With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi-sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance. INDEX TERMS Mental health assessment, wearable device, attention-based feature fusion.
This paper proposes a hybrid multi-dimensional features fusion structure of spatial and temporal segmentation model for automated thermography defects detection. In addition, the newly designed attention block encourages local interaction among the neighboring pixels to recalibrate the feature maps adaptively. A Sequence-PCA layer is embedded in the network to provide enhanced semantic information. The final model results in a lightweight structure with smaller number of parameters and yet yields uncompromising performance after model compression. The proposed model allows better capture of the semantic information to improve the detection rate in an end-to-end procedure. Compared with current state-of-the-art deep semantic segmentation algorithms, the proposed model presents more accurate and robust results. In addition, the proposed attention module has led to improved performance on two classification tasks compared with other prevalent attention blocks. In order to verify the effectiveness and robustness of the proposed model, experimental studies have been carried out for defects detection on four different datasets.
Due to its sedimentary characteristics and natural fractures, oil shale shows anisotropy in heat transfer characteristics. Moreover, the anisotropic thermal conductivity will change with the temperature. This change in the anisotropic thermal conductivity coefficient affects the temperature field distribution and heating efficiency during the in situ electric heating pyrolysis of oil shale. Therefore, it is very important to study the evolution of the anisotropy thermal conductivity coefficient of oil shale with temperature. In this study, the variation of weight loss and the specific heat of an oil shale with temperature is investigated using a differential scanning calorimeter. The variation of the anisotropic pore and fracture structure of the oil shale with temperature is studied through CT scanning technology. The variation of the anisotropic thermal conductivity with temperature is studied through the hot disk method. Finally, the relationship between the change in the anisotropic heat conductivity of the oil shale and the evolution of the anisotropic pore and fracture structure is discussed. The results show that the mass loss of oil shale mainly occurs after 400 °C. The thermal conductivity of both perpendicular and parallel to bedding directions decreases linearly with the increase of temperature. The research results of this study can serve as an important reference in the study of the in situ pyrolysis of oil shale.
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