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.
Source‐to‐sink system studies in oil fields are especially important for optimizing the favourable oil‐gas areas; however, this basin‐mountain interaction process is particularly complicated in intermontane basins. In this study, detrital zircon U–Pb dating of 8 new and 3 published samples from throughout the Jurassic in the central Turpan Basin (TB) are combined with collected heavy mineral data to investigate the evolution of the source‐to‐sink system and reconstruct the filling process of this intermontane basin from the Lower to Upper Jurassic. Based on the detrital zircon ages and heavy mineral assemblage, the filling rocks mainly weathered from Mesozoic, Palaeozoic and Precambrian volcanic and metamorphic rocks in the Tian Shan (TS) area, and the source‐to‐sink systems in the basin show a clear alteration corresponding to the uplift of the surrounding mountains during the Jurassic. In the Lower Jurassic, the uplifted central and northern TS was the main provenance and shed the most source rocks northward, and the gentle positive relief of the Bogda Mountains (BM), the northern boundary of the TB, was a secondary provenance and provided limited material to the northern area. In the early Middle Jurassic, these provenances did not alter much, but in the later Middle Jurassic, uplift of the western BM began and provided some extreme new source rocks with detrital zircon ages of approximately 159–172 Ma. In the Upper Jurassic, the considerably uplifted BM became the main provenance, while the still gently uplifted central and northern TS became the secondary provenance.
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