This article looks at the semantic space of abstract and concrete concepts from the perspective of distributed models of conceptual representations. It focuses on abstract metaphorical classes and the mechanisms through which these concepts are processed. When the metaphor X is a Y is understood, X is included in the abstract metaphorical class of Y. This metaphorical class is abstract because the most of semantic features of Y are filtered out through a suppressiveoriented mode of processing. It is suggested that abstract metaphorical classes of living things are usually defined by a single or a very small set of semantic features. Therefore, such metaphorical classes are highly abstract. On the other hand, abstract metaphorical classes of nonliving things are defined by a relatively larger cluster of semantic features. Therefore, abstract metaphorical classes of nonliving things have a relatively higher degree of concreteness compared to those of living things. In other words, abstract metaphorical classes of living things and nonliving things are rather different in terms of nature and the structure of semantic space.

Aiming at the problem of DC voltage control deviation and instability caused by a large-scale renewable energy access VSC–MTDC system, this paper combines voltage margin control and droop control. A strategy for controlling collaborative optimization in a sparsely distributed communication network has been proposed. Firstly, the distributed modeling of the system is carried out by combining MAS technology with small signal modeling. Then, a distributed model predictive controller is designed for a single droop control converter station. On this basis, a distributed cooperative optimization control strategy is proposed. According to the DC voltage deviation, the system adopts different control methods to control the receiving converter station. Finally, based on PSCAD/EMTDC and MATLAB co-simulation platforms, a six-terminal flexible HVDC system is built to verify the effectiveness of the control strategy under different conditions such as input power fluctuation, any converter station out of operation and system communication failure.

In order to reveal the non-uniform distribution of grain size in thick direction for engineering heavy plate, microstructure of 40 mm-thick Q345 steel was observed and measured under different short-term high temperature environments formed by fire. Moreover, the influence of the short-term high temperature environment was revealed on the distribution of ferrite grain size in the Q345 steel. Under different fire service environments, there was a log-normal distribution relationship between the distribution parameter N_f (number of ferrite grains) and d_f (average grain diameter), as well as ρ_Af (area fraction density) and d_f, at different positions along the thickness direction. However, the statistical results are greatly affected by the length of the statistical interval. When df is about 4 to 6 times the length of the statistical interval, the statistical accuracy is higher. By using nonlinear fitting method, multiple non-uniform distribution empirical models including N_f-d_f empirical formulas and ρ_Af-d_f empirical formulas were established at different positions along thick direction under various fire environments. Furthermore, the interrelationships between fire temperature T and N_f , T and ρ_Af , fire duration t and N_f , t and ρ_Af were revealed, respectively.