Improved CS Algorithm and its Application in Parking Space Prediction

This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network(WNN)model,and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search(CS)algorithm.First,the initialization parameters are provided to optimize the WNN using the improved CS.The traditional CS algorithm adopts the strategy of overall update and evaluation,but does not consider its own information,so the convergence speed is very slow.The proposed algorithm employs the evaluation strategy of group update,which not only retains the advantage of fast convergence of the dimension-by-dimension update evaluation strategy,but also increases the mutual relationship between the nests and reduces the overall running time.Then,we use the WNN model to predict parking information.The proposed algorithm is compared with six different heuristic algorithms in five experiments.The experimental results show that the proposed algorithm is superior to other algorithms in terms of running time and accuracy.

Intelligent Space All-Optical Network Technology

Microwave transmission in a space network is greatly restricted due to precious radio spectrum resources. To meet the demand for large-bandwidth, global seamless coverage and on-demanding access, the Space All-Optical Network(SAON) becomes a promising paradigm. In this paper, the related space optical communications and network programs around the world are first briefly introduced. Then the intelligent Space All-Optical Network(i-SAON), which can be deemed as an advanced SAON, is illustrated, with the emphasis on its features of high survivability, sensing and reconfiguration intelligence, and large capacity for all optical load and switching. Moreover, some key technologies for i-SAON are described, including the rapid adjustment and control of the laser beam direction, the deep learning-based multi-path anti-fault routing, the intelligent multi-fault diagnosis and switching selection mechanism, and the artificial intelligence-based spectrum sensing and situational forecasting.

Assessment of Model Predictive Control Performance Criteria

The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.

DECOMPOSITION OF A CLASS OF FUNCTIONALS AND THE PREDICTABLE REPRESENTATION THEOREM ON BANACH SPACES

Let B be a separable real Banach space and X(t) be a symmetric conservative diffusionprocess taking values in B. In this paper, we decompose the functional u(X(t),t) into a sumof a square integrable martingale and a regular 0-quadratic variation process. On this basis, weestablish the predictable representation theorem of X(t).

Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D

The charging load of electric vehicles(EVs)has a strong spatiotemporal randomness.Predicting the dynamic spatiotemporal distribution of the charging load of EVs is of great significance for the grid to cope with the access of large-scale EVs.Existing studies lack a prediction model that can accurately describe the dual dynamic changes of EVs charging the load time and space.Therefore,a spatial-temporal dynamic load forecasting model,dilated causal convolution-2D neural network(DCC-2D),is proposed.First,a hole factor is added to the time dimension of the three-dimensional convolutional convolution kernel to form a two-dimensional hole convolution layer so that the model can learn the spatial dimension information.The entire network is then formed by stacking the layers,ensuring that the network can accept long-term historical input,enabling the model to learn time dimension information.The model is simulated with the actual data of the charging pile load in a certain area and compared with the ConvLSTM model.The results prove the validity of the proposed prediction model.