CFH drug has been suggested for the treatment of bacterial infections such as urinary tract infections and acute sinusitis.A clear critical micelle concentration(CMC)was obtained for pure CTAB as well as(CFH+CTAB)mixed systems.The decrease in CMC values of CTAB caused by the addition of CFH reveals the existence of the interaction between the components and therefore it is the indication of micelle formation at lower concentration of CTAB and their CMC values further decrease in attendance of salts.A nonlinear behavior in the CMC versus T plot was observed in all the cases.The ΔG_m^0 values are found to be negative in present study systems demonstrated the stability of the solution.The values of ΔH_m^0 and ΔS_m^0 reveal the existence of hydrophobic and electrostatic interactions between CFH and CTAB.The thermodynamic properties of transfer for the micellization were also evaluated and discussed in detail.Molecular dynamic simulation disclosed that environment of water and salts have impact on the hydrophobic interaction between CFH and CTAB.In water and salts,CTAB adopts spherical micelle in which charged hydrophilic groups are interacted with waters whereas hydrophobic tails form the core of the micelle.This hydrophobic core region is highly conserved and protected.In addition,micelle formation is more favorable in aqueous Na Cl solution than other solutions.