Dynamic Sensor Research Articles

An Integrated High-Gain Wide–Dynamic Range Photoplethysmography Sensor for Cardiac Health Monitoring

An Integrated High-Gain Wide–Dynamic Range Photoplethysmography Sensor for Cardiac Health Monitoring

An ANN-Based Output-Error-Driven Incremental Model Predictive Control for Buck Converter Against Parameter Variations

DC/DC buck converters have been widely applied in the power processing of distributed energy resources (DERs) penetrated DC microgrid. Model predictive control (MPC) provides optimal control for the buck converter, but it often requires multiple sensors or complex observers to realize zero-steady-state-error control against parameter variations. An output-error-driven incremental MPC (OEDIMPC) for buck converter is proposed in this paper. Different from the traditional concept of calculating the optimal duty cycle, the proposed MPC calculates the optimal rate of change of the duty cycle by using a reduced-state output-error-driven prediction model. As a result, the OEDIMPC achieves zero-steady-state-error control against parameter variations, including load value, input voltage, output inductor, and output capacitor, with significantly improved dynamic performance and minimal sensor and observer requirements Besides, small- and large-signal analyzes are used to study the stability boundary of the OEDIMPC under parameter variations, and a shallow layer artificial neural network (ANN) is utilized to realize the OEDIMPC on the hardware. The proposed technique has been evaluated experimentally on a buck converter with resistor load and constant power load (CPL), confirming its effectiveness.

Dynamic Event-Triggered Sensor Fault Interval Estimation and Accommodation for Aeroengine

This paper concerns with the dynamic event-triggered fault interval estimation and accommodation for aeroengine sensors. Firstly, a dynamic event-triggered based robust augmented state observer is designed to reconstruct sensor fault for an aeroengine subject to external disturbance and measurement noise. Next, the event-triggered parameters and fault-tolerant controller (FTC) are co-designed based on the obtained observer gains to save communication resources. Moreover, the interval estimation of sensor fault is achieved via reachability analysis. Eventually, the designed scheme is tested with aeroengine Hardware-in-the-loop (HIL) experiment, which illustrates its potential in aeronautical engineering.

Dynamic Data Advertising and Packet Loss Analysis Using BLE Legacy Advertising

Bluetooth Low energy (BLE) advertisements have great potential in Internet of Things applications like monitoring systems, which involve the real-time transfer of data collected from sensors interfaced to multiple power-constrained devices. Since sensor data conveys information about the present state of the system, packet loss is vital in such applications. The paper proposes a customized BLE legacy advertising packet structure to advertise dynamic sensor data, and proposes two techniques: One-Set-Multiple-Events (OSME) and One-Set-X-Events (OSXE), to advertise the dynamic sensor data using the non-connectable and non-scannable undirected advertising mode of BLE. Using appropriate advertising parameters with OSXE, each advertising packet gets successfully advertised <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$X$</tex-math></inline-formula> times, whereas OSME neither guarantees successful advertisement of all the packets, nor does it allow control over the number of transmitted copies of each packet. Using OSXE, experimental analysis is carried out to study packet loss when single BLE node transmits alone, and when 25 nodes transmit simultaneously. Mathematical analysis has been carried out for packet collision for multiple nodes. Experimental results show that packet loss is also impacted by factors like advertising set duration, scan window, scanner's link layer activities like advertising report generation and channel switching. Using OSXE, packet loss close to 0% is obtained for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$X=3$</tex-math></inline-formula> when 25 nodes advertise simultaneously.

Dynamic Sensor Scheduling for Data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

Dynamic Sensor Scheduling for Data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning

The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. However, the non-structural spatial-temporal event-streams make it challenging for providing intuitive visualization with rich semantic information for human vision. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. However, current solutions are predominantly data-driven without considering the prior knowledge of the underlying statistics relating event-streams and video frames. It highly relies on the non-linearity and generalization capability of the deep neural networks, thus, is struggling on reconstructing detailed textures when the scenes are complex. In this work, we propose E2HQV, a novel E2V paradigm designed to produce high-quality video frames from events. This approach leverages a model-aided deep learning framework, underpinned by a theory-inspired E2V model, which is meticulously derived from the fundamental imaging principles of event cameras. To deal with the issue of state-reset in the recurrent components of E2HQV, we also design a temporal shift embedding module to further improve the quality of the video frames. Comprehensive evaluations on the real world event camera datasets validate our approach, with E2HQV, notably outperforming state-of-the-art approaches, e.g., surpassing the second best by over 40% for some evaluation metrics.

Memristor-CMOS Hybrid Circuits Implementing Event-Driven Neural Networks for Dynamic Vision Sensor Camera.

For processing streaming events from a Dynamic Vision Sensor camera, two types of neural networks can be considered. One are spiking neural networks, where simple spike-based computation is suitable for low-power consumption, but the discontinuity in spikes can make the training complicated in terms of hardware. The other one are digital Complementary Metal Oxide Semiconductor (CMOS)-based neural networks that can be trained directly using the normal backpropagation algorithm. However, the hardware and energy overhead can be significantly large, because all streaming events must be accumulated and converted into histogram data, which requires a large amount of memory such as SRAM. In this paper, to combine the spike-based operation with the normal backpropagation algorithm, memristor-CMOS hybrid circuits are proposed for implementing event-driven neural networks in hardware. The proposed hybrid circuits are composed of input neurons, synaptic crossbars, hidden/output neurons, and a neural network's controller. Firstly, the input neurons perform preprocessing for the DVS camera's events. The events are converted to histogram data using very simple memristor-based latches in the input neurons. After preprocessing the events, the converted histogram data are delivered to an ANN implemented using synaptic memristor crossbars. The memristor crossbars can perform low-power Multiply-Accumulate (MAC) calculations according to the memristor's current-voltage relationship. The hidden and output neurons can convert the crossbar's column currents to the output voltages according to the Rectified Linear Unit (ReLU) activation function. The neural network's controller adjusts the MAC calculation frequency according to the workload of the event computation. Moreover, the controller can disable the MAC calculation clock automatically to minimize unnecessary power consumption. The proposed hybrid circuits have been verified by circuit simulation for several event-based datasets such as POKER-DVS and MNIST-DVS. The circuit simulation results indicate that the neural network's performance proposed in this paper is degraded by as low as 0.5% while saving as much as 79% in power consumption for POKER-DVS. The recognition rate of the proposed scheme is lower by 0.75% compared to the conventional one, for the MNIST-DVS dataset. In spite of this little loss, the power consumption can be reduced by as much as 75% for the proposed scheme.

An event-oriented diffusion-refinement method for sparse events completion

Event cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames, and feature ultra-high sensitivity at low bandwidth. The new mechanism demonstrates great advantages in challenging scenarios with fast motion and large dynamic range. However, the recorded events might be highly sparse due to either limited hardware bandwidth or extreme photon starvation in harsh environments. To unlock the full potential of event cameras, we propose an inventive event sequence completion approach conforming to the unique characteristics of event data in both the processing stage and the output form. Specifically, we treat event streams as 3D event clouds in the spatiotemporal domain, develop a diffusion-based generative model to generate dense clouds in a coarse-to-fine manner, and recover exact timestamps to maintain the temporal resolution of raw data successfully. To validate the effectiveness of our method comprehensively, we perform extensive experiments on three widely used public datasets with different spatial resolutions, and additionally collect a novel event dataset covering diverse scenarios with highly dynamic motions and under harsh illumination. Besides generating high-quality dense events, our method can benefit downstream applications such as object classification and intensity frame reconstruction.

Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure

Abstract The experimental study investigated the impact of partially closing the end of a tubular steel pile with a diameter of 1.22 m on the induced driving vibrations. Long piles, spanning 50 m, were driven into Al-Fao soil using hammers from Daewoo Company – the PTC-110HD vibro-hammer for the initial 24 m of penetration and the IHC S-280 hydro-hammer for the remaining depth. This study established comparisons between open-end pipe (OEP) and partially closed pipe (PCP) piles concerning various parameters. These parameters included the number of blows, driving energy delivered by the hydro-hammer along the depth, and the peak soil particle velocity (PPV). Data collection was carried out using pile dynamic integral sensors for pile driving analysis, strain gauges placed along the lengths of the piles, and geophones positioned at varying distances from the pile. The results of the study unveiled a substantial effect of partial closure on the vibration response. Specifically, vibrations in the vicinity of the pile were amplified by a factor of 3–4 at depths ranging from 1 to 22 m where the vibro-hammer was employed, and by a factor of 2.5 to over 3 for the remaining depth where the hydro-hammer was utilized. Furthermore, it was observed that the rate of vibration attenuation was higher for the OEP when compared to the PCP. The vibrations are attenuated along the distance from the source where the PPV is decreased to approximately average (3.5% for OEP) and (2.3% for PCP) at a distance of around two times the penetration depth (50 m) for all embedded depths. Varied properties like stiffness, density, and damping in different soils can influence the propagation of vibrations differently. Nonetheless, as the distance from the vibration source increases, the impact of soil properties may diminish, and the vibrations tend to homogenize. Consequently, the statistical analyses yielded empirical equations that can be used for estimating vibrations in scenarios involving similar pile and hammer characteristics within comparable site conditions.

Model-level Weight Update Domain Adaptive Dynamic CNN Soft Sensor for Free Calcium Ion Concentration in Cement Clinker

The paramount determinant of clinker quality within the cement clinker production process resides in the precise prediction of F-CAO (free calcium ion) content. Given the intricate nature of cement clinker production, a profound coupling and temporal progression are inherently intertwined between the process data, as working conditions undergo gradual transformations over time. This research suggests a Model-level Weight Update Domain Adaptive Dynamic CNN Soft Sensor for Free Calcium Ion Concentration in Cement Clinker to overcome the aforementioned issues. We introduce a graph-structured two-dimensional methodology aimed at transforming both labeled and unlabeled data within the original process dataset into two-dimensional process data blocks. This innovative approach serves to enhance the spatial and temporal attributes of the labeled samples within the comprehensive sample set. Importantly, this methodology effectively addresses the intricate challenges posed by robust data coupling and the substantial presence of unlabeled samples. In response to the challenge posed by the frequent temporal fluctuations in working conditions, we introduce the Model-level Weight Update Domain Adaptive (MWUDA) approach. MWUDA leverages the hidden layer outputs from both the source and target domains to optimize the weight updates of the fully-connected layer within the CNN model. This strategic weight optimization is aimed at ameliorating the performance degradation observed in static models when confronted with shifting patterns in sample distributions. Subsequently, a limited subset of source domain samples exhibiting analogous regularities to those in the test samples is identified, enabling the acquisition of refined weights through the MWUDA method. Employing this strategy safeguards the precision of model updates throughout the MWUDA computation procedure, ensuring a robust and accurate adaptation process. The effectiveness of the proposed method is verified in an experimental study with real cement production data.

SGLFormer: Spiking Global-Local-Fusion Transformer with high performance.

Spiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance. Recognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of inaccurate gradient backpropagation caused by Maxpooling in SNNs and address it by developing a new Maxpooling module. Furthermore, we adopt spatio-temporal block (STB) in the classification head instead of global average pooling, facilitating the aggregation of spatial and temporal features. SGLFormer demonstrates its superior performance on static datasets such as CIFAR10/CIFAR100, and ImageNet, as well as dynamic vision sensor (DVS) datasets including CIFAR10-DVS and DVS128-Gesture. Notably, on ImageNet, SGLFormer achieves a top-1 accuracy of 83.73% with 64 M parameters, outperforming the current SOTA directly trained SNNs by a margin of 6.66%. With its high performance, SGLFormer can support more computer vision tasks in the future. The codes for this study can be found in https://github.com/ZhangHanN1/SGLFormer.

Restoring the discontinuous heat equation source using sparse boundary data and dynamic sensors

This study focuses on addressing the inverse source problem associated with the parabolic equation. We rely on sparse boundary flux data as our measurements, which are acquired from a restricted section of the boundary. While it has been established that utilizing sparse boundary flux data can enable source recovery, the presence of a limited number of observation sensors poses a challenge for accurately tracing the inverse quantity of interest. To overcome this limitation, we introduce a sampling algorithm grounded in Langevin dynamics that incorporates dynamic sensors to capture the flux information. Furthermore, we propose and discuss two distinct dynamic sensor migration strategies. Remarkably, our findings demonstrate that even with only two observation sensors at our disposal, it remains feasible to successfully reconstruct the high-dimensional unknown parameters.

A study on dynamic pressure sensor based on Pitot tube structure.

To meet the demand for the accurate measurements of the dynamic pressure of a shock wave, a composite dynamic pressure sensor design method is proposed based on the formation mechanism, propagation characteristics, special testing environment of the dynamic pressure, and Pitot tube structure. The dynamic pressure of the shock wave is evaluated by the total pressure and static pressure units installed in the composite sensor. FLUENT simulation software was used to analyze the aerodynamic characteristics of the dynamic pressure sensor, and parameters such as the structural size and inlet position of the sensor were determined. In response to the special experimental environment of the shock wave, the requirements for the dynamic pressure measurements under damage conditions were analyzed, and a dynamic pressure testing system was established. Dynamic pressure tests with four 2,4,6-trinitrotoluene [C7H5(NO2)3] equivalents of 1, 2, 15, and 20kg were carried out. The experimental results show that the proposed sensor design method can accurately and effectively measure the dynamic pressure signal, and the dynamic pressure gain multiple decreases with an increase in the proportional distance. This provides an effective testing method for evaluating the dynamic pressure damage effect of ammunition systems.

Operation Fault Detection of Electrical and Mechanical Equipment Based on Kalman Filter

In order to realize the remote collaborative diagnosis of electromechanical equipment fault in integrated energy system, a design method of remote collaborative diagnosis system of electromechanical equipment fault in integrated energy system based on Kalman filter is proposed. The hardware of the diagnosis system designed in this paper includes main control module, fault information parameter identification module, dynamic sensor acquisition module, fault signal filtering and detection module, adaptive control module, output interface module and so on. The collected fault information of electromechanical equipment in the system is fused by multi-source sensors, the fault information feature extraction is completed by Kalman filter, the information clustering processing is completed, and the algorithm design of electromechanical equipment operation fault diagnosis is realized. The fault diagnosis algorithm based on Kalman filter is loaded into the main control module of the motor equipment fault diagnosis system to realize the identification of motor equipment fault parameters and complete the development and design of the diagnosis system. The test results show that the text diagnosis system has good adaptability, high reliability and accuracy.

Planar Polymer Membranes Accommodate Functional Self-Assembly of Inserted Resorcinarene Nanocapsules.

Solid-supported polymer membranes (SSPMs) offer great potential in material and life sciences due to their increased mechanical stability and robustness compared to solid-supported lipid membranes. However, there is still a need for expanding the functionality of SSPMs by combining them with synthetic molecular assemblies. In this study, SSPMs served as a flexible matrix for the insertion of resorcinarene monomers and their self-assembly into functional hexameric resorcinarene capsules. Resorcinarene capsules provide a large cavity with affinity specifically for cationic and polyhydroxylated molecules. While the capsules are stable in apolar organic solvents, they disassemble when placed in polar solvents, which limits their application. Here, a solvent-assisted approach was used for copolymer membrane deposition on solid support and simultaneous insertion of the resorcinarene monomers. By investigation of the molecular factors and conditions supporting the codeposition of the copolymer and resorcinarene monomers, a stable hybrid membrane was formed. The hydrophobic domain of the membrane played a crucial role by providing a sufficiently thick and apolar layer, allowing for the self-assembly of the capsules. The capsules were functional inside the membranes by encapsulating cationic guests from the aqueous environment. The amount of resorcinarene capsules in the hybrid membranes was quantified by a combination of quartz-crystal microbalance with dissipation and liquid chromatography-mass spectrometry, while the membrane topography and layer composition were analyzed by atomic force microscopy and neutron reflectometry. Functional resorcinarene capsules inside SSPMs can serve as dynamic sensors and potentially as cross-membrane transporters, thus holding great promise for the development of smart surfaces.

Rights against the world

Abstract For philosophers, rights against the world are equivalent to rights in rem. Contrary to what Hart thought, however, this does not make them equivalent to general rights. Rights in rem contrast with rights in personam, whereas general rights contrast with special rights. As I explain, rights against the world can be either general rights or special rights. My explanation follows Waldron’s strategy of exhibiting property rights as justified by Locke’s theory of property as a case of rights in rem that are also special rights. Moreover, despite what ‘in rem’ means in Latin, rights against the world include more than property rights. For example, they also include moral human rights. With moral human rights and property rights alike, the correlative duties are borne by ‘everyone’, understood in a dynamic sense I undertake to specify.

A New Model of Air-Oxygen Blender for Mechanical Ventilators Using Dynamic Pressure Sensors.

Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air-oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli's equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives.

AIoT-enabled digital twin system for smart tunnel fire safety management

High traffic flow in a confined tunnel makes fire safety a critical issue. This paper proposed a digital twin framework for tunnel fire safety management in real-time, driven by dynamic sensor data and AIoT technologies. A deep learning model trained by the Transformer network and simulation dataset is used to predict real-time fire location and size. Then, the AI model is integrated into a 3D digital twin platform developed by the game engine Unity 3D. The performance of the proposed digital twin framework is demonstrated using numerical experiments and large-scale tunnel fire tests. Results show that the established AI model achieved promising accuracy in predicting fire location and power for both numerical and experimental data. The digital twin platform can also visualize the 3D fire scene that supports evacuation, firefighting, and emergency rescue. This research demonstrates the feasibility of using a 3D environment and digital twin in real-time fire safety management.

Transformer enhanced by local perception self-attention for dynamic soft sensor modeling of industrial processes

The use of dynamic soft sensor modeling methods to mine the time-varying and dynamic characteristics of industrial process data is of great significance for improving production efficiency and quality, given the rapid development of industrial processes and the increasing prominence of dynamic changes in the production process. However, existing dynamic soft sensor methods have limited long-term memory capacity, making it difficult to capture the long dynamic dependence, which can severely affect the results of the soft sensor model. To address this issue, we propose a dynamic soft sensor model based on local perception transformer, where the transformer is applied to achieve global perception of the variables. Through the application of the self-attention mechanism in the transformer encoder, the dynamic tracking and prediction of parameters can be realized by assigning different weights to the process variables and quality variables at different time steps, thereby adapting to the time-varying nature of the process. Additionally, convolution is used to generate a Query and Key in the self-attention mechanism, thereby enhancing local information learning. The proposed dot product self-attention calculation method effectively utilizes local information, thereby reducing the potential impact of abnormal data at a certain moment. Furthermore, by utilizing LSTM to extract time series information, the final prediction result was obtained. In soft sensor modeling experiments of the sulfur recovery unit and debutanizer tower, our proposed model demonstrated higher prediction accuracy compared to other methods, such as SVR, MLP, LSTM, CNN + LSTM, and vanilla transformer.

PDMS diaphragm based miniature fiber-optic Fabry-Perot dynamic pressure sensor for turbomachinery application.

Small-sized, highly sensitive dynamic pressure sensors are crucial in the field of turbomachinery application. In this paper, a fiber-tip structure dynamic pressure sensor utilizing a small piece of glass tube as the air cavity and PDMS material as the diaphragm was fabricated. It has the advantage of being small in size with the diameter of 125µm while having high sensitivity of 26.26pm/kPa. The fabrication process was described in detail, which is simple and cost-effective. The sensor characteristics were studied theoretically and experimentally. Quasi-square pressure signal of different frequencies generated by the siren disk were measured by the sensor and compared with that obtained from the commercial piezoresistive pressure sensor to verify the accuracy of the proposed sensor. The R2 of the four pairs of pressure waveforms were 0.94, 0.81, 0.93, and 0.96, respectively. Stability testing of the sensor was also performed, showing that the sensor can work reliably under dynamic pressure environment.