Design of Enterprise Economic Information Management System Based on Big Data Integration Algorithm

In economic growth, the gradual increase in the effect of information technology makes the enterprise economic information management increasingly important for the survival and development of the enterprises. This paper designs an enterprise economic information management system for the complex internal economic information management business and process of enterprises. It provides daily office, information access, document preview, and transmission. The proposed design (i) copes with the inconsistency and irregularity of enterprise economic information data, (ii) quickly obtains valuable information from these massive high-frequency data, and (iii) improves the economic benefits of data assets and data management efficiency. The printing function systematizes the information management for departments such as enterprise economic information, personnel, and production. The main focus of this research includes the mode, framework, and function of the whole system software. Moreover, it also comprises of the use of Internet platform big data technology to realize the practicality, stability, and security of the system database algorithm, which has been practically used by enterprises to improve office efficiency and meet the needs of daily management of enterprises. Based on the analysis of the current status of enterprise big data application, this paper constructs an enterprise economic informational management system based on big data and also describes in detail the key technologies of enterprise economic informational data management from three aspects: NoSQL-based big data storage management, Hadoop-based economic informational big data informational and economic informational big data analysis, and mining algorithm. Provide theoretical basis and basic technical support for online decision analysis.

Stream Classification Algorithm Based on Decision Tree

Due to the rise of many fields such as e-commerce platforms, a large number of stream data has emerged. The incomplete labeling problem and concept drift problem of these data pose a huge challenge to the existing stream data classification methods. In this respect, a dynamic stream data classification algorithm is proposed for the stream data. For the incomplete labeling problem, this method introduces randomization and iterative strategy based on the very fast decision tree VFDT algorithm to design an iterative integration algorithm, and the algorithm uses the previous model classification result as the next model input and implements the voting mechanism for new data classification. At the same time, the window mechanism is used to store data and calculate the data distribution characteristics in the window, then, combined with the calculated result and the predicted amount of data to adjust the size of the sliding window. Experiments show the superiority of the algorithm in classification accuracy. The aim of the study is to compare different algorithms to evaluate whether classification model adapts to the current data environment.

Precomputation of Critical State Soil Plastic Models

In this paper, a simple precomputing procedure is proposed to improve the numerical performance of the technological application of critical state soil models. In these models, if associated plasticity is assumed, the normalization of the stress space allows both the yield surface and the plastic components of the elastoplastic matrix to be defined as a function of a single variable. This approach facilitates their parameterization and precomputation, preventing the repetition of calculations when the boundary value problems appear at the yield surface with the calculation of plastic strain. To illustrate the scope of the procedure, its application on a modified Cam Clay model is analysed, which shows that the method allows a significant reduction of about 50% (as compared with the conventional explicit integration algorithm) in the computational time without reducing the precision. Although it is intended for critical state models in soils, the approach can be applied to other materials and types of constitutive models provided that parameterization is possible. It is therefore a methodology of practical interest, especially when a large volume of calculations is required, for example when studying large-scale engineering systems, performing sensitivity analysis, or solving optimization problems.

Interperiod coherent integration of the received signal with a variable repetition period of the probing signal

Interperiod coherent integration of the received signal provides an increase in the signal-to-noise ratio and is simply implemented with a fixed repetition period of the probing signals. In practice, pulsed radars use a variable repetition period to protect against blind speeds. The algorithms of the interperiod coherent integration with a variable repetition period have been developed and their features have been revealed, which are advisable to take into account in the practical implementation in the radars. These features determine the complexity of the interperiod coherent integration algorithm, the radial velocity (Doppler frequency) survey interval and the spectrum features. An algorithm is developed with simultaneous interperiod coherent integration of the received signal and a single-delay clutter cancelation in the spectral domain in the case of variable repetition period of the probing signals. The quantitative indicators obtained by modeling are presented and a comparative analysis is carried out.

Impact of Sports Wearable Testing Equipment Based on Vision Sensors on the Sports Industry

Sports wearable monitoring equipment is an intelligent device that collects many physiological signals of the human body through multiple sensors. It has a very active role in promoting data testing in the field of sports. This article is aimed at studying the impact of sports wearable detection devices based on vision sensors on the sports industry and at proposing effective strategies for the development of sports wearable devices in the sports industry. This paper proposes an azimuth integration algorithm based on wearable sensor data. This goal establishes a new feature based on azimuth angle information for a reliable human behavior recognition system based on acceleration data. Based on summarizing and comparing the advantages and disadvantages of existing azimuth conversion algorithms, this paper develops an azimuth code conversion algorithm based on the combination of additional processing and Kalman processing to explore the impact of wearable devices on the sports industry. The experimental results of this article show that in the current sports industry, more than 19.74 million sports wearable testing devices have been put into use normally, which also means that the industry is about to enter a significant stage of development.

SINS attitude algorithm based on moving-window overdetermined polynomial fitting of gyro outputs

The attitude algorithm is the most important part of the whole strapdown inertial navigation (SINS) processing. It calculates the attitude of certain parameterization by integrating the gyro outputs or measurements in a specifically tailored way according to the attitude kinematic differential equation. The measurements or some angular velocity models obtained by fitting these measurements are often assumed free of errors in order to assess the numerical errors only. However, the gyro outputs and hence the models from them are by no means free of measurement errors. It is more often than not that the measurement errors dominate the numerical ones in practice. In this study, with coping with the measurement errors as the focus, we aim to improve the angular velocity model which is used as input in an attitude integration algorithm. This is achieved by exploiting the potential of overdetermined least-squares polynomial fitting. In order to avoid reducing the update rate by incorporating more measurements, the moving window trick is employed to re-use measurements in the previous update interval. The conventional attitude algorithm with second-order approximation in solving the differential equation of the equivalent rotation vector is employed as an example; however, the proposed method can be readily applied to other parameterizations such as direction cosine matrix, quaternion or Rodrigues parameters, and other high order approximations in solving the differential equation widely studied recently.

Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression

This paper proposes a new method for calculating the monomer reactivity ratios for binary copolymerization based on the terminal model. The original optimization method involves a numerical integration algorithm and an optimization algorithm based on k-nearest neighbour non-parametric regression. The calculation method has been tested on simulated and experimental data sets, at low (<10%), medium (10–35%) and high conversions (>40%), yielding reactivity ratios in a good agreement with the usual methods such as intersection, Fineman–Ross, reverse Fineman–Ross, Kelen–Tüdös, extended Kelen–Tüdös and the error in variable method. The experimental data sets used in this comparative analysis are copolymerization of 2-(N-phthalimido) ethyl acrylate with 1-vinyl-2-pyrolidone for low conversion, copolymerization of isoprene with glycidyl methacrylate for medium conversion and copolymerization of N-isopropylacrylamide with N,N-dimethylacrylamide for high conversion. Also, the possibility to estimate experimental errors from a single experimental data set formed by n experimental data is shown.

Design of Automatic Integration Algorithm for Popular Science Microanimation Works in the Context of New Media

When traditional methods integrate popular science microanimation works, the integration effect of the animation works is not good. In this paper, we propose an automatic integration algorithm of popular science microanimation works in the context of new media. The system first analyzes the characteristics of the new media context and gives the meaning of microanimation in the context of new media. It simplifies the edge folding of popular science microanimation integration and calculates the Facial Animation Parameter (FAP) value to realize the automatic integration of popular science microanimation works. We conducted a number of experiments using various size datasets to test the proposed system. We achieved an average integration accuracy of 96.3% with datasets of 500 to 3000 animation works, having the highest accuracy of 99% with a dataset of 500 animation works. On the other hand, the integration time of the animation works was recorded just 1.25 seconds with a dataset of 3000 animation works which is much lower than the existing work.