Nowadays, information security becomes increasing important, and cryptography is an indispensable part of information security. Typically, the encryption algorithms are implemented through performing various operations on plaintext with a secret key to achieve information hiding. Based on the existing Tangent-Delay Ellipse Reflecting Cavity-map System (TD-ERCS) chaotic system and the Traditional Hill Cipher (THC) with a time-invariant key matrix, a Time-Varying Hill Cipher (TVHC) with a time-variant key matrix is proposed in this work. As an effective method in solving time-varying problems, the Zeroing Neural Network (ZNN) is used to effective find the Time-Variant Inversion Key Matrix (TVIKM) for the TVHC decryption process. Moreover, a Novel Fix-time Convergence Fuzzy ZNN (NFCF-ZNN) with superior convergence and robustness is constructed for quickly solving the TVIKM of the TVHC decryption process. The convergence and robustness of NFCF-ZNN for solving TVKIM in the absence and presence of noise are both demonstrated through rigorous mathematical derivation and comparative simulation experiments. Additionally, the successful simulation experiments of the proposed TVHC in grayscale and RGB color images encryption and decryption further validates its effectiveness in practical applications.

With the advancement of intelligence in Active Distribution Networks (ADNs), effective fault recovery methods have become increasingly crucial. In this study, a reconfiguration method combining the immune mechanism and Northern Goshawk Optimization algorithm (NGO) is proposed, aimed at swiftly restoring power post-fault, maximizing the recovery of lost power areas within ADN, and minimizing losses. Firstly, an identification model within the immune mechanism is crafted to precisely match failures in ADNs. Then, the successful matched failure types can be used to restore power supply by the direct invocation of the recovery strategy from the library of strategies. Secondly, the immune response of ADNs is modeled, known as the reconfiguration model. For faults beyond the recovery strategy library, NGO is leveraged to address distribution network failures, with restoration solutions integrated into the library. Additionally, a reversed learning approach and stochastic variation strategy enhance the robustness of algorithm, preventing it from converging to suboptimal solutions. Finally, through simulation experiments, it is demonstrated that the recovery scheme obtained using the algorithm can be used to recover failure as well as reduce network losses in an effective manner. When similar or identical faults recur, ADN failure recovery becomes swift and efficient.