Applying the compact constraint calculation method of S-box based on Mixed Integer Linear Programming (MILP) model can solve the low efficiency of differential path search of SPONGENT in differential cryptanalysis. To find the best description of S box, a compactness verification algorithm was proposed to verify the inequality constraints in S-box from the perspective of the necessity of the existence of constraints. Firstly, the MILP model was introduced to analyze the inequality constraints of SPONGENT S-box, and the constraint composed of 23 inequalities was obtained. Then, an index for evaluating the existence necessity of constraint inequality was proposed, and a compactness verification algorithm for verifying the compactness of group of constraint inequalities was proposed based on this index. Finally, the compactness of the obtained SPONGENT S-box constraint was verified by using the proposed algorithm. Calculation analysis show that the 23 inequalities have a unique impossible difference mode that can be excluded, that is, each inequality has the necessity of existence. Furthermore, for the same case, the number of inequalities was reduced by 20% compared to that screened by using the greedy algorithm principle. Therefore, the obtained inequality constraint of S-box in SPONGENT is compact, and the proposed compactness verification algorithm outperforms the greedy algorithm.
To address the degradation problem of traditional depth estimation models caused by image quality degradation in haze environment, a model based on Conditional Generative Adversarial Network (CGAN) was proposed to estimate the depth of single haze image by fusing dual attention mechanism. Firstly, for the network structure of the generator of the model, the DenseUnet structure fused with dual attention mechanism was proposed. The dense blocks were used as basic blocks in the encoding and decoding processes of U-net. Dense and jump connections were used to enhance information flow, as well as extract the underlying structural features and high-level depth information of the direct transmission rate map. Then, the global dependencies of spatial features and channel features were adaptively adjusted by the dual attention module. At the same time, a new structure-preserving loss function was proposed by combining the least absolute value function, perceptual loss, gradient loss, and adversarial loss. Finally, using the direct transmission rate map of the haze image as a condition of CGAN, the depth map of the haze image was estimated through the adversarial learning of the generator and the discriminator. Training and testing were performed on the indoor dataset NYU Depth v2 and the outdoor dataset DIODE. Experimental results show that the proposed model has a finer geometric structure and richer local details. Compared with the fully convolutional residual network, on NYU Depth v2, the proposed model has the Logarithmic Mean Error (LME) and Root Mean Square Error (RMSE) error reduced by 7% and 10%, respectively. Compared with the deep ordinal regression network, on DIODE, the proposed model has the accuracy with threshold less than 1.25 increased by 7.6%. It can be seen that the proposed model improves the estimation accuracy and generalization ability of depth estimation under the interference of haze.
Aiming at the lack of consideration of the psychological behaviors of decision makers in software quality evaluation methods, a TOmada de Decisao Interativa e Multicritevio (TODIM) software quality evaluation method based on interval 2-tuple linguistic information was proposed. Firstly, interval 2-tuple linguistic information was used to characterize the evaluation information of experts for software quality. Secondly, the subjective and objective weights of software quality attributes were calculated by subjective weighting method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) respectively. On this basis, the comprehensive weights of software quality attributes were obtained by combined weighting method. Thirdly, in order to better describe the psychological behaviors of experts in the process of software quality evaluation, TODIM was introduced into software quality evaluation. Finally, the method was used to evaluate the software quality of assistant dispatcher terminal in high-speed railway dispatching system. The result shows that the third assistant dispatcher terminal software provided by the railway software supplier has the highest dominance value and its quality is the best. The results of comparing this method with the regret theory and Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE-II) show that the three methods are consistent in the selection of the best quality software, but the overall rankings of the three methods are somewhat different, indicating that the constructed method has strong superiority in describing the interaction between multiple criteria and the psychological behaviors of decision makers.
Aiming at the problems of current aviation card readers, include poor portability, slow speed and tags' little capacity, a design method of large capacity Radio Frequency Identification (RFID) system based on STM32 was proposed. Using STM32 microprocessor as a core and adopting CR95HF radio chip, a new handled RFID card reader which worked in High Frequency (HF) and supported ISO 15693, ISO 18092 protocols was designed. The design of power, antenna and optimization of software speed, error rate was discussed in detail. A new large compiled capacity passive tag was also designed whose capacity is up to 32KB to form a large capacity RFID system with card reader. The experimental results show that, compared with the traditional card reader, the reading and writing speed of the card reader increases by 2.2 times, error rate reduces by 91.7% and tag capacity increases 255 times. It provides a better choice for fast, accurate and high data requirements of aviation logistics.