Thermodynamic model of wet compression process is established in this paper. The topics of ideal wet compression process, actual wet compression process, water droplet evaporative rate, wet compression work, inlet evaporative cooling, concept of wet compression efficiency, aerodynamic breaking of water droplets etc. are investigated and discussed in this paper.

Object transportation could be a challenging problem for a single robot due to the oversize and/or overweight issues. A multi-robot system can take the advantage of increased driving power and more flexible configuration to solve such a problem. However, an increased number of individuals also changed the dynamics of the system which makes control of a multi-robot system more complicated. Even worse, if the whole system is sitting on a centralized decision making unit, the data flow could be easily overloaded due to the upscaling of the system. In this research, we propose a decentralized control scheme on a multi-robot system with each individual equipped with a deep Q-network (DQN) controller to perform an oversized object transportation task. DQN is a deep reinforcement learning algorithm, thus does not require the knowledge of system dynamics, instead, it enables the robots to learn appropriate control strategies through trial-and-error style interactions within the task environment. Since analogous controllers are distributed on the individuals, the computational bottleneck is avoided systematically. We demonstrate such a system in a scenario of carrying an oversized rod through a doorway by a two-robot team. The presented multi-robot system learns abstract features of the task and cooperative behaviors are observed. The decentralized DQN-style controller is showing strong robustness against uncertainties. In addition, We propose a universal metric to assess the cooperation quantitatively.

When the data of target domain are scarce, the established model will not be accurate enough to analyze the target problem. For the abnormal condition identification modeling problem of electro-fused magnesia smelting process, this paper proposes the new Bayesian network (BN) parameters transfer learning method based on the expert knowledge from target domain to increase the accuracy of abnormal condition identification. First of all, the electro-fused magnesia smelting process is introduced and the existing research results on the abnormal condition identification are analyzed. The problem to solve in this paper is described. Furthermore, the constraints from expert knowledge for the target model are shown in two forms. The new BN parameters transfer learning method is proposed. Finally, the proposed method is verified by the Asia network, and it is applied to establish the abnormal exhausting condition identification model for the electrofused magnesia smelting process. The simulation results demonstrate the effectiveness of proposed method which owns the better performances.INDEX TERMS Bayesian network, transfer learning, fused magnesium furnace, abnormal conditions identification, expert knowledge.

Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use mobile robots equipped with visual sensors to perform automated inspections. For such a robotic inspection, a digital model of the aircraft is usually required for planning the robot’s path, but a CAD model of the entire aircraft is usually inaccessible to most maintenance shops. It is very labor-intensive and time-consuming to generate an accurate digital model of an aircraft, or even a large portion of it, because the scanning work still must be performed manually or by a manually controlled robotic system. This paper presents a two-stage approach of automating aircraft scanning with an unmanned aerial vehicle (UAV) or autonomous drone equipped with a red–green–blue and depth (RGB-D) camera for detailed inspection or for reconstructing a digital replica of the aircraft when its original CAD model is unavailable. In the first stage, the UAV–camera system follows a predefined path far from the aircraft surface (for safety) to quickly scan the aircraft and generate a coarse model of the aircraft. Then, an optimal scanning path (much closer to the surface) in the sense of the shortest flying distance for full coverage is computed based on the coarse model. In the second stage, the UAV–camera system follows the computed path to closely inspect the surface for possible defects or scan the surface for generating a dense and precise model of the aircraft. We solved the coverage path planning (CPP) problem for the aircraft inspection or scanning using a Monte Carlo tree search (MCTS) algorithm. We also implemented the max–min ant system (MMAS) strategy to demonstrate the effectiveness of our approach. We carried out a digital experiment and the results showed that our approach can scan 70% of the aircraft surface within one hour, which is much more efficient than manual scanning.

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