In fact, as a traditional encryption method, DES has been certified as an unsuitable tool for ciphering due to its smaller key space. Further, in concern of the real-time encryption in the current fast communication era, such as 5G, long-time as well as large computational level processes are not gotten into the consideration. As a result, an innovative encryption structure with hyperchaotic keys for efficient encryption is constructed, where the frame of DES structure is applied, the plain image is shuffled through row and column directions in the first round, and then rearranged to be 64 blocks to fit into the frame of DES structure for 4 rounds ciphering with hyperchaotic subkeys. Also, in order to encrypt the content of the image at the block level, a set of alternative S-box has been produced in this article as well. The simulation results indicate that the proposed scheme is feasible and reliable for digital image encrypting, not only a large key space can be obtained, but also the low correlation of the adjacent contents can be achieved, and further, in comparison of several existing approaches, less-computational resource can be proven as well. In particular, due to the innovative DES structure, the computational speed is significantly faster than the original DES algorithm and many other chaos-based image ciphering schemes.

In this paper, a set of smart fault-detection approach with chaotic mapping strategy is developed for an industrial ball-bearing system. There are four main statuses in this ball-bearing system: normal, inner race fault, outer race fault, and ball fault. However, it is hard to simply classify each of them through their vibration signals in time-series. By developing a nonlinear error dynamic system as well as a chaotic mapping strategy, the signals in the time series can be converted into the chaotic domain, which are revealed in 3D phase portraits. Further, through collocation of clustering methods, such as Euclidean distance (ED) and the kernel method of K-means (KM), the proposed 3D phase portraits of each different state can be efficiently identified through checking the autonomously adjusted ranges of feature values. The experiment results show that the proposed smart detection approach is effective and feasible, and the accuracy of detection in the testing stage is close to 100%.