Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause of the adversarial examples is unclear. Therefore, it is challenging to build a defense mechanism. In this paper, we propose an image-to-image translation model to defend against adversarial examples. The proposed model is based on a conditional generative adversarial network, which consists of a generator and a discriminator. The generator is used to eliminate adversarial perturbations in the input. The discriminator is used to distinguish generated data from original clean data to improve the training process. In other words, our approach can map the adversarial images to the clean images, which are then fed to the target deep learning model. The defense mechanism is independent of the target model, and the structure of the framework is universal. A series of experiments conducted on MNIST and CIFAR10 show that the proposed method can defend against multiple types of attacks while maintaining good performance.

Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and their assumption of a static environment limits their ability to adapt to changing conditions. Moreover, these algorithms suffer from low sample efficiency. Collectively, these factors contribute to convergence difficulties, low learning efficiency, and instability. To address these challenges, the Deep Deterministic Policy Gradient (DDPG) algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay (PER) method, aiming to improve exploration performance and learning efficiency from experience samples. Additionally, the corresponding Markov Decision Process (MDP) is established. Finally, an EMS based on the improved DRL model is presented. Comparative simulation experiments are conducted against rule-based, optimization-based, and DRL-based EMSs. The proposed strategy exhibits minimal deviation from the optimal solution obtained by the dynamic programming (DP) strategy that requires global information. In the typical driving scenarios based on World Light Vehicle Test Cycle (WLTC) and New European Driving Cycle (NEDC), the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption (EFC) of 2696.77 g. Compared to the DP strategy baseline, the proposed method improved the fuel efficiency variances (FEV) by 18.13%, 15.1%, and 8.37% over the Deep Q-Network (DQN), Double DRL (DDRL), and original DDPG methods, respectively. The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance, stability, and reliability, effectively optimizing vehicle economy and fuel consumption.