“…This difference impacts the model so that the proposed method can predict the near-failure state more credibly. The proposed approach can be applied in different kinds of fields including e-commerce systems (Qi et al, 2020) and transportation (Qi et al, 2018).…”
“…This difference impacts the model so that the proposed method can predict the near-failure state more credibly. The proposed approach can be applied in different kinds of fields including e-commerce systems (Qi et al, 2020) and transportation (Qi et al, 2018).…”
“…Thus, our approach can capture any feature on other datasets without retraining the GAN model. The proposed approach will be used in many other fields such as e-commerce systems [31], transportation systems [32], and manufacturing [33], [34].…”
There are mainly three limitations of the traditional facial attribute editing techniques: 1) incapability of generating an arbitrary facial image with high-resolution; 2) being unable to generate and edit new facial images synthesized by the computer and 3) limited diversity of edited images. This paper presents a method for generating and editing images simultaneously. It incorporates a high-resolution facial image generator, a multi-label classifier, and a Generalized Linear Model (GLM). Experimental results show that our method can generate arbitrary high-resolution facial images, edit computer-synthesized images, perform multi-attribute editing, and effectively control the intensity and style of the generated images. Besides, the approach has high efficiency and flexibility, allowing rapid migration of attribute information from the data set. We design a graphical interface program, which can be integrated as a mobile application.
“…Reinforcement learning (RL) is an important branch of the artificial intelligence technology that has strong adaptability and self-learning ability in the complex environment. With the development of deep learning, the combination of the deep learning and reinforcement learning has become a research hotspot and has been successfully applied in many fields such as playing games [26], [27] and has potential in many traditional fields such as business process mining [28], transportation system [29], scheduling problems [32] and multiresource-constrained [30], [31]. The agent has the capacity to enhance its strategy to fulfill mission over time with reinforcement learning.…”
The artificial potential field approach is an efficient path planning method. However, to deal with the local-stable-point problem in complex environments, it needs to modify the potential field and increases the complexity of the algorithm. This study combines improved black-hole potential field and reinforcement learning to solve the problems which are scenarios of local-stable-points. The blackhole potential field is used as the environment in a reinforcement learning algorithm. Agents automatically adapt to the environment and learn how to utilize basic environmental information to find targets. Moreover, trained agents adopt variable environments with the curriculum learning method. Meanwhile, the visualization of the avoidance process demonstrates how agents avoid obstacles and reach the target. Our method is evaluated under static and dynamic experiments. The results show that agents automatically learn how to jump out of local stability points without prior knowledge.
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