Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

Prediction of Surface Roughness Based on Least Square Support Vector Machine in Low-Frequency Vibration Cutting Process

2014 ◽  
Vol 620 ◽  
pp. 592-597
Author(s):  
Wei Wen Du ◽  
Li Zhi Gu ◽  
Jiao Tao Wang
Keyword(s):  
Surface Roughness ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Low Frequency ◽  
Training Sample ◽  
Least Square ◽  
Support Vector ◽  
Vibration Cutting ◽  
Low Frequency Vibration ◽  
Sample Set

A prediction method based on least square support vector machine is introduced into the surface roughness prediction model in low-frequency vibration cutting. The model is created with low-frequency vibration cutting experiment for the corresponding relationship between vibration parameters and cutting parameters and the workpiece surface roughness. The training sample set is constructed to train regression models of least square support vector machine through experimental data. Identification of training sample set is done to gain the regression parametersaandb. The amplitude ofA, vibration frequencyf, feedf1and spindle speednare used as the input variable in Xi. Predicted values of surface roughness are forecasted with the model. Evaluation is made with the difference between the predicted value and experiment. Comparison with BP neural network and support vector machine method has shown that the least square support vector machine prediction model works faster than SVM method, the prediction error is about 29% of that by support vector machine, and the prediction accuracy is higher than the BP model.

scholarly journals Impaired vagal activity to meal in patients with functional dyspepsia and delayed gastric emptying

2017 ◽  
Vol 46 (2) ◽  
pp. 792-801 ◽  
Author(s):  
W-J Guo ◽  
S-K Yao ◽  
Y-L Zhang ◽  
S-Y Du ◽  
H-F Wang ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Gastric Emptying ◽  
Functional Dyspepsia ◽  
Delayed Gastric Emptying ◽  
Low Frequency ◽  
Treatment Method ◽  
Frequency Component ◽  
High Frequency Component ◽  
Vagal Activity ◽  
Nervous Function

Objective This study was performed to investigate impaired vagal activity to meal in patients with functional dyspepsia (FD) with delayed gastric emptying (GE). Methods Eighty-five patients were studied. GE parameters, including those in the overall and proximal stomach, were measured by GE functional tests at the Department of Nuclear Medicine. Autonomic nervous function was tested by spectral analysis of heart rate variability (HRV). The vagal activity and sympathetic activity were analyzed by recording the power in the high-frequency component (HF), low-frequency component (LF), and LF/HF ratio. Results Overall and proximal GE were delayed in 47.2% and 50.9% of the patients, respectively. Spectral analysis of HRV showed that the HF in patients with delayed proximal GE was significantly lower and that the LF/HF ratio was significantly higher than those in patients with normal proximal GE after a meal. Conclusion Delayed proximal GE might be caused by disrupted sympathovagal balance as a result of decreased vagal activity after a meal. Improvement in vagal activity may constitute an effective treatment method for patients with FD.

scholarly journals STORM INDUCED PERIODICITIES OF SUSPENDED SAND MOVEMENT

1978 ◽  
Vol 1 (16) ◽  
pp. 105 ◽  
Author(s):  
Jay E. Leonard ◽  
Benno M. Brenninkmeyer
Keyword(s):  
Surf Zone ◽  
Normal Wave ◽  
Low Frequency ◽  
Sediment Concentration ◽  
Frequency Component ◽  
High Frequency Component ◽  
Computational Technique ◽  
Electromagnetic Current ◽  
Discrete Fourier Analysis ◽  
Sediment Movement

An array of electronic sensors was installed on Nauset Light Beach, Cape Cod, Massachusetts, U.S.A., in order to provide a description of the sediment movement during storm conditions. These sensors included two sediment concentration indicators (almometers) which monitor sediment movement as a function of elevation and time, one bidirectional electromagnetic current meter, and a resistive wave staff. Prior field studies performed during "normal" conditions have indicated that surf-zone suspended sediment movement is a low-frequency phenomenon, with the relatively high-frequency component (normal wave period) contributing little to the amount of total sediment transported. Development of a computational technique based upon discrete Fourier analysis and digital filtering called Spectrally Filtered Integration (SFI) provides the calculation and filtering of true units of sediment change in grams-per-liter. Moreover, the SFI technique eliminates the possibility spurious sediment information created by the presence of air bubbles in the water column. Generally, higher-frequency sediment movement is more common during storm conditions than during normal non-storm conditions. This movement is controlled not by the prevailing wave and swell periods, but by a longer period which may be due to water interactions below the surface.

Research on Fault Prediction Technology of Complicated Equipment

2013 ◽  
Vol 347-350 ◽  
pp. 448-452 ◽  
Author(s):  
Sai Sai Jin ◽  
Kao Li Huang ◽  
Guang Yao Lian ◽  
Bao Chen Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector ◽  
Hydraulic Pump ◽  
Fault Information

For the problems of not enough fault information for the complicated equipment and difficult to predict the fault, we apply Support Vector Machine (SVM) to build the fault prediction model. On the basis of analyzing regression algorithm of SVM, we use Least Square Support Vector Machine (LS-SVM) to build the fault prediction model.LS-SVM can effectively debase the complication of the model. Finally, we take the fault data of a hydraulic pump to validate this model. By selecting appropriate parameters, this model can make better prediction for the fault data, and it has higher prediction precision. It is proved that the fault prediction model which based on LS-SVM can make better prediction for fault trend of complicated equipment.

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

2021 ◽  
pp. 0309524X2110568
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Power Grid ◽  
Wavelet Denoising ◽  
Slime Mold ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

A Data-Driven Two-Stage Prediction Model for Train Primary-Delay Recovery Time

2020 ◽  
Vol 30 (07) ◽  
pp. 921-940
Author(s):  
Bowen Gao ◽  
Dongxiu Ou ◽  
Decun Dong ◽  
Yusen Wu
Keyword(s):  
Prediction Model ◽  
Knowledge Base ◽  
Recovery Time ◽  
Prediction Accuracy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Model Framework ◽  
Two Stage ◽  
Delay Propagation ◽  
Proposed Model

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on the prediction accuracy of buffer time in each section or station. It is found that explicitly inputting the attribute features of stations and sections to the model, instead of implicit simulation, will improve the prediction accuracy effectively. For validation purpose, the proposed model has been compared with several alternative models, namely, Logistic Regression (LR), Artificial Neutral Network (ANN), Support Vector Machine (SVM) and Gradient Boosting Tree (GBT). The results show that its remarkable performance is better than other schemes. Specifically, when the error is extended to 3[Formula: see text]min, the proposed model can achieve up to the accuracy of 94.63%. It proves that our method has high value in practical engineering application. Considering the delay propagation of trains is a complex process, our future study will focus on building delay propagation knowledge base and dispatcher experience knowledge base.

scholarly journals Extreme prolongation of expiration breathing: Effects on electroencephalogram and autonomic nervous function

2018 ◽  
Vol 10 (2) ◽  
pp. 62-65
Author(s):  
Teruhisa Komori
Keyword(s):  
High Frequency ◽  
Frequency Ratio ◽  
Low Frequency ◽  
Frequency Component ◽  
Small Sample ◽  
High Frequency Component ◽  
Deep Breathing ◽  
Continuous Eeg ◽  
Nervous Function ◽  
Electrocardiogram Ecg

To clarify the physiological and psychological effects of deep breathing, the effects of extreme prolongation of expiration breathing (Okinaga) were investigated using electroencephalogram (EEG) and electrocardiogram (ECG). Participants were five male Okinaga practitioners in their 50s and 60s. Participants performed Okinaga for 31 minutes while continuous EEG and ECG measurements were taken. After 16 minutes of Okinaga, and until the end of the session, the percentages of theta and alpha 2 waves were significantly higher than at baseline. After 20 minutes, and until the end of the session, the percentage of beta waves was significantly lower than at baseline. The high frequency component of heart rate variability was significantly lower after 12 minutes of Okinaga and lasted until 23 minutes. The low frequency/high frequency ratio was significantly lower after 18 minutes of Okinaga and until the end of the session. Okinaga produced relaxation, suggesting that deep breathing may relieve anxiety. However, study limitations include potential ambiguity in the interpretation of the low frequency/high frequency ratio, the small sample, and the fact that EEG was measured only on the forehead.

scholarly journals Extreme prolongation of expiration breathing: Effects on electroencephalogram and autonomic nervous function

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Teruhisa Komori
Keyword(s):  
High Frequency ◽  
Frequency Ratio ◽  
Low Frequency ◽  
Frequency Component ◽  
Small Sample ◽  
High Frequency Component ◽  
Deep Breathing ◽  
Continuous Eeg ◽  
Nervous Function ◽  
Electrocardiogram Ecg

To clarify the physiological and psychological effects of deep breathing, the effects of extreme prolongation of expiration breathing (Okinaga) were investigated using electroencephalogram (EEG) and electrocardiogram (ECG). Participants were five male Okinaga practitioners in their 50s and 60s. Participants performed Okinaga for 31 minutes while continuous EEG and ECG measurements were taken. After 16 minutes of Okinaga, and until the end of the session, the percentages of theta and alpha 2 waves were significantly higher than at baseline. After 20 minutes, and until the end of the session, the percentage of beta waves was significantly lower than at baseline. The high frequency component of heart rate variability was significantly lower after 12 minutes of Okinaga and lasted until 23 minutes. The low frequency/high frequency ratio was significantly lower after 18 minutes of Okinaga and until the end of the session. Okinaga produced relaxation, suggesting that deep breathing may relieve anxiety. However, study limitations include potential ambiguity in the interpretation of the low frequency/high frequency ratio, the small sample, and the fact that EEG was measured only on the forehead.

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