Dynamic Electrode-to-Image (DETI) Mapping Reveals the Human Brain’s Spatiotemporal Code of Visual Information

A chief goal of systems neuroscience is to understand how the brain encodes information in our visual environments. Understanding that neural code is crucial to explaining how visual content is transformed via subsequent semantic representations to enable intelligent behavior. Although the visual code is not static, this reality is often obscured in voxel-wise encoding models of BOLD signals due to fMRI’s poor temporal resolution. We leveraged the high temporal resolution of EEG to develop an encoding technique based in state-space theory. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. This technique offers a spatiotemporal visualization of the evolution of the neural code of visual information thought impossible to obtain from EEG and promises to provide insight into how visual meaning is developed through dynamic feedforward and recurrent processes.

Dynamic prediction and compensation of aerocraft assembly variation based on state space model

Purpose – This paper aims to clarify the predicting and compensating method of aeroplane assembly. It proposes modeling the process of assembly. The paper aims to solve the precision assembly of aeroplane, which includes predicting the assembly variation and compensating the assembly errors. Design/methodology/approach – The paper opted for an exploratory study using the state space theory and small displacement torsor theory. The assembly variation propagation model is established. The experiment data are obtained by a real small aeroplane assembly process. Findings – The paper provides the predicting and compensating method for aeroplane assembly accuracy. Originality/value – This paper fulfils an identified need to study how the assembly variation propagates in the assembly process.

The Approach for Analyzing Motion Mode of High-Order Control Systems

This paper aims at exploring the modal analysis approach of a motion control system. Based on the inverse Laplace transformation, the step response of a control system is derived. Then this response is associated with the modal analyses in state space theory. And then the motion mode of a control system is analyzed with the modal analysis method. Application example indicates that this approach can be used to analyze the high-order control system successfully. This facilitates the motion mode analyses of high-order control system very much.

A State Recognition Method Based on the State Space Theory

With the development of the monitoring technology in industrial process, it is increasingly important for state recognition of the mechanical devices. By learning from the immune identification process, state space theory is used for state recognition in this paper. In this method, some state spaces are compared by the space affinity which is regarded as the calculated difference between spaces. Take the minimum space affinity as the final recognition index and then its corresponding remark in remark set can be selected. Through the sample analysis, state space theory is proved to be simple, effective and practical, and can improve the accuracy of recognition according to detailed state standard.

The Research of Product Concept Feature Modeling Based on Energy Saving

Product conceptual feature modeling based on energy saving is established. Through having decomposed the general functions, product design information of every function unit is extracted and converted into energy eigenvector. The combination of every function unit becomes functional chain and the function model is got. Using the bond graph and state space theory, principal analysis and behavior modeling is developed. Structure modeling is established based on function modeling and behavior modeling, comprehensively considered energy consumption influencing factors in structure modeling. Function-behavior-Structure modeling is put forward and product conceptual feature modeling based on energy saving is realized.