The increasing scale of network access in recent years requires an advanced analysis of future network data traffic(NDT) trends to anticipate various network scenarios. However, current prediction methods are limited in accuracy due to the overly complex components of network traffic on the time scale. This paper proposes a time series network traffic prediction(NTP) method based on the ARIMA-LSTM model. The method uses the ARIMA to extract the linear components of the web traffic data and then uses the LSTM to extract the nonlinear components further. It will achieve effective prediction of complex time series. The article validates the effectiveness of the method experimentally on two publicly available datasets. All experimental results show that the ARIMA-LSTM method has satisfactory prediction performance and outperforms several other state-of-the-art works. In sum, this NTP method points to a new orientation for NDT monitoring and analysis.
Thunderstorm detection based on the Atmospheric Electric Field (AEF) has evolved from time-domain models to space-domain models. It is especially important to evaluate and determine the particularly Weather Attribute (WA), which is directly related to the detection reliability and authenticity. In this paper, a strategy is proposed to integrate three currently competitive WA's evaluation methods. First, a conventional evaluation method based on AEF statistical indicators is selected. Subsequent evaluation approaches include competing AEF-based predicted value intervals, and AEF classification based on fuzzy c-means. Different AEF attributes contribute to a more accurate AEF classification to different degrees. The resulting dynamic weighting applied to these attributes improves the classification accuracy. Each evaluation method is applied to evaluate the WA of a particular AEF, to obtain the corresponding evaluation score. The integration in the proposed strategy takes the form of a score accumulation. Different cumulative score levels correspond to different final WA results. Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes. Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms, with a 100% accuracy of WA evaluation. This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation, which provides promising solutions for a more reliable and flexible thunderstorm detection.
The distribution of elliptical aggregates in concrete with wall effect is investigated by computer simulation.The simulation results show that,as the number of simulations increases,the aggregate distribution density at any point in concrete approaches to a fixed value for a given aggregate area fraction.After the validity of the proposed simulation method is verified with experimental results,the effects of the aggregate gradation,aspect ratio,minimum aggregate diameter,and maximum aggregate diameter on the boundary layer thickness are discussed.Based on the numerical results,it is found that the boundary layer thickness increases with an increase in aspect ratio,minimum aggregate diameter,and maximum aggregate diameter.It is also found that the aggregate gradation has a significant influence on the boundary layer thickness.
To get source azimuth from microphone observation angle of view in a complex real environment, this article, on the basis of the analysis of geometric positioning method, established a seven‐element microphone array model and proposed a sound source omnidirectional positioning calibration method based on microphone observation angle. By using a seven‐element array to invert the position and angle of a sound source, the relative time delay value of a pair of microphones on the vertical axis of the coordinate system is used to determine the elevation angle polarity and realize the omnidirectional sound source positioning. The array parameters, sound velocity, array size, horizontal deflection angle, elevation angle, and sound source are analyzed, and the error method is proposed. The sound source data was measured using the microphone array perspective, and a new Cartesian coordinate system was established based on the observation angle of view for omnidirectional positioning calibration of the sound source. The simulation results show that the position error of the method is about 0.01% and the angle error is about 0.005%, with high calibration accuracy. The actual measurement results show that this method can effectively calibrate the sound source azimuth, the error rate of the source coordinates is around 10%, the horizontal declination angle error is less than 5%, and the elevation angle error is less than 8%. Appropriately increasing the spacing of the array will have a better calibration effect in an actual complex experimental environment.
Abstract During the construction and operation period, an offshore wind farm is exposed to a variety of meteorological threats, which could lead to severe damages and massive financial loss. In this work, we analyse meteorological risks of offshore wind farms in the coastal areas of the south-east Jiangsu Province. Firstly, we identified the risk factors through the Delphi Method and analysis of historical data. The five major risks include typhoon, lightning, extreme temperatures, salt frog, and oceanic disasters caused directly by meteorological disasters. Then, using the Analytic Hierarchy Process (AHP), we established index module of each layer and calculated the risk value of each influence factor. After that, we established the judgement matrix and the weight of each factor. Based on that, we got the comprehensive risk value and considered it as a medium risk. According to the weighting module, we also came to the conclusion that typhoon contributes most to the meteorological disaster of offshore wind farms, with a weight of 0.3937.
The effects of the curing time,aggregate gradation,water/cement ratio,aggregate volume content and maximum aggregate diameter on the chloride diffusivity of early age concrete were studied by experiment.It was found that the chloride diffusivity of early age concrete decreased with the increase of the curing time,but increases by increasing the water/cement ratio.The chloride diffusivity of early age concrete decreases with the increase of the aggregate volume content and maximum aggregate diameter.It was also found that the aggregate gradation had a certain influence on the chloride diffusivity of early age concrete.
Additional file of Genome-wide identification and expression profiling of auxin response factor (ARF) gene family in maize
The current thunderstorm monitoring methods ignore the nonlinear and non-stationary characteristics of atmospheric electric field signals, which has a negative effect on the monitoring results. Based on complementary ensemble empirical mode decomposition with adaptive noise and Savitzky-Golay filtering (CEEMDAN-SG), a point charge localization method for the thunderstorm cloud is proposed. After CEEMDAN is used to decompose the electric field signal into a series of intrinsic mode function (IMF) components, the signal is reconstructed after SG filtering of those noise-dominant components. Then, the reconstructed signal is used for point charge localization correction. By changing the signal samples, the decomposition order and SNR of CEEMDAN-SG, CEEMDAN, etc. are compared, and the performance of the method is analyzed. Experiments show that compared to the SNR before reconstruction, the SNR after reconstruction is improved by about 3%. At the same time, the results can match the radar chart well on the time scale. After using the CNN-LSTM network model, it is found that compared with the original signal, the amplitude of the absolute error of the reconstructed signal is larger, so that the signal characteristics can be more displayed. This once again proves the localization effects.
This study proposed a quasi-order-based temporal data structure (QOTDS) which differed from conventional, algebraic data management models. Based on this QOTDS, a temporal data index called the temporal quasi-order index (TQOindex) was established. Firstly, the study proposed the concepts of temporal quasi-order (TQO) and linear order partitioning (LOP) of time period sets and discussed the construction algorithm of LOP and the optimum (minimum) properties. On this basis, a temporal data structure was established based on LOP. This structure realized the set-at-a-time data operation-like relational data structure and improved the inquiry efficiency by using multiple threads. Subsequently, in the structural framework of TQO, we discussed the temporal data index (TQOindex) based on quasi-order extensions. This index was effectively applicable to various conventional database platforms depending on the disk (external memory)-based data management and also to big data dynamic index technology relying on the incremental updating mechanism. Finally, a corresponding experimental simulation and comparative evaluation were designed to verify the feasibility and effectiveness of TQOindex. Research and experiments showed that QOTDS were effective at temporal inquiry and management in cases involving the temporal processing and integration mechanisms in new data, such as semantic data, XML data, and moving object data.
Weak signal detection has always been a hot spot in the signal processing field. In this paper, the chaotic and large data size characteristics of sea clutter are analyzed, the advantage of the Long and Short Term Memory network (LSTM) is taken to design a weak signal detection method based on deep learning. The reconstructed phase space signal is used as the input of LSTM network, the length of training data is determined by embedding dimension and delay time, and a chaotic prediction model is established to detect weak signals from the prediction error. In order to improve the detection performance, reduce the missing rate of deep learning method for small feature signal, frequency domain conversion of the prediction error is conducted, the spectrum of the prediction error of different distance gates is compared to locate the coordinates of the weak signal. The experimental results show that the sea clutter detection method based on LSTM prediction error frequency domain conversion has strong applicability and higher accuracy, and the detection performance is improved by 30%.
In order to solve the problem of different marine noise signals' classification, a multi-layer neuron networks model, which can be used to learn and analyze different marine noise signals, is built based on DNN (Deep Neural Networks) model in this article. Firstly, let's generate the initialized weight value randomly. Secondly, do linear operation that the input values of each layer multiply the weight values and then add the figures together. Thirdly, function value normalization is achieved by implementing nonlinear sigmoid active function, and we can get error function of actual output and desired output. Fourthly, we can get error coefficient of weight value and minimal value by gradient descent algorithm. In the last, we can get classification weight value which can distinguish different marine noise signals by summing this coefficient and weight value to keep weigh value updated. In the article, a four-layer deep neuron networks is built, of which three layers are hidden. Train the matrix data, test it and the result is that there are four errors among 100 test objects with 94%o accuracy. At the same time, the average accuracy of the 10 test results was 91.7%. It proves that this method can achieve the marine noise signals' classification.
Addressing the limitations of manually extracting features from small maritime target signals, this paper explores Markov transition fields and convolutional neural networks, proposing a detection method for small targets based on an improved Markov transition field. Initially, the raw data undergo a Fourier transform, feature fusion is performed on the series, and a spectrogram is generated using Markov transition fields to extract radar data features from both the time domain and frequency domain, providing a more comprehensive data representation for the detector. Then, the InceptionResnetV2 network is employed as a classifier, setting decision thresholds based on the softmax layer’s output, thus achieving controllable false alarms in the detection of small maritime targets. Additionally, transfer learning is introduced to address the issue of sample imbalance. The IPIX dataset is used for experimental verification. The experimental results show that the proposed detection method can deeply mine the differences between targets and the maritime clutter background, demonstrating superior detection performance. When the observation time is set to 1.024 s, the IMIRV2 detector performs best. Cross-validation with different data preprocessing methods and classification models reveals a significant advantage in the performance of the IMIRV2 detector, especially at low signal-to-noise ratios. Finally, a comparison with the performance of existing detectors indicates that the proposed method offers certain improvements.
This paper analyzes some problems existing in the experiment teaching of universities,compares every kind of solution scheme,and analyses the open teaching laboratory management strategy.The solution scheme for management mode of open laboratory is also presented.In order to cultivate the practicing ability and innovative thinking ability of students,to improve the personal ability and whole level for teachers,and to strengthen the efficiency of laboratory on external competitive ability,different strategies for students,teachers and laboratories are put forward respectively.
High precise pH value measurement meter is depicted.This pH value measurement meter is designed based on MSP430 MCU and pH electrode sensor,while the method of two point calibration and software temperature compensation is applied in the designed meter.This pH value measurement has the features of two point calibration,auto temperature compensation,historical data storage and real-time data transmission.The problem of low power,high precise and data transmission of the pH value measurement meter are solved.This meter has been applied in industry.
In this study, detection of small target in chaotic clutter with unknown dynamics is presented. We achieve this in four steps: (i) by using db3 wavelet decomposition of the signals, (ii) using Takens delay embedding theorem and least-squares support vector machine (LS-SVM) prediction, including increase the symmetric constraint and improve the kernel function, (iii) wavelet reconstruction, (iv) separation the weak signals from the prediction error. Efficiency of the new approach is evaluated by computing the root mean square error (RMSE) and signal-noise-radio (SNR) of the estimation. Lorenz attractor and the data from the McMaster IPIX radar sea clutter database will be used in the simulation. It is demonstrated in the simulation that compared with conventional RBF neural network LS-SVM regression prediction method; this approach has stronger generalization ability and better accuracy.
