Control and Treatment of Bone Cancer: A Novel Theoretical Study

The human body has symmetric bones.This paper uses control engineering concepts to design a suitable controller to synchronize two symmetric bones of the human body to control and treat bone cancer.A Nonsingular Terminal Sliding Mode Control(NTSMC)method will be employed to design the proposed control inputs.The control inputs can be the chemical drugs that can be used to treat bone cancer.The dynamical equations of bone cancer will be used to apply the designed control method and test it.For testing the designed controller,Simulink/MATLAB software will be used.The proposed controller is chattering-free,robust against uncertainties and external disturbances,and finite-time stable in the control engineering view.Bone cancer will be treated for almost one year using the proposed control method.

Analysis of a FBG sensing system with transverse uniform press by using genetic algorithm

An analytical method in a quasi-static fiber grating sensing system under transverse uniform press is proposed based on genetic algorithm The effect of population size, generations, crossover ratio and mutation ratio to genetic algorithm, and the optimization parameters of genetic algorithm were given. The relevant experimental system is constructed. The simulation and experiments show that the analytical method proposed can be applied to analyze the reflective spectra of the quasistatic FBG sensing system at transverse uniform press, the strain measurement with high-precision of 0.91% can be realized

Application of Hilbert-Huang signal processing to ultrasonic non-destructive testing of oil pipelines

In this paper, a detection technique for locating and determining the extent of defects and cracks in oil pipelines based on Hilbert-Huang time-frequency analysis is proposed. The ultrasonic signals reflected from defect-free pipelines and from pipelines with defects were processed using Hilbert-Huang transform, a recently developed signal processing technique based on direct extraction of the energy associated with the intrinsic time scales in the signal. Experimental results showed that the proposed method is feasible and can accurately and efficiently determine the location and size of defects in pipelines.

A method of line spectrum extraction based on target radiated spectrum feature and its post-processing

To improve the ability of detecting underwater targets under strong wideband interference environment,an efficient method of line spectrum extraction is proposed,which fully utilizes the feature of the target spectrum that the high intense and stable line spectrum is superimposed on the wide continuous spectrum.This method modifies the traditional beam forming algorithm by calculating and fusing the beam forming results at multi-frequency band and multi-azimuth interval,showing an excellent way to extract the line spectrum when the interference and the target are not in the same azimuth interval simultaneously.Statistical efficiency of the estimated azimuth variance and corresponding power of the line spectrum band depends on the line spectra ratio(LSR)of the line spectrum.The change laws of the output signal to noise ratio(SNR)with the LSR,the input SNR,the integration time and the filtering bandwidth of different algorithms bring the selection principle of the critical LSR.As the basis,the detection gain of wideband energy integration and the narrowband line spectrum algorithm are theoretically analyzed.The simulation detection gain demonstrates a good match with the theoretical model.The application conditions of all methods are verified by the receiver operating characteristic(ROC)curve and experimental data from Qiandao Lake.In fact,combining the two methods for target detection reduces the missed detection rate.The proposed post-processing method in2-dimension with the Kalman filter in the time dimension and the background equalization algorithm in the azimuth dimension makes use of the strong correlation between adjacent frames,could further remove background fluctuation and improve the display effect.

Dynamic Stiffness Analysis and Experimental Verification of Axial Magnetic Bearing Based on Air Gap Flux Variation in Magnetically Suspended Molecular Pump

Current and displacement stiffness are important parameters of axial magnetic bearing(AMB)and are usually considered as constants for the control system.However,in actual dynamic work situations,time-varying force leads to time-varying currents and air gap with a specific frequency,which makes the stiffness of appear decrease and even worsens control performance for the whole system.In this paper,an AMB dynamic stiffness model considering the flux variation across the air gap due to frequency is established to obtain the accurate dynamic stiffness.The dynamic stiffness characteristics are analyzed by means of the dynamic equivalent magnetic circuit method.The analytical results show that the amplitude of current and displacement stiffness decreases with frequency increasing.Moreover,compared with the stiffness model without considering the variation of flux density across the air gap,the improved dynamic stiffness results are closer to the actual results.Through the dynamic stiffness measurement method of AMB,experiments of AMB in magnetically suspended molecular pump(MSMP)are carried out and the experimental results are consistent with theoretical analysis results.This paper proposes the dynamic stiffness model of axial magnetic bearing considering the variation of flux density across the air gap,which improves the accuracy of the AMB stiffness analysis.

Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Maintenance for wind turbines has been transformed using supervised machine learning techniques. This method of automatic and autonomous learning can identify, monitor, and detect electrical and mechanical components of wind turbines and predict, detect, and anticipate their degeneration. Using a machine learning classifier and frequency analysis, we simulate two failure states caused by bearing vibrations. Implementing KNN facilitates efficient monitoring, monitoring, and fault-finding for wind turbines. It is possible to reduce downtime, anticipate breakdowns, and import offshore aspects through these technologies.

Forecasting Short Time Series with Missing Data by Means of Energy Associated to Series

In this work an algorithm to predict short times series with missing data by means energy associated of series using artificial neural networks (ANN) is presented. In order to give the prediction one step ahead, a comparison between this and previous work that involves a similar approach to test short time series with uncertainties on their data, indicates that a linear smoothing is a well approximation in order to employ a method for uncompleted datasets. Moreover, in function of the long- or short-term stochastic dependence of the short time series considered, the training process modifies the number of patterns and iterations in the topology according to a heuristic law, where the Hurst parameter H is related with the short times series, of which they are considered as a path of the fractional Brownian motion. The results are evaluated on high roughness time series from solutions of the Mackey-Glass Equation (MG) and cumulative monthly historical rainfall data from San Agustin, Cordoba. A comparison with ANN nonlinear filters is shown in order to see a better performance of the outcomes when the information is taken from geographical point observation.

A Model-free Approach to Fault Detection of Continuous-time Systems Based on Time Domain Data

In this paper, a model-free approach is presented to design an observer-based fault detection system of linear continuoustime systems based on input and output data in the time domain. The core of the approach is to directly identify parameters of the observer-based residual generator based on a numerically reliable data equation obtained by filtering and sampling the input and output signals.

A simple algorithm to calculate the pulsed sound field of a wide-band linear phased array

A simple algorithm using an impulse response for a rectangular piston element is discussed. The impulse response of linear phased array is obtained hv stumming the impulse responses of rectangular piston elements with different delay times. The output response of the linear wide-band array is equal to the convolution of impulse response functions with wide-band pulse exeiting signal. Sound field distributions and impulse responses of three kinds of transducers are compared. The results can be used to optimize the parameters of the linear phased array transducers used in uhrasonie imaging in nondestructive testing （NDT）.

Inspection of pipeline girth welds with ultrasonic phased array technique

A novel automatic ultrasonic system used for the inspection of pipeline girth welds is developed, in which a linear phased array transducer using electronic scan is adopted. Optimal array parameters are determined based on a mathematical model of acoustic field for linear phased army derived from Huygens＇ principle. The testing method and the system structure are introduced. The experimental results show that the phased array transducer system has the same detectability as that of conventional ultrasonic transducer system, but the system architecture can be simplified greatly, and the testing flexibility and the testing speed can be improved greatly.

The radial self-positioning algorithm of intelligent ultrasonic probe array in oil/gas pipeline

The working principle of the pipeline intelligent pig and the structure of ultrasonic probe array used to detect defects in oil/gas pipeline are introduced. The effects of the array＇ s radial position in pipe on defect resolution are analyzed. The causes leading to radial positioning error are investigated. An algo- rithm for the radial self-positioning is presented. The experimental results show that the radial self-positioning algorithm can eliminate the array＇ s space position error effectively and improve the defect resolution.

Methods for Detection of Viruses in Water and Wastewater

Viruses present in water might be harmful for human health and life. Nowadays over 100 pathogenic human virus species occur in water polluted with sewage. Chlorination, which is the most popular disinfection method is not able to remove easily viruses from treated water. Due to this, it’s necessary to detect viruses in water before treatment in order to determine disinfectant dose and to ensure the sanitary safety level of treated water. The aim of this article is to review viruses detection methods as well as the problems related to implementation of those methods in analysis of water and wastewater samples.

Coseismic Effects of the 2011 Magnitude 9.0 Tohoku-Oki Earthquake Measured at Far East Russia Continental Coast by Gravity and GPS Methods

This work presents the results of complex gravity observation performed at Shults Cape, Gamov peninsula (42.58°N, 131.15°E), Russia. Absolute laser gravimeter GABL type and Scintrex type relative gravimeter were used for measurement. To investigate the accuracy of tidal corrections we compared the observed tidal parameters of the main tidal waves O1 and M2 with modeled ones computed from 6 different ocean tidal models: CSR4, FES02, FES04, GOT00, NAO99 and TPX06. After discussion a theoretical model based on TPX06 ocean tides model and DDW99 non hydrostatic body tides model was used for tidal correction of absolute gravity data. Preliminary estimate of gravity effect induced by the Tohoku-Oki earthquake of11 March 2011Mw = 9.0 at Primorye territory (Russia) was found to be 5.1 ± 2.0 μGal. Co-seismic crustal displacements revealed by GPS data at Far EastRussiacontinental coast are also investigated. Volumetric dilatation of this area is observed at +1.7 × 10-8 level.

Radiation Induced Bystander Effect: From in Vitro Studies to Clinical Application

In the past 20 years, the classic paradigm in radiobiology recognizing DNA as the main target for the action of radiation has changed. The new paradigm assumes that both targeted and non-targeted effects of radiation determine the final outcome of irradiation. Radiotherapy is one of the main modality treatments of neoplastic diseases with intent to cure, or sometimes to palliate only, thus radiation-induced non-targeted effect, commonly referred to as the radiation-induced bystander effect (RIBE) may have a share in cancer treatment. RIBE is mediated by molecular signaling from radiation targeted cells to their non-irradiated neighbors, and comprises such phenomena as bystander effect, genomic instability, adaptive response and abscopal effect. Whereas first three phenomena may appear both in vitro and in vivo, an abscopal effect is closely related to partial body irradiation and is a systemic effect mediated by immunologic system which synergizes with radiotherapy. From the clinical point of view abscopal effect is particularly interesting due to both its possible valuable contribution to the treatment of metastases, and the potential harmful effects as induction of genetic instability and carcinogenesis. This review summarized the main results of investigations of non-targeted effects coming from in vitro monolayer cultures, 3-dimentional models of tissues, preclinical studies on rodents and clinically observed beneficial abscopal effects with particular emphasis on participation of immunotherapy in the creation of abscopal effects.

Ultrasonic flaw detection using EMD-based signal processing

The precise detection of flaw echoes buried in backscattefing noise caused by material microstructure is a problem of great importance in uhrasonic non-destructive testing （NDT）. In this work, empirical mode decomposition （EMD） is proposed to deal with ultrasonic signal. A time-frequency filtering method based on EMD is designed to suppress noise and enhance flaw signals. Simulated results are presented, showing that the proposed method has an excellent performance even for a very low signal-to-noise ratio （SNR）. The improvement in flaw detection was experimentally verified using stainless steel pipe sample with artificial flaws.

Wavelet domain adaptive filtering algorithm for removing the seamless pipe noise contained in the magnetic flux leakage data

With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.

Model selection for SVM using mutative scale chaos optimization algorithm

This paper proposes a new search strategy using mutative scale chaos optimization algorithm （MSCO） for model selection of support vector machine （SVM）. It searches the parameter space of SVM with a very high efficiency and finds the optimum parameter setting for a practical classification problem with very low time cost. To demonstrate the performance of the proposed method it is applied to model selection of SVM in ultrasonic flaw classification and compared with grid search for model selection. Experimental results show that MSCO is a very powerful tool for model selection of SVM, and outperforms grid search in search speed and precision in ultrasonic flaw classification.

"Smart" ultrasonic pig design and its signal processing research

This paper introduces the design and development of intelligent ultrasonic ＂pig＂ used for seabed pipeline inspection. The data acquisition system （DAS） and the signal processing method are presented. The DAS was designed using a muhi-DSP based structure. The signal processing method adopted a well-enhanced split-spectrum processing （SSP） based on weighting algorithm according to statistical times, which was the statistical distribution of the discrete signals selected by the minimization algorithm. Furthermore, the data compressing method was discussed. It assures the mass data to be processed and compressed in real-time. The effectiveness of the method was proved by laboratory experiments. The research provides an excellent base for further development of the ultrasonic pig.

Coefficients de-noising with wavelet transform for magnetic flux leakage data obtained from oil pipeline

This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.

Stability and Stabilization on Generalized Delay Stochastic Neural Network with Distributed Parameter

Inner stability and stabilization of Cohen-Grossberg generalized delay stochastic neural network with distributed parameter are discussed. The main method adopted is, combining inequality techniques, to apply Ito differential formula to the constructed average function with respect to spatial variables along the system considered under the integral operator. Some sufficient conditions are given.