Networks of sensors placed on the skin can provide continuous measurement of human physiological signals for applications in clinical diagnostics, athletics and human-machine interfaces. Wireless and ...battery-free sensors are particularly desirable for reliable long-term monitoring, but current approaches for achieving this mode of operation rely on near-field technologies that require close proximity (at most a few centimetres) between each sensor and a wireless readout device. Here, we report near-field-enabled clothing capable of establishing wireless power and data connectivity between multiple distant points around the body to create a network of battery-free sensors interconnected by proximity to functional textile patterns. Using computer-controlled embroidery of conductive threads, we integrate clothing with near-field-responsive patterns that are completely fabric-based and free of fragile silicon components. We demonstrate the utility of the networked system for real-time, multi-node measurement of spinal posture as well as continuous sensing of temperature and gait during exercise.
Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and ...pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 10⁷ Ω · kPa−1), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0–20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Electronic skins equip robots and biomedical devices with intuitive skin‐like sensitivity. Performance‐driven design of electronic skins is a critical need for electronic or biomedical applications. ...Prior research primarily focuses on investigating effects of microstructures on sensor performance at low pressure ranges. However, having predictive and tunable electro–mechanical responses across an extensive pressure range (>100 kPa) is paramount. Here, the authors propose a system that virtually customizes micropyramids for e‐skin sensors. The associations between geometry parameters, material properties, and single‐pyramid performance are systematically explored via numerical simulations, empirical characterizations, and analytical solutions. These experimentally validated models allow for the determination of the sensor parameters for the desired performance. An augmented reality interface system for surgery skills training by optimizing sensitivities that match varying tissue stiffnesses is further demonstrated. The platform enables greater effectiveness in rapidly iterating and designing micropyramidal e‐skin for applications in augmented reality interfaces, robotics, and telehealthcare.
An augmented reality surgical interface system can aid in surgical training of hand stability. Piezo‐capacitive sensors made using pyramidal microstructures transduce tactile feedbacks and the data are used for conducting virtual excisions. Sensor performance is tuned with the new predictive mechanical models developed through single‐pyramid level microstructure indentation measurements.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Reliable online transient stability assessment (TSA) is fundamentally required for power system operation security. Compared with time-costly classical digital simulation methods, data-driven deep ...learning (DL) methods provide a promising technique to build a TSA model. However, general DL models show poor adaptability to the variation of power system topology. In this paper, we propose a new graph-based framework, which is termed as recurrent graph convolutional network based multi-task TSA (RGCN-MT-TSA). Both the graph convolutional network (GCN) and the long short-term memory (LSTM) unit are aggregated to form the recurrent graph convolutional network (RGCN), where the GCN explicitly integrate the bus (node) states with the topological characteristics while the LSTM subsequently captures the temporal features. We also propose a multi-task learning (MTL) scheme, which provides joint training of stability classification (Task-1) as well as critical generator identification (Task-2) in the framework, and accelerate the process with parallel computing. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models, as well as its robustness under various scenarios.
This article presents a versatile soft robotic gripper system whereby its fingers can be reconfigured into different poses such as scoop, pinch, and claw. This allows the gripper to efficiently and ...safely handle food samples of different shapes, sizes and stiffness such as uncooked tofu and broccoli floret. The 3D-printed fingers were tested to last up to 25 000 cycles without significant changes in the curvature profile and force output profile. A benchmark experiment was conducted to evaluate the performance of the gripper and state-of-the-art gripping solutions. Capability of versatile soft gripper was optimized by integrating vision and tactile sensing facilities. An object recognition system was developed to identify food samples such as potato, broccoli, and sausage. Position and orientation of food samples were identified and pick-and-place pathway was optimized to achieve the best gripping performance. Flexible tactile sensors were integrated into soft fingers and closed-loop force feedback control system was developed. This allowed the gripper to automatically explore and select the most stable grip pose for different food samples. Integration of vision and force feedback system ensure that objects detected by the system would be firmly gripped. The reconfigurable soft robotic gripper system has been demonstrated to perform high-speed pick-and-place tasks (∼3 s per item) with object recognition system, making it a potential solution to food and grocery supply chain needs.
Monitoring surgical wounds post-operatively is necessary to prevent infection, dehiscence and other complications. However, the monitoring of deep surgical sites is typically limited to indirect ...observations or to costly radiological investigations that often fail to detect complications before they become severe. Bioelectronic sensors could provide accurate and continuous monitoring from within the body, but the form factors of existing devices are not amenable to integration with sensitive wound tissues and to wireless data transmission. Here we show that multifilament surgical sutures functionalized with a conductive polymer and incorporating pledgets with capacitive sensors operated via radiofrequency identification can be used to monitor physicochemical states of deep surgical sites. We show in live pigs that the sutures can monitor wound integrity, gastric leakage and tissue micromotions, and in rodents that the healing outcomes are equivalent to those of medical-grade sutures. Battery-free wirelessly operated bioelectronic sutures may facilitate post-surgical monitoring in a wide range of interventions.
Abstract In the simulation of complex power electronic equipment containing multiple power electronic switches, it is often encountered that the same switch occurs several times in one step, or there ...are multiple switch actions in one step, that is, multiple switches. When the switching event occurs between two moments, because the voltage value can only be updated at the end of the simulation step, there is a cumulative error in the current obtained by the integration. In traditional electromagnetic transient simulation, the interpolation algorithm and its deformation are commonly used to solve this problem. However, due to the complexity of the conventional interpolation algorithm, it will seriously affect the efficiency in large-scale power grid simulation, and can only be applied to offline simulation, which cannot meet the needs of real-time simulation. In this paper, a method is presented to deal with synchronous switches and multiple switches, which can reliably solve the problem of synchronous switches and multiple switches encountered in real-time simulation of complex power electronic systems.
Data-driven transient stability assessment (TSA) models are usually sensitive to system scale changes and require dynamic information from time-domain simulation (TDS) as inputs. We propose a S ...ystem-sc A le- F re E T ransient C ontingency S creening (SAFE-TCS) scheme based on only the steady-state measurements. An analytical model is set up to estimate the state variation at fault occurrence (<inline-formula><tex-math notation="LaTeX">t_{0+}</tex-math></inline-formula>) snapshot, which forms multi-graph inputs together with the steady-state information. A novel pooling-ensemble multi-attention graph convolutional network (PE-MAGCN) realizes the spatio-temporal graph embedding, in which an inter-graph convolution link works for the temporal abstraction. Following a pooling-ensemble mechanism, PE-MAGCN derives a fixed-size expressive vector for task-specific networks. This promotes the robustness of the model against system extension. The advantages of SAFE-TCS also benefit from the coordination of various training tricks, including channel attention, category-balanced sampling and joint-decoupling learning, etc. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models and its adaptability under various scenarios.
•A scheme develops TSA almost immediately after fault occurrence.•A physical-enhanced graph attention mechanism with residual structure for topology learning.•A piece-wise stability index (PSI) is ...proposed.•Both the stability category and stability level are predicted.•Excellent accuracy and robustness to noise and topological changes.
Reliable and fast transient stability assessment (TSA) is significantly required for the power system emergency control. We propose a topology adaptive high-speed transient stability assessment (HSTSA) scheme, where the inputs of the model adopt only the pre-fault state and the dynamics at the fault occurrence snapshot. A novel multi-graph attention network with residual structure (ResGAT) is designed to capture the stability characteristics. ResGAT applies improved graph attention mechanism to enhance its adaptability to the power system topology changes and the residual structure helps to avoid network degeneration. Meanwhile, a new piece-wise transient stability index (PSI) is proposed for the stability level prediction. Integration of both the stability category and the stability level results increase the precision of the HSTSA scheme. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models and its robustness under various scenarios. .
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The development of modern robotics has triggered increasing interest in developing artificial tactile sensory systems. However, the tactile mechanism in the design process has been highly limited to ...experimental tests that are expensive and time-consuming. This work is concerned with the development of using virtual tests for tactile sensory response prediction and design, which includes numerical simulation settings, database creation, response regression and interpolation, and sensor sensitivity designs. Experimental verifications and numerical demonstrations are performed based on the NUS NeuTouch sensors. The potential of using virtual tests to design new tactile sensors to improve response linearity is illustrated.
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•Tactile response prediction and design using virtual tests is proposed with demonstration on NUS NeuTouch sensors.•The framework consists of numerical simulations, database creation, response regression and interpolation.•The potential of using virtual tests to design new tactile sensors for improving response linearity is illustrated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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