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Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.

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A slag flow submodel has been developed to simulate the slag flow and phase transformation behaviors in coal gasifiers. The volume of the fluid (VOF) model is used to capture the free surface of the slag flow, and the continuum surface force (CSF) model is employed to calculate the surface tension between the gas phase and the liquid slag phase. The slag is treated as a Newtonian fluid when the slag temperature is above the critical viscosity temperature (T cv), and plastic fluid is treated when the slag temperature is between the flow temperature (T f) and the T cv. The ash particle deposition, viscosity−temperature dependence, and different thermal conductivity for different slag phase are all included in the present simulation. For membrane wall coal gasification, the liquid slag and solid slag layer increases along the flow and total slag thickness increases as the operating temperature decreases. The velocity profiles and viscosity profiles at different operating temperatures are performed. The liquid slag flow will produce fluctuations when the slag temperature decreases to the lowest at the bottom of the gasifier. In addition, the temperature difference (T o − T f) between 150 and 200 °C is suitable for a membrane wall coal entrained-flow gasifier. For refractory wall coal gasification, the thicker refractory bricks can effectively prevent the heat lost from the gasifier wall, so the slag flow is steady when the operating temperature is higher than the critical operating temperature. An expression of solid slag layer formation criterion has been deduced from heat-transfer balance. The critical operating temperature of the different slag mass flow rate is studied by heat-transfer balance. In addition, the solid slag layer will rapidly increase as the operating temperature decreases to critical operating temperature.

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

A comprehensive model has been developed to analyze the multiphase flow and heat transfer in the radiant syngas cooler (RSC) of an industrial-scale entrained-flow coal gasification. The three-dimensional multiphase flow field and temperature field were reconstructed. The realizable k−ϵ turbulence model is applied to calculate the gas flow field, while the discrete random walk model is applied to trace the particles, and the interaction between the gas and the particle is considered using a two-way coupling model. The radiative properties of syngas mixture are calculated by weighted-sum-of-gray-gases model (WSGGM). The Ranz−Marshall correlation for the Nusselt number is used to account for convection heat transfer between the gas phase and the particles. The discrete ordinate model is applied to model the radiative heat transfer, and the effect of ash/slag particles on radiative heat transfer is considered. The model was successfully validated by comparison with the industrial plant measurement data, which demonstrated the ability of the model to optimize the design. The results show that a torch shape inlet jet was formed in the RSC, and its length increased with the diameter of the central channel. The recirculation zones appeared around the inlet jet, top, and bottom of the RSC. The overall temperature decreased with the heat-transfer surface area of the fins. The concentration distribution, velocity distribution, residence time distribution, and temperature distribution of particles with different diameters have been discussed. Finally, the slag/ash particles size distribution and temperature profile at the bottom of the RSC have been presented.