Abstract: In order to improve the accuracy of missing value filling of power load data and ensure the efficient follow-up data analysis and application, a filling model based on dynamic fusion attention mechanism is proposed. The model consists of an attention mechanism module and a dynamic weighted fusion module, and the deep association between features and timestamps is mined through two different attention mechanisms of the attention mechanism module. The learnable weights are assigned to the two outputs of the attention mechanism module by the dynamic weighted fusion module to get the feature representation. Finally, the feature representation is used to replace the values at the missing positions to obtain accurate filling results. The proposed model is validated using the meteorological and load dataset of a certain area of New York City and the UCI power load dataset, and the experimental results show that DFAIM has certain advantages over statistical, machine learning, and deep learning filling models in MAE, RMSE, and MRE.

Abstract: Aiming at the problem of long time and low success rate of automatic parking planning in restricted parking channels, an improved hybrid A * algorithm for path planning is proposed. Firstly, the parking path is divided into two parts: the forward pose adjustment section and the backward reverse parking section. Secondly, the collision risk cost is introduced into the hybrid A * algorithm, the node expansion method is improved, and the collision detection is carried out by judging whether the vehicle contour line intersects with the obstacle line, so as to improve the real-time and safety of the parking segment planning. Finally, the objective function is designed with the path length, smoothness and deviation as indexes, and the initial path is smoothed by quadratic programming to get the final path. The improved algorithm and the original algorithm are simulated by MATLAB. The results show that the improved algorithm can obtain a smooth and collision-free parking path under the constrained parking channel, and the search time is reduced by 23.8% compared with the hybrid A * algorithm, and the obtained path is safer and easier to track.

Abstract:

Aiming to meet the requirement of modulation recognition in high order and to solve the difficulty of  modulation recognition in low signal-to-noise ratio environment in 6G communication, a modulation recognition al gorithm based on multi-criteria fusion and intelligent decision was proposed by combining artificial intelligence tech nology and modern signal processing technology. The algorithm was divided into two parts. Multi-criteria fusion net work and intelligent decision network. The multi-criteria fusion network calculated the higher-order cumulative ex tensions of the standard modulation signals, traversed all the potential thresholds by using local optimal solutions,  and determined the judgment thresholds by Gini coefficient and the entropy of certainty gain. The intelligent deci sion network adopted a CART architecture to recognize the modulation format of unknown signals using the deter mined judgment thresholds, and the model was iterative optimized using a pruning algorithm to obtain the finally op timal decision tree, forming a modulation recognition algorithm based on multi-criteria fusion and intelligent deci sion making.  Experimental results showed that the algorithm could accurately recognize 16QAM, 64QAM,  128QAM, 1024QAM, 2PSK, 4PSK, 8PSK, 2FSK, 4FSK at 0 dB SNR, and the comprehensive recognition accu racy reached 99. 4%. Compared with other methods, the modulation recognition accuracy and the types of recogniz able modulation were improved.

Abstract:

Due to the fact that the physical constraints of traded goods in the electricity market and the carbon mar ket are different, and the trading time scale is quite different, it is difficult for the two markets to integrate effective ly. Aiming at the problem, a rolling clearing model of the electro-carbon joint market based on the variable carbon  emission intensity and the centralized carbon trading mechanism was proposed. In the proposed model, the interac tion between the electricity market and the carbon market was enhanced by considering the carbon intensity and  load rate interval of the unit. Meanwhile, the rolling clearance of the joint market based on the centralized carbon  trading mechanism reduced the trading time scale of the carbon market to synchronize with the electricity market,  making it′s better to found the value of carbon emission rights in different periods. With the further reduction of  China′s carbon emission baseline value and the increase of new energy penetration rate, the impact on each unit  was analyzed by simulation examples. It was verified that in the proposed model, with the reduction of the carbon  emission baseline value, the average carbon cost of high-carbon emission units increased by 46%, the average car bon income of low-carbon emission units increased by 27%, and the increase in the penetration rate of new energy  units reduced the average carbon cost of the large-capacity thermal power units by 5. 53%. Therefore, the proposed  model could effectively promote the transformation of the clean direction of the system. Compared with the tradition al stepped carbon pricing mechanism, the average carbon cost of high-carbon emission units in the proposed model  was reduced by 6. 13%, which could indirectly improve the enthusiasm of high-carbon emission units to participate  in the carbon market