High Speed Regular Expression Matching Engine with Fast Pre-Processing

Regular expression matching is playing an important role in deep inspection. The rapid development of SDN and NFV makes the network more dynamic, bringing serious challenges to traditional deep inspection matching engines. However, state-of-theart matching methods often require a significant amount of pre-processing time and hence are not suitable for this fast updating scenario. In this paper, a novel matching engine called BFA is proposed to achieve high-speed regular expression matching with fast pre-processing. Experiments demonstrate that BFA obtains 5 to 20 times more update abilities compared to existing regular expression matching methods, and scales well on multi-core platforms.

Pre-processing and post-processing method for geostress simulation using seismic interpretation results

In order to carry out numerical simulation using geologic structural data obtained from Landmark(seismic interpretation system), underground geological structures are abstracted into mechanical models which can reflect actual situations and facilitate their computation and analyses.Given the importance of model building, further processing methods about traditional seismic interpretation results from Landmark should be studied and the processed result can then be directly used in numerical simulation computations.Through this data conversion procedure, Landmark and FLAC(the international general stress software) are seamlessly connected.Thus, the format conversion between the two systems and the pre-and post-processing in simulation computation is realized.A practical application indicates that this method has many advantages such as simple operation, high accuracy of the element subdivision and high speed, which may definitely satisfy the actual needs of floor grid cutting.

Pre-processing filter design at transmitters for IBI mitigation in an OFDM system

In order to meet the demands for high transmission rates and high service quality in broadband wireless communication systems, orthogonal frequency division multiplexing （OFDM） has been adopted in some standards. However, the inter-block interference （IBI） and inter-carrier interference （ICI） in an OFDM system affect the performance. To mitigate IBI and ICI, some pre-processing approaches have been proposed based on full channel state information （CSI）, which improved the system performance. A pre-processing filter based on partial CSI at the transmitter is designed and investigated. The filter coefficient is given by the optimization processing, the symbol error rate （SER） is tested, and the computation complexity of the proposed scheme is analyzed. Computer simulation results show that the proposed pre-processing filter can effectively mitigate IBI and ICI and the performance can be improved. Compared with pre-processing approaches at the transmitter based on full CSI, the proposed scheme has high spectral efficiency, limited CSI feedback and low computation complexity.

Scientific data products and the data pre-processing subsystem of the Chang'e-3 mission

The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.

Review of pre-processing technologies for ice cores

High-resolution ice core records covering long time spans enable reconstruction of the past climatic and environmental conditions allowing the investigation of the earth system's evolution. Preprocessing of ice cores has direct impacts on the data quality control for further analysis since the conventional ice core processing is time-consuming, produces qualitative data, leads to ice mass loss, and leads to risks of potential secondary pollution. However, over the past several decades, preprocessing of ice cores has received less attention than the improvement of ice drilling, the analytical methodology of various indices, and the researches on the climatic and environmental significance of ice core records. Therefore, this papers reviews the development of the processing for ice cores including framework, design as well as materials, analyzes the technical advantages and disadvantages of the different systems. In the past, continuous flowanalysis(CFA) has been successfully applied to process the polar ice cores. However, it is not suitable for ice cores outside polar region because of high level of particles, the memory effect between samples, and the filtration before injection. Ice core processing is a subtle and professional operation due to the fragility of the nonmetallic materials and the random distribution of particles and air bubbles in ice cores, which aggravates uncertainty in the measurements. The future developments of CFA are discussed in preprocessing, memory effect, challenge for brittle ice, coupling with real-time analysis and optimization of CFA in the field. Furthermore, non-polluting cutters with many different configurations could be designed to cut and scrape in multiple directions and to separate inner and outer portions of the core. This system also needs to be coupled with streamlined operation of packaging, coding, and stacking that can be implemented at high resolution and rate, avoiding manual intervention. At the same time, information of the longitudinal sections could be scanned andidentified, and then classified to obtain quantitative data. In addition, irregular ice volume and weight can also be obtained accurately. These improvements are recorded automatically via user-friendly interfaces. These innovations may be applied to other paleomedias with similar features and needs.

Image pre-processing research of coal level in underground coal pocket

Mathematical morphology is widely applicated in digital image procesing.Vari- ary morphology construction and algorithm being developed are used in deferent digital image processing.The basic idea of mathematical morphology is to use construction ele- ment measure image morphology for solving understand problem.The article presented advanced cellular neural network that forms mathematical morphological cellular neural network (MMCNN) equation to be suit for mathematical morphology filter.It gave the theo- ries of MMCNN dynamic extent and stable state.It is evidenced that arrived mathematical morphology filter through steady of dynamic process in definite condition.

Intelligent Data Pre-processing Model in Integrated Ocean Observing Network System

There are a number of dirty data in observation data set derived from integrated ocean observing network system. Thus, the data must be carefully and reasonably processed before they are used for forecasting or analysis. This paper proposes a data pre-processing model based on intelligent algorithms. Firstly, we introduce the integrated network platform of ocean observation. Next, the preprocessing model of data is presemed, and an imelligent cleaning model of data is proposed. Based on fuzzy clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means clustering. The proposed dynamic algorithm can automatically f＇md the new clustering center with the updated sample data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results through observation data analysis.

Signal pre-processing method and application design of edge nodes for distributed electromechanical system

A signal pre-processing method based on optimal variational mode decomposition(OVMD)is proposed to improve the efficiency and accuracy of local data filtering and analysis of edge nodes in distributed electromechanical systems.Firstly,the singular points of original signals are eliminated effectively by using the first-order difference method.Then the OVMD method is applied for signal modal decomposition.Furthermore,correlation analysis is conducted to determine the degree of correlation between each mode and the original signal,so as to accurately separate the real operating signal from noise signal.On the basis of theoretical analysis and simulation,an edge node pre-processing system for distributed electromechanical system is designed.Finally,by virtue of the signal-to-noise ratio(SNR)and root-mean-square error(RMSE)indicators,the signal pre-processing effect is evaluated.The experimental results show that the OVMD-based edge node pre-processing system can extract signals with different characteristics and improve the SNR of reconstructed signals.Due to its high fidelity and reliability,this system can also provide data quality assurance for subsequent system health monitoring and fault diagnosis.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

The Pre-processing Parallel Algorithm of A Sparse Linear Equation Group

The solution of linear equation group can be applied to the oil exploration, the structure vibration analysis, the computational fluid dynamics, and other fields. When we make the in-depth analysis of some large or very large complicated structures, we must use the parallel algorithm with the aid of high-performance computers to solve complex problems. This paper introduces the implementation process having the parallel with sparse linear equations from the perspective of sparse linear equation group.

The Role of Combined OSR and SDF Method for Pre-Processing of Microarray Data that Accounts for Effective Denoising and Quantification

Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.

On the Use of Monotonicity-Preserving Interpolatory Techniques in Multilevel Schemes for Balance Laws

Cost-effective multilevel techniques for homogeneous hyperbolic conservation laws are very successful in reducing the computational cost associated to high resolution shock capturing numerical schemes.Because they do not involve any special data structure,and do not induce savings in memory requirements,they are easily implemented on existing codes and are recommended for 1D and 2D simulations when intensive testing is required.The multilevel technique can also be applied to balance laws,but in this case,numerical errors may be induced by the technique.We present a series of numerical tests that point out that the use of monotonicity-preserving interpolatory techniques eliminates the numerical errors observed when using the usual 4-point centered Lagrange interpolation,and leads to a more robust multilevel code for balance laws,while maintaining the efficiency rates observed forhyperbolic conservation laws.

An Arbitrarily High Order and Asymptotic Preserving Kinetic Scheme in Compressible Fluid Dynamic

We present a class of arbitrarily high order fully explicit kinetic numerical methods in compressible fluid dynamics,both in time and space,which include the relaxation schemes by Jin and Xin.These methods can use the CFL number larger or equal to unity on regular Cartesian meshes for the multi-dimensional case.These kinetic models depend on a small parameter that can be seen as a"Knudsen"number.The method is asymptotic preserving in this Knudsen number.Also,the computational costs of the method are of the same order of a fully explicit scheme.This work is the extension of Abgrall et al.(2022)[3]to multidimensional systems.We have assessed our method on several problems for two-dimensional scalar problems and Euler equations and the scheme has proven to be robust and to achieve the theoretically predicted high order of accuracy on smooth solutions.

S-scheme heterojunction with ultrafast interfacial electron transfer for artificial photosynthesis

The rapid development of the global economy has led to the over-exploitation and burning of fossil fuels,causing a severe energy crisis and continuous CO_(2) emissions.Although solar energy is a clean and renewable resource,it faces significant diurnal and seasonal variations and is difficult to store[1-4].Converting solar energy into storable chemical energy through photocatalysis is an effective way to address both energy scarcity and environmental issues.Photocatalytic CO_(2) reduction,with the development of high-efficiency photocatalysts as the key,offers a clean and environmentally friendly method to convert CO_(2) into valuable hydrocarbon fuels,providing a viable solution to the global energy crisis and climate change[5,6].

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

Analysis and Comparison of Slope-cutting Widening Schemes in Highway Reconstruction and Expansion Project Based on MIDAS Software

In this paper,the geological condition of the right-side slope of the K114+694–K115+162 section of Yong-tai-wen Expressway is investigated and analyzed with the results showing that the strength of rock mass is the main contributor to the stability of the slope.Then,two widening schemes are proposed,which are the steep slope with strong support and the gentle slope with general support schemes.The static/slope module of MIDAS GTS finite element analysis software and the strength reduction method were used to compare the two schemes.The results show that the steep slope with a strong support scheme has obvious advantages in land requisition,environmental protection,and safety and is more suitable for reconstructing and expanding the highway slope.

Deployment Dynamic Modeling and Driving Schemes for a Ring-Truss Deployable Antenna

Mesh reflector antennas are widely used in space tasks owing to their light weight,high surface accuracy,and large folding ratio.They are stowed during launch and then fully deployed in orbit to form a mesh reflector that transmits signals.Smooth deployment is essential for duty services;therefore,accurate and efficient dynamic modeling and analysis of the deployment process are essential.One major challenge is depicting time-varying resistance of the cable network and capturing the cable-truss coupling behavior during the deployment process.This paper proposes a general dynamic analysis methodology for cable-truss coupling.Considering the topological diversity and geometric nonlinearity,the cable network's equilibrium equation is derived,and an explicit expression of the time-varying tension of the boundary cables,which provides the main resistance in truss deployment,is obtained.The deployment dynamic model is established,which considers the coupling effect between the soft cables and deployable truss.The effects of the antenna's driving modes and parameters on the dynamic deployment performance were investigated.A scaled prototype was manufactured,and the deployment experiment was conducted to verify the accuracy of the proposed modeling method.The proposed methodology is suitable for general cable antennas with arbitrary topologies and parameters,providing theoretical guidance for the dynamic performance evaluation of antenna driving schemes.

Improvement and security analysis of multi-ring discrete modulation continuous variable quantum secret sharing scheme

In order to avoid the complexity of Gaussian modulation and the problem that the traditional point-to-point communication DM-CVQKD protocol cannot meet the demand for multi-user key sharing at the same time, we propose a multi-ring discrete modulation continuous variable quantum key sharing scheme(MR-DM-CVQSS). In this paper, we primarily compare single-ring and multi-ring M-symbol amplitude and phase-shift keying modulations. We analyze their asymptotic key rates against collective attacks and consider the security key rates under finite-size effects. Leveraging the characteristics of discrete modulation, we improve the quantum secret sharing scheme. Non-dealer participants only require simple phase shifters to complete quantum secret sharing. We also provide the general design of the MR-DM-CVQSS protocol.We conduct a comprehensive analysis of the improved protocol's performance, confirming that the enhancement through multi-ring M-PSK allows for longer-distance quantum key distribution. Additionally, it reduces the deployment complexity of the system, thereby increasing the practical value.

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an Atmospheric Dynamical Core

Here,a nonhydrostatic alternative scheme(NAS)is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core.The NAS is designed to replace this solver,which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time.Recent advances in machine learning(ML)provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship.In this study,an ML approach called a neural network(NN)was adopted to select leading input features and develop the NAS.The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting(WRF)model.The forward time difference of the nonhydrostatic tendency was used as the target variable,and the five selected features were the nonhydrostatic tendency at the last time step,and four hydrostatic variables at the current step including geopotential height,pressure in two different forms,and potential temperature,respectively.Finally,a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution,which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency.Corrected by the NN-based NAS,the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias,anomaly root-mean-square error,and the error of the wave spatial pattern,which proves the feasibility and superiority of this scheme.