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.

Effect of Loading Frequency on the High Cycle Fatigue Strength of Flax Fiber Reinforced Polymer Matrix Composites

Among natural fibers,flax fiber reinforced polymer matrix composites show excellent dynamic/fatigue properties due to its excellent damping properties.Knowledge about fatigue limit and effect of loading frequency on fatigue limit is very crucial to know before being used a member as a structural component.Fatigue limit of fiber reinforced composite is measured through high cycle fatigue strength(HCFS).The effect of loading frequency on the HCFS of flax fiber reinforced polymer matrix composites was investigated using stabilized specimen surface temperature based thermographic and dissipated energy per cycle-based approaches.Specimens of unidirectional flax fiber reinforced thermoset composites were tested under cyclic loading at different percentages of applied stresses for the loading frequencies of 5,7,10,and 15 Hz in order to determine the stabilized surface temperature of the specimen and dissipated energy per fatigue cycle.Both approaches predicted similar fatigue limits(HCFS)which showed a good agreement with experimental results from Literature.HCFS of flax fiber reinforced composites decrease little with increasing loading frequency.Furthermore,effect of loading frequency on stabilized specimen temperature and dissipated energy per fatigue cycle was also investigated.Although specimen surface temperature increases with loading frequency,dissipated energy per-cycle does not change with loading frequency.Thermal degradation at higher loading frequencies may play a significant role in decreasing HCFS with increasing loading frequency.

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.

On indexing in the Web of Science and predicting journal impact factor

We discuss what document types account for the calculation of the journal impact factor （JIF） as published in the Journal Citation Reports （JCR）. Based on a brief review of articles discussing how to predict JIFs and taking data differences between the Web of Science （WoS） and the JCR into account, we make our own predictions. Using data by cited-reference searching for Thomson Scientific＇s WoS, we predict 2007 impact factors （1Fs） for several journals, such as Nature, Science, Learned Publishing and some Library and Information Sciences journals. Based on our colleagues＇ experiences we expect our predictions to be lower bounds for the official journal impact factors. We explain why it is useful to derive one＇s own journal impact factor.

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.

Mesoplasticity Approach to Studies of the Cutting Mechanism in Ultra-precision Machining

There have been various theoretical attempts by researchers worldwide to link up different scales of plasticity studies from the nano-, micro- and macro-scale of observation, based on molecular dynamics, crystal plasticity and continuum mechanics. Very few attempts, however, have been reported in ultra-precision machining studies. A mesoplasticity approach advocated by Lee and Yang is adopted by the authors and is successfully applied to studies of the micro-cutting mechanisms in ultra-precision machining. Traditionally, the shear angle in metal cutting, as well as the cutting force variation, can only be determined from cutting tests. In the pioneering work of the authors, the use of mesoplasticity theory enables prediction of the fluctuation of the shear angle and micro-cutting force, shear band formation, chip morphology in diamond turning and size effect in nano-indentation. These findings are verified by experiments. The mesoplasticity formulation opens up a new direction of studies to enable how the plastic behaviour of materials and their constitutive representations in deformation processing, such as machining can be predicted, assessed and deduced from the basic properties of the materials measurable at the microscale.

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.

Notes on Extended-Range Atmospheric Prediction in the Northern Hemisphere Winter

We examined the characteristic feature and predictability of low frequency variability (LFV) of the atmosphere in the Northern Hemisphere winter (January and February) by using the empirical orthogonal functions (EOFs) of the geopotential height at 500 hPa. In the discussion, we used the EOFs for geostrophic zonal wind (Uznl) and the height deviation from the zonal mean (Zeddy). The set of EOFs for Uznl and Zeddy was denoted as Uznl-1, Uznl-2, ..., Zeddy-1, Zeddy-2, ..., respectively. We used the data samples of 396 pentads derived from 33 years of NMC, ECMWF and JMA analyses, from January 1963 to 1995. From the calculated scores for Uznl-1, Uznl-2, Zeddy-1, Zeddy-2 and so on we found that Uznl-1 and Zeddy-1 were statistically stable and their scores were more persistent than those of the other EOFs. A close relationship existed between the scores of Uznl-1 and those of Zeddy-1. 30-day forecast experiments were carried out with the medium resolution version of JMA global spectral model for 20 cases in January and February for the period of 1984-1992. Results showed that Zeddy-1 was more predictable than the other EOFs for Zeddy. Considering these results, we argued that prediction of the Zeddy-1 was to be one of the main target of extended-range forecasting.

Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

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.

THE RELATION BETWEEN MAGNITUDE AND FREQUENCY IN ROCKBURST AND ITS APPLICATION IN FORECASTING

The rockburst data of Huafeng and Da’anshan Mine were analysised in this paper. From the statistical results, we know that the relation between magnitude and frequency in rockburst is linear. Using this relation, the maximum magnitude and tendency of rockburst can be predicted.The theory can improve the quantitive prediction level in rockburst.

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.

Model Predictive Control Circuit of the Current Source Matrix Converter

In this paper, a new predictive control strategy for current source matrix converter (CSMC) is presented. Proposed predictive control strategy allows for creating output voltages with boost type voltage transfer ratio and desired frequency. The description of predictive control circuit of the CSMC is presented. Furthermore the simulation test results to confirm functionality of the proposed control strategy and converter properties under this strategy are shown.

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.

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.

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.

Multivariable Decoupling Predictive Control Based on QFT Theory and Application in CSTR Chemical Process

A novel method of incorporating generalized predictive control (GPC) algorithms based on quantitative feedback theory (QFT) principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output (MIMO) plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output (SISO) generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.

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.