A Novel Decomposition and Combination Technique for Forecasting Monthly Electricity Consumption

With the share of electricity in total final energy consumption increasing quickly, the world is becoming increasingly dependent on electricity, which makes it more and more important to improve the forecasting accuracy of electricity consumption to ensure the normal operation of economic activities. In this paper, a novel decomposition and combination technique to forecast monthly electricity consumption is proposed. First, we use STL decomposition to obtain the trend, season, and residual components of the time series. Second, we use SARIMA, SVR, ANN, and LSTM to forecast trend, season, and residual component, respectively. Third, we use time correlation principle to improve the forecasting accuracy of season component. Fourth, we integrated the residual component predicted by SARIMA, SVR, ANN, and LSTM into a new sequence to improve the forecasting accuracy of residual component. In order to verify the performance of the proposed forecast model, monthly electricity consumption data in China is introduced as an example for empirical analysis. The results show that after STL decomposition, time correlation modification, and residual modification, the forecasting accuracy of each model has been gradually improved. We believe that the proposed forecast model in this paper can also be used to solve other mid- and long-term forecasting problems with obvious seasonal characteristics.

Stochastic modelling of residual tropospheric delays / by Hassan Elobeid Ibrahim

Real-time and near real-time precise point positioning (PPP) requires shorter solution convergence time. Residual tropospheric delay, which exists as a result of the limitations of current tropospheric correction models, is a limiting factor for fast PPP convergence. To overcome the limitations of existing tropospheric models, we proposed a new approach. In this approach, the bulk of the tropospheric delay is accounted for using an empirical model, while the residual component is accounted for stochastically. The analysis of many daily tropospheric residuals data series for stations spanning North America shows that the residual component can be accounted for using an exponential cosine model. A random walk (RW) model was also developed and used along with the NOAA tropospheric corrections with Vienna Mapping Function 1. It is shown that the RW improved the accuracy of station coordinates within the PPP convergence time by a few centimetres.

Stochastic modelling of residual tropospheric delays / by Hassan Elobeid Ibrahim

Real-time and near real-time precise point positioning (PPP) requires shorter solution convergence time. Residual tropospheric delay, which exists as a result of the limitations of current tropospheric correction models, is a limiting factor for fast PPP convergence. To overcome the limitations of existing tropospheric models, we proposed a new approach. In this approach, the bulk of the tropospheric delay is accounted for using an empirical model, while the residual component is accounted for stochastically. The analysis of many daily tropospheric residuals data series for stations spanning North America shows that the residual component can be accounted for using an exponential cosine model. A random walk (RW) model was also developed and used along with the NOAA tropospheric corrections with Vienna Mapping Function 1. It is shown that the RW improved the accuracy of station coordinates within the PPP convergence time by a few centimetres.

Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data

Bridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data.

Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means

In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.

Regrowth of a Large Intracranial Aneurysm after On-Label Use of the Pipeline Embolization Device

ABSTRACTDespite the widespread use of the pipeline embolization device (PED), no complete aneurysm regrowth after its placement has been reported in the literature. We report the first case of aneurysm regrowth after the initial follow-up angiography demonstrating near-complete occlusion of the aneurysm and remodeling of the vessel with on-label PED use for a large 20 mm × 24 mm × 22 mm (width × depth × height) cavernous segment internal carotid artery (ICA) aneurysm. The patient was treated with two overlapping PED (4.5 mm × 20 mm and 5 mm × 20 mm). Follow-up angiogram at 4 months after treatment demonstrated remodeling of the ICA with a small residual component measuring approximately 7 mm × 8 mm × 7 mm. However, at 10 months after treatment, there was a complete regrowth of the aneurysm with interval growth, now measuring 25 mm × 28 mm × 18 mm. Despite the high aneurysm occlusion rates reported with the PED, persistent aneurysm filling and aneurysm regrowth, although rare, should not be overlooked.

Channel Model Optimization with Reflection Residual Component for Indoor MIMO-VLC System

A fast channel modeling method is studied to solve the problem of reflection channel gain for multiple input multiple output-visible light communications (MIMO-VLC) in the paper. For reducing the computational complexity when associating with the reflection times, no more than 3 reflections are taken into consideration in VLC. We think that higher order reflection link consists of corresponding many times line of sight link and firstly present reflection residual component to characterize higher reflection (more than 2 reflections). We perform computer simulation results for point-to-point channel impulse response, receiving optical power and receiving signal to noise ratio. Based on theoretical analysis and simulation results, the proposed method can effectively reduce the computational complexity of higher order reflection in channel modeling.