Quantitative prediction of channel sand bodies based on seismic peak attributes in the frequency domain and its application

The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration.We present a new method for boundary identification and quantitative thickness prediction of channel sand bodies based on seismic peak attributes in the frequency domain.Using seismic forward modeling of a typical thin channel sand body,a new seismic attribute-the ratio of peak frequency to amplitude was constructed.Theoretical study demonstrated that seismic peak frequency is sensitive to the thickness of the channel sand bodies,while the amplitude attribute is sensitive to the strata lithology.The ratio of the two attributes can highlight the boundaries of the channel sand body.Moreover,the thickness of the thin channel sand bodies can be determined using the relationship between seismic peak frequency and thin layer thickness.Practical applications have demonstrated that the seismic peak frequency attribute can depict the horizontal distribution characteristics of channels very well.The ratio of peak frequency to amplitude attribute can improve the identification ability of channel sand body boundaries.Quantitative prediction and boundary identification of channel sand bodies with seismic peak attributes in the frequency domain are feasible.

Application of coupled analysis methods for prediction of blast-induced dominant vibration frequency

Blast-induced dominant vibration frequency （DVF） involves a complex, nonlinear and small sample system considering rock properties, blasting parameters and topography. In this study, a combination of grey relational analysis and dimensional analysis procedures for prediction of dominant vibration frequency are presented. Six factors are selected from extensive effect factor sequences based on grey relational analysis, and then a novel blast-induced dominant vibration frequency prediction is obtained by dimensional analysis. In addition, the prediction is simplified by sensitivity analysis with 195 experimental blast records. Validation is carried out for the proposed formula based on the site test database of the first- period blasting excavation in the Guangdong Lufeng Nuclear Power Plant （GLNPP）. The results show the proposed approach has a higher fitting degree and smaller mean error when compared with traditional predictions.

Frequency-Hopping Frequency Reconnaissance and Prediction for Non-cooperative Communication Network

The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping(FH)sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency(TF)analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory(LSTM)neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.

Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle

Genomic selection has been demonstrated as a powerful technology to revolutionize animal breeding. However, marker density and minor allele frequency can affect the predictive ability of genomic estimated breeding values （GEBVs）. To investigate the impact of marker density and minor allele frequency on predictive ability, we estimated GEBVs by constructing the different subsets of single nucleotide polymorphisms （SNPs） based on varying markers densities and minor allele frequency （MAF） for average daily gain （ADG）, live weight （LW） and carcass weight （CW） in 1 059 Chinese Simmental beef cattle. Two strategies were proposed for SNP selection to construct different marker densities： 1） select evenly-spaced SNPs （Strategy 1 ）, and 2） select SNPs with large effects estimated from BayesB （Strategy 2）. Furthermore, predictive ability was assessed in terms of the correlation between predicted genomic values and corrected phenotypes from 10-fold cross-validation. Predictive ability for ADG, LW and CW using autosomal SNPs were 0.13＋0.002, 0.21＋0.003 and 0.25＋0.003, respectively. In our study, the predictive ability increased dramatically as more SNPs were included in analysis until 200K for Strategy 1. Under Strategy 2, we found the predictive ability slightly increased when marker densities increased from 5K to 20K, which indicated the predictive ability of 20K （3% of 770K） SNPs with large effects was equal to the predictive ability of using all SNPs. For different MAF bins, we obtained the highest predictive ability for three traits with MAF bin 0.01-0.1. Our result suggested that designing a low-density chip by selecting low frequency markers with large SNP effects sizes should be helpful for commercial application in Chinese Simmental cattle.

Numerical Prediction Methods for Clock Deviation Based on Two-Way Satellite Time and Frequency Transfer Data

Three functional models, polynomial, spectral analysis, and modified AR model, are studied and compared in fitting and predicting clock deviation based on the data sequence derived from two-way satellite time and frequency transfer. A robust equivalent weight is applied, which controls the significant influence of outlying observations. Some conclusions show that the prediction precision of robust estimation is better than that of LS. The prediction precision calculated from smoothed observations is higher than that calculated from sampling observations. As a count of the obvious period variations in the clock deviation sequence, the predicted values of polynomial model are implausible. The prediction precision of spectral analysis model is very low, but the principal periods can be determined. The prediction RMS of 6-hour extrapolation interval is Ins or so, when modified AR model is used.

A Spectrum Prediction-Based Frequency Band Pre-Selection over Deteriorating HF Electromagnetic Environment

As the earliest invented and utilized communication approach, shortwave, known as high frequency(HF) communication now experience the deterioration of HF electromagnetic environment. Finding quality frequency in efficient manner becomes one of the key challenges in HF communication. Spectrum prediction infers the future spectrum status from history spectrum data by exploring the inherent correlations and regularities. The investigation of HF electromagnetic environment data reveals the correlations and predictability of HF frequency band in both time and frequency domain. To solve this problem, we develop a Spectrum Prediction-based Frequency Band Pre-selection(SP-FBP) for HF communications. The pre-selection of HF frequency band mainly incorporated in prediction of HF spectrum occupancy and prediction of HF usable frequency, which provide the frequency band ranking of spectrum occupancy and alternative frequency for spectrum sensing, respectively. Performance evaluation via real-world HF spectrum data shows that SP-FBP significantly improves the efficiency of finding quality frequency in HF communications.

Interval finite difference method for steady-state temperature field prediction with interval parameters

A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.

Prediction study of hydrocarbon reservoir based on time-frequency domain electromagnetic technique taking Ili Basin as an example

The time-frequency domain electromagnetic(TFEM)sounding technique can directly detect oil and gas characteristics through anomalies in resistivity and polarizability.In recent years,it has made some breakthroughs in hydrocarbon detection.TFEM was applied to predict the petroliferous property of the Ili Basin.In accordance with the geological structure characteristics of the study area,a two-dimensional layered medium model was constructed and forward modeling was performed.We used the forward-modeling results to guide fi eld construction and ensure the quality of the fi eld data collection.We used the model inversion results to identify and distinguish the resolution of the geoelectric information and provide a reliable basis for data processing.On the basis of our results,key technologies such as 2D resistivity tomography imaging inversion and polarimetric constrained inversion were developed,and we obtained abundant geological and geophysical information.The characteristics of the TFEM anomalies of the hydrocarbon reservoirs in the Ili Basin were summarized through an analysis of the electrical logging data in the study area.Moreover,the oil-gas properties of the Permian and Triassic layers were predicted,and the next favorable exploration targets were optimized.

A PREDICTION TECHNIQUE FOR DYNAMIC ANALYSIS OF FLAT PLATES IN MID-FREQUENCY RANGE

The wave-based method （WBM） has been applied for the prediction of mid-frequency vibrations of fiat plates. The scaling factors, Gauss point selection rule and truncation rule are introduced to insure the wave model to converge. Numerical results show that the prediction tech- nique based on WBM is with higher accuracy and smaller computational effort than the one on FEM, which implies that this new technique on WBM can be applied to higher-frequency range.

A STATISTICAL MODEL FOR PREDICTION OF INTENSITY AND FREQUENCY OF TROPICAL CYCLONES MAKING LANDFALL ON CHINA

Based on NCEP/NCAR reanalysis data and Yearbook of China landfalling tropical cyclones(hereafter CLTC) from 1949 to 2008,correlation between CLTC frequency/intensity and 500 hPa height field and sea surface temperature(SST) fields are investigated and employed for TC statistical prediction.A prediction model for yearly and monthly intensity and frequency of CLTC is established with binomial curve fitting by choosing the gridpoints with high correlation coefficients as composite factors.Good performance of the model in experiments shows that the model could be used in routine forecast.

TIME-FREQUENCY 2-D LMS BASED LONG-RANGE CHANNEL PREDICTION FOR WIRELESS OFDM SYSTEMS

Adaptive modulation can optimize the spectrum efficiency and system performance with the channel state information achieved by the long-range channel prediction. To avoid re-estimating channel correlation function as the channel stationarity varies and to track the channel adaptively, LMS （Least-Mean-Square） based long-range channel prediction is discussed in the existing literature, but it needs long observation interval to reach the convergence. Given that all OFDM （Orthogonal Frequency Division Multiplexing） subcarriers have the identical time-domain correlation and stationarity during the same time interval, this paper proposed a 2-D LMS based predictor which updates the filter weights in both time and frequency domain. The proposed scheme can effectively decrease the observation intervals and significantly speed up the convergence than the conventional LMS and Parallel LMS （PLMS）. Complexity analysis and simulation results prove that the proposed scheme can improve the BER （Bit Error Rate） performance and spectrum efficiency with negligible complexity increase.

A Disturbance Source Location Method on the Low Frequency Oscillation With Time-varying Steady-state Points

The low frequency oscillation is a serious threat to security and stability of a power grid.How to locate the disturbance source accurately is an important issue to low frequency oscillation disposal.Existing methods have poor adaptability to the low frequency oscillation with time-varying steady-state points because of the limitations in the location criterion derivation.A disturbance source location method on a low frequency oscillation with good generality is presented in the paper.Firstly,the reasons why the steady-state points are time-varying on a low frequency oscillation are analyzed.Then,based on the energy function construction form,the branch transmission energy is decomposed into state energy,reciprocating energy and dissipation energy by mathematical derivation.The flow direction of the dissipation energy shows the source and destination of the disturbance energy,and the specific location of a disturbance source can be identified according to its flow direction.Meanwhile,to meet the needs of energy calculation,a recognition method on the electrical quantities steady-state points is also presented by using the cubic spline interpolation.Simulation results show the correctness of the derivation and analysis on energy structure in the paper,and the disturbance source can be located accurately according to the dissipation energy.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

Atrial Fibrillation during Cerebral Infarction in Brazzaville: Frequency and Predictive Factors

In order to contribute to the improvement of brain infarction management in Brazzaville, a cross-sectional and analytical study with prospective data collection was conducted in the cardiology and neurology departments of the Brazzaville University Hospital, from February 1 to July 31, 2018. It included patients hospitalized for cerebral infarction confirmed with imaging, and having done an etiological assessment with at least one electrocardiogram at rest and one of long duration. Among these 138 patients included, 11 had atrial fibrillation, equaling?a frequency of 7.9%. The mean age of AF patients was 71 ± 8.8 years. The cardiovascular risk factors found were hypertension in eight cases (72.7%), diabetes in five cases (45.5%), abdominal obesity in four cases (36.4%). AF was permanent in 10 cases (91%), and paroxysmal in one case (9%). It was valvular in three cases (27.3%) and non-valvular in eight cases (72.7%). The cardiopathy involved was hypertensive in seven cases (63.6%), ischemic and valvular in two cases each. The CHA2DS2-VASc score, calculated in eight patients, was an average of 2.2, and ≥2 in more than 80% of patients;HAS-BLED score of 2.4 on average was ≥?3 in more than 72% of patients. Digoxin was prescribed in seven cases (63.6%) and an anti-vitamin K in eight cases (72.7%). In multivariate analysis, age (OR = 20.10, p = 0.023), arterial hypertension (OR = 23.82, p = 0.011), and dyslipidemia (OR = 2.03, p = 0.032) were the predictive factors found. AF is infrequent during brain infarction in Brazzaville. This systematic research raises the problem of age in our context.

Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.

Kautz Function Based Continuous-Time Model Predictive Controller for Load Frequency Control in a Multi-Area Power System

A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model Predictive Controller(MPC)design.MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix.In this paper,the optimal closed loop feedback gain matrix was calculated using Kautz function.Being an Orthonormal Basis Function(OBF),Kautz function has an advantage of solving complex pole-based nonlinear system.Genetic Algorithm(GA)was applied to optimally tune the Kautz function-based MPC.A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC.The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral(PI)controller and Linear Quadratic Regulation(LQR).

Model predictive control of three-level active front-end converter with low switching frequency

In medium voltage-high power(MV-HP)applications,the high switching frequency of power converter will result in unnecessary energy losses,which directly affect efficiency.To resolve this issue,a novel finite control set-model predictive control(FCS-MPC)with low switching frequency for three-level neutral point clamped-active front-end converters(NPC-AFEs)is proposed.With this approach,the prediction model of three-level NPC-AFEs is established inα-βreference frame,and the control objective of low average switching frequency is introduced into a cost function.The proposed method not only achieves the desired control performance under low switching frequency,but also performs the efficient operation for the three-level NPC-AFEs.The simulation results are provided to verify the effectiveness of proposed control scheme.

A VSFPWM Strategy for High Frequency Torque Ripple Control of PMSM based on Stator Flux Ripple Prediction

Vector-controlled AC motor drives utilize pulse width modulation(PWM)to synthesize the desired output voltage of the voltage source inverter(VSI).In space vector PWM(SVPWM)techniques,the average realization of the space vector applying the volt-sec balance principle results in an instantaneous error voltage that generates high frequency torque ripple.It may lead to an increase in motor vibration and acoustic noise.This article presents a high frequency torque ripple prediction model based on stator flux ripple and proposes a targeted designed variable switching frequency PWM(VSFPWM)strategy to diminish high frequency torque ripple.The switching frequency is dynamically adjusted according to the peak value of the predicted stator flux ripple to mitigate high frequency torque ripple.In contrast to existing strategies,the strategy outlined in this article directly suppresses high frequency torque ripple,thus remaining unaffected by inaccurate motor parameters.Additionally,due to the introduction of the power factor angle,the proposed strategy can better adapt to the full speed range operating conditions of the motor.Detailed simulations and experiments are provided to validate the effectiveness of the proposed strategy.

Comparison of dominant frequency attenuation of blasting vibration for different charge structures

Dominant frequency attenuation is a significant concern for frequency-based criteria of blasting vibration control.It is necessary to develop a concise and practical prediction equation describing dominant frequency attenuation.In this paper,a prediction equation of dominant frequency that accounts for primary parameters influencing the dominant frequency was proposed based on theoretical and dimensional analyses.Three blasting experiments were carried out in the Chiwan parking lot for collecting blasting vibration data used to conduct regression analysis of the proposed prediction equation.The fitting equations were further adopted to compare the reliability of three different types of dominant frequencies in the proposed equation and to explore the effects of different charge structures on the dominant frequency attenuation.The apparent frequency proved to be more reliable to express the attenuation law of the dominant frequency.The reliability and superiority of the proposed equation employing the apparent frequency were verified by comparison with the other five prediction equations.The smaller blasthole diameter or decoupling ratio leads to the higher initial value and corresponding faster attenuation of the dominant frequency.The blasthole diameter has a greater influence on the dominant frequency attenuation than the decoupling ratio does.Among the charge structures applied in the experiments,the charge structure with decoupling ratio of 1.5 and blasthole diameter of 48 mm results in the greatest initial value and corresponding fastest attenuation of the dominant frequency.

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on shorttime stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.