Humans can skillfully recognize actions from others' body motion and make a judgment or response at once. Previous neuroimaging studies have mostly utilized diminished and brief human motion stimuli and indicated that human occipito-temporal cortex plays a critical role at biological motion recognition. It remains unclear to what extent that the areas related to human motion perception are involved in decoding basic movements. Because human movement naturally stems from the sequences of body posture, so we utilized the stimulus of real movements. Participants were presented four categories of human movements (jump, run, skip and walk) in a blocked fMRI experiment. Multi-voxel pattern analysis (MVPA) was adopted to assess whether different movements could be discriminated in four regions. We found that movement-specific information was represented in both human body-sensitive areas, extrastriate body area (EBA) and motion-sensitive areas, posterior superior temporal sulcus (pSTS) and human middle temporal complex (hMT+). Additionally, a further functional connectivity analysis using EBA as a seed was conducted and it suggested that EBA showed a task-modulated functional connectivity with multiple areas that were involved in the behavior perception and motor control. Human motion processing appeared to be completed in a distributed network. The occipito-temporal cortex may perform the initial processing of human motion information extracting, and then transform them to interconnected areas for a further utilization.

Causal relations among variables may change significantly due to different control strategies and fault types. Off line-based knowledge is not adequate for fault diagnosis, and existing causal models obtained from data driven methods are mostly based on historical data only. However, variable correlation would not remain identical, and could be very different under certain industrial operation conditions. To deal with this problem, a fault diagnosis framework is proposed based on information solely extracted from process data. By this method, mutual information (MI) between each pair of variables is first calculated to obtain thresholds using historical data, as variable correlation under normal conditions is mostly contributed by random noises, which is often neglected in existing causal analysis models. Once a process deviation is detected, each pair of variables with mutual information beyond these thresholds are further investigated by time delayed mutual information (TDMI) analysis using current data, so as to determine the causal logic between them, which is represented as fault propagation paths, can be tracked all the way back to the root cause. The proposed method is first applied to a simulated process and the Tennessee Eastman process. The results show that the difference in variable correlation under diverse operation or control response conditions can be captured in real time, and fault propagation path can be objectively identified, together with the root cause. Then, the method has been successfully applied to a whole year data in an industrial process, which proves the feasibility of industrial application.

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