Multi-Layer Polyurethane-Fiber-Prepared Entangled Strain Sensor with Tunable Sensitivity and Working Range for Human Motion Detection.

The entanglement of fibers can form physical and topological structures, with the resulting bending and stretching strains causing localized changes in pressure. In this study, a multi-layer polyurethane-fiber-prepared (MPF) sensor was developed by coating the CNT/PU sensing layer on the outside of an elastic electrode through a wet-film method. The entangled topology of two MPFs was utilized to convert the stretching strain into localized pressure at the contact area, enabling the perception of stretching strain. The influence of coating mechanical properties and surface structure on strain sensing performance was investigated. A force regulator was introduced to regulate the mechanical properties of the entangled topology of MPF. By modifying the thickness and length proportion of the force regulator, the sensitivity factor and sensitivity range of the sensor could be controlled, achieving a high sensitivity factor of up to 127.74 and a sensitivity range of up to 58%. Eight sensors were integrated into a sensor array and integrated into a dance costume, successfully monitoring the multi-axis motion of the dancer's lumbar spine. This provides a new approach for wearable biomechanical sensors.

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism.

With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time-frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time-frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods.

Full-Textile Human Motion Detection Systems Integrated by Facile Weaving with Hierarchical Core-Shell Piezoresistive Yarns.

The tremendous progress of the wearable intelligent system has brought an urgent demand for flexible pressure sensors, especially for those possessing high sensing performances, simple manufacture technology, and efficient integration. In this work, hierarchical core-shell piezoresistive yarns (HCPYs), which contain internal silver-plated nylon electrodes and surface microporous structured carbon nanotubes (CNTs)/thermoplastic polyurethane (TPU) sensing layer, are designed and manufactured via facile wet-spinning accompanied by a water vapor coagulating bath. The obtained HCPY can either be inserted into traditional textiles to assemble a single-pressure sensor, or be woven into a textile-based flexible pressure sensors array with expected size and resolution, without compromising their comfort, breathability, and three-dimensional (3D) conformability. Simultaneously, to further enhance the sensing performance, the surface microporous structures of HCPYs are optimized by altering the treatment humidity and exposure time during the process of water vapor-induced phase separation. The wearable pressure sensors assembled by the optimal HCPY achieved a high sensitivity up to 84.5 N-1, a good durability over 5000-cycle tests, a fast response time of 2.1 ms, and a recovery time of 2.4 ms. Moreover, the wearable pressure sensors have been successfully used to monitor physical signals and human motions. The textile-based flexible pressure sensors array has also been seamlessly integrated with sportswear to detect movements of the elbow joint and map spatial pressure distribution, which makes HCPY a promising candidate for constructing next-generation wearable electronics.

Multi-criteria decision-making on assessment of proposed tidal barrage schemes in terms of environmental impacts.

For tidal range power plants to be sustainable, the environmental impacts caused by the implement of various tidal barrage schemes must be assessed before construction. However, several problems exist in the current researches: firstly, evaluation criteria of the tidal barrage schemes environmental impact assessment (EIA) are not adequate; secondly, uncertainty of criteria information fails to be processed properly; thirdly, correlation among criteria is unreasonably measured. Hence the contributions of this paper are as follows: firstly, an evaluation criteria system is established from three dimensions of hydrodynamic, biological and morphological aspects. Secondly, cloud model is applied to describe the uncertainty of criteria information. Thirdly, Choquet integral with respect to λ-fuzzy measure is introduced to measure the correlation among criteria. On the above bases, a multi-criteria decision-making decision framework for tidal barrage scheme EIA is established to select the optimal scheme. Finally, a case study demonstrates the effectiveness of the proposed framework.