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A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.

To solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a novel model‐free adaptive control algorithm with data compensation under constraint conditions (COM‐cMFAC) is proposed in this manuscript. In the COM‐cMFAC algorithm, the pseudo partial derivative (PPD), which is a time‐varying parameter, is used to linearize the nonlinear dynamics of the multivehicle cooperative system by using dynamic linearization technology. Then, a COM‐cMFAC controller is designed. For the case of data packet loss, the proposed controller uses a data compensation mechanism that estimates and compensates for the lost data through the data collected at the previous moment to perform packet loss control. Additionally, the controller considers the constrained input and output problems that will occur in the actual control process and imposes input and output constraints. The main advantage of the COM‐cMFAC algorithm is that the entire control process only needs the input and output data of the multivehicle cooperative system, and it also has a good control effect in the case of packet loss. The stability of the proposed method is verified through strict mathematical analysis, and its effectiveness is verified by semiphysical experiments based on a MATLAB/Simulink and Carsim platform connection environment. [ABSTRACT FROM AUTHOR]

The effects of different sizes of gel pores on the elastic mechanical parameters of calcium silicate hydrate (C-S-H) were analyzed by molecular dynamics. Then, the mechanical and deformation properties under different stress states were simulated, and the characteristic parameters of the stress-strain relationships were compared and analyzed. The results show that the bulk modulus, shear modulus, and Young's modulus of C-S-H decrease with the increase of gel pore size. Gel pores will reduce the mechanical and deformation properties of C-S-H, but the effect varies under different stress states. Gel pores have the largest impact on tensile strength, and have almost the same effect on the compressive and shear strength. In terms of the gel pore effect on deformation properties, it is the largest under tension, followed by shearing, and finally compression. [ABSTRACT FROM AUTHOR]

In this article, an improved model‐free adaptive control method based on the controller dynamic linearization technique combined with the locally weighted regression‐based lazy learning method (cMFAC‐LL) is presented, and it is applied to solve the path‐tracking control problem for an NAO robot. In the proposed cMFAC‐LL method, two dynamic linearization techniques are first applied on the controlled plant, and then the cMFAC‐LL controller is further designed with the time‐varying parameters estimated using a novel locally weighted regression‐based lazy learning (LWR‐LL) technique. The greatest advantage of the cMFAC‐LL method is that it is a pure data‐driven control method, and the designed controller makes full use of both the online and offline measurement data of the plant. Moreover, the introduction of the local learning (LL) method gives the cMFAC‐LL method strong data learning ability and satisfactory control performance. The stability of cMFAC‐LL is proven via rigorous mathematical analysis. Furthermore, cMFAC‐LL is applied to the path‐tracking control of the NAO robot under different walking environments, by which the applicability of cMFAC‐LL is further verified. [ABSTRACT FROM AUTHOR]

Bus, as an important means of public transportation, has always received attention for its safety. When an emergency occurs, fall behaviour is one of representative features to help alarm. However, compared with other open and fixed scenes, the bus compartment has its own characteristics, such as crowded, closed, in moving, and so on. Considering the problems of people blocked as well as light changes, this paper proposes a new image‐based fall detection method in bus compartment scene, which can be divided into three parts: (1) Human detection, where object detection and pose estimation algorithms are combined, and then both global and local human features can be obtained; (2) fall discrimination, where both of the fall discrimination conditions and fall discrimination network are designed; (3) alert, where an alarm strategy is designed. Experiments are done in a real bus, and the Nvidia Jetson Xavier NX module is used to analyse the videos and images. Results finally show that the proposed fall detection method in bus compartment scenes can achieve 90% accuracy. [ABSTRACT FROM AUTHOR]