Using wearable sensors to identify human activities has elicited significant interest within the discipline of ubiquitous computing for everyday facilitation. Recent research has employed hybrid models to better leverage the modal information of sensors and temporal information, enabling improved performance for wearable human activity recognition. Nevertheless, the lack of effective exploitation of human structural information and limited capacity for cross-channel fusion remains a major challenge. This study proposes a generic design, called GT-WHAR, to accommodate the varying application scenarios and datasets while performing effective feature extraction and fusion. Firstly, a novel and unified representation paradigm, namely Body-Sensing Graph Representation , has been proposed to represent body movement by a graph set, which incorporates structural information by considering the intrinsic connectivity of the skeletal structure. Secondly, the newly designed Body-Node Attention Graph Network employs graph neural networks to extract and fuse the cross-channel information within the graph set. Eventually, the graph network has been embedded in the proposed Bidirectional Temporal Learning Network , facilitating the extraction of temporal information in conjunction with the learned structural features. GT-WHAR outperformed the state-of-the-art methods in extensive experiments conducted on benchmark datasets, proving its validity and efficacy. Besides, we have demonstrated the generality of the framework through multiple research questions and provided an in-depth investigation of various influential factors.

Since individual neural networks have limited deep expressiveness and effectiveness, many learning frameworks face difficulties in the availability and balance of sample selection. As a result, in change detection, it is difficult to upgrade the hit rate of a high-performance model on both positive and negative pixels. Therefore, supposing that the sacrificed components coincide perfectly with the important evaluation objectives, such as positives, it would lose more than gain. To address this issue, in this paper, we propose a multi-visual collaborative deep network (MV-CDN) served by three collaborative network members that consists of three subdivision approaches, the CDN with one collaborator (CDN-C), CDN with two collaborators (CDN-2C), and CDN with three collaborators (CDN-3C). The purpose of the collaborator is to re-evaluate the feature elements in the network transmission, and thus to translate the group-thinking into a more robust field of vision. We use three sets of public double-temporal hyperspectral images taken by the AVIRIS and HYPERION sensors to show the feasibility of the proposed schema. The comparison results have confirmed that our proposed schema outperforms the existing state-of-the-art algorithms on the three tested datasets, which demonstrates the broad adaptability and progressiveness of the proposal.

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water–slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase.