Physical Signals Research Articles

Vibro-Acoustic Responses and Pressure Signal Analysis for Early Surge Detection in a Turbocharger Compressor

Abstract High-speed centrifugal compressors may be exploited to pressurize fuel cell systems. Nonetheless, due to fuel cells significant interposed volumes, compressor behavior can lead to severe vibrations related to fluid-dynamic instabilities during part load operating conditions. In particular, surge strongly limits centrifugal compressors stable operating region when moving toward low mass flow rates due to a change in system working point. Therefore, compressor dynamic response must be adequately characterized for early surge detection. To this aim, a dedicated experimental activity was conducted on a vaneless diffuser turbocharger coupled to a solid oxide fuel cell emulator plant; compressor evolution toward surge was investigated. Several signal processing techniques were applied to pressure signals as well as vibro-acoustic responses to better predict compressor behavior and classify its status as stable or unstable. Cepstrum, cross-correlation, and wavelet transform have been identified as suitable techniques to define precursors able to early detect surge. By means of cross-correlation function, propagation phenomena in the ducts can be investigated to assess how they interact near compressor low-mass flowrate unstable conditions. Cepstrum provides a convenient way to determine pressure signal spectrum distortion in terms of further periodic components onset. These harmonic components are due to complex system responses generated by transient phenomena; indeed, cepstrum allows to identify hidden anomalous contributions in system response spectra which may arise in incipient surge conditions. Wavelet transform was performed on both structural and pressure response signals to observe their dominant energy contents temporal evolution; indeed, such spectral pattern time-dependent variation can detect the rise of unstable conditions. By exploiting all these techniques, a complete system identification is performed which allows a deeper investigation of the physical phenomena involved; moreover, a more complete set of surge precursors extracted from different probes' physical signals were defined. The results obtained provide original diagnostic insights for monitoring systems suited to perform early surge detection. Compressor instability prevention can extend its operating range, performance, and reliability to allow better integration with other plant components. Finally, cepstrum application for compressor instability identification can be regarded as a novel method in the fluid machinery field.

Deep Learning - Powered Radio Frequency Fingerprint Identification: Methodology and Case Study

Radio frequency fingerprint identification (RFFI) is an authentication technique that identifies wireless devices by analyzing the characteristics of the received physical layer signals. In recent years, RFFI has been significantly enhanced by deep learning. A neural network (NN) is often leveraged to predict device identity. As a data-driven approach, deep learning requires the collection of large amounts of data for NN training. In addition, the RFFI system should be evaluated on datasets collected under various conditions to assess the system's robustness. However, only a few RFFI datasets are publicly available, and there are no clear guidelines for building an RFFI testbed for data collection. This paper presents a tutorial to build both closed-set and openset RFFI systems. A LoRa- RFFI testbed is created as a case study and the implementation details are described in depth. The LoRa-RFFI testbed involves 60 commercial-off-the-shelf (COTS) LoRa development boards as devices to be identified, and a USRP N210 software-defined radio (SDR) platform for physical layer signal reception. Experiments are carried out using the implemented LoRa-RFFI testbed, and the collected datasets are made publicly available online. It is anticipated that this work can aid the research community in constructing RFFI testbeds and facilitate the development of RFFI research.

(Invited) micro-Nanoscale Living Lasers for Healthcare Applications

Have you imagined of carrying a laser in your cell or body? Lasers are ubiquitous in our daily lives from industry, communication to medicine. The scale of lasers has also shrunk down to micron and nanoscales. As the scale of laser become smaller, the functions of lasers have also been redefined by transforming living biologicals into micro- and nanoscale lasers, so called living lasers. Such tiny lasers could therefore be used to detect or monitor critical chemical or physical signals in living cells or human body with distinctive sensitivity and intensity. In this talk, I will introduce the recent development of biological lasers and showcase how this interdisciplinary technology bridges laser physics and biology to tackle biomedical problems. In the first part, the talk will be focused on its application in detecting cancer and neurological diseases. In the second part, I will introduce how tiny lasers can be transformed with intelligent functions for information encoding and healthcare. This will cover applications including sensing at multiscales in biology, imaging in tissues, tracking and monitoring, as well as biomedical devices. Finally, discussion and outlook will be made on the strategies to pioneer novel on-chip and wearable laser devices for future medical diagnosis and bio-intelligent technology. Figure 1

A Novel Fault Diagnosis Method for Train Real‐Time Ethernet Network Based on Physical Layer Electrical Signal Features

Train real‐time ethernet network (TREN) is responsible for the transmission of train control commands and equipment status information. Due to the harsh and changeable climatic environments, complex electromagnetic environments, and long service, the TREN has a high incidence of physical layer failures, which affects train operation safety. This paper presents a novel fault diagnosis method in which waveform electrical signal features are extracted from the physical layer signal of TREN. Random forest is used for identifying typical failure modes of actual TREN, and the model parameters are optimized to improve diagnostic performance. Finally, the physical layer signals of actual TREN are collected. The results show that the performance of the proposed fault diagnosis method is better than other advanced methods. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Wearable Recognition System for Complex Motions Based on Hybrid Deep‐Learning‐Enhanced Strain Sensors

Wearable recognition systems based on flexible electronics present immense potential for applications in human–machine interfaces, medical care, soft robots, etc. However, they experience challenges in terms of the nonideal consistency and stability of flexible sensors, which are responsible for detecting physical signals from human motions. These challenges hinder the improvement of recognition precision and capability in the wearable systems. Furthermore, the computational consumption for the recognition increases as more sensors are used to extensively gather information for distinguishing between complex motions. Herein, a wearable recognition system based on deep‐learning‐enhanced strain sensors for distinguishing between the complex motions of the human body is presented. A strain sensor based on peak–valley microstructures is fabricated and packaged to improve consistency and stability. Moreover, a lightweight hybrid convolutional neural network long short‐term memory model is designed to lower the computational costs of the deep learning process. In particular, by designing Butterworth filtering and Z‐score normalization algorithms, the error in feature extraction caused by sensor signal fluctuation is reduced, thereby improving the recognition accuracy of the proposed wearable system to 95.72% for seven gait motions and 100% for four different continuous series of Tai Chi forms.

Study on a Novel Strategy for High-Quality Grinding Surface Based on the Coefficient of Friction

Surface quality has a significant impact on the service life of machine parts. Grinding is often the last process to ensure surface quality and accuracy of material formation. In this study, a high-quality surface was developed by determining the coefficient of friction in grinding a quartz fiber-reinforced silica ceramic composite. By processing the physical signals in the grinding process, a multi-objective function was established by considering grinding parameters, i.e., surface roughness, coefficient of friction, active energy consumption, and effective grinding time. The weight vector coefficients of the sub-objective functions were optimized through a multi-objective evolutionary algorithm based on the decomposition (MOEA/D) algorithm. The genetic algorithm was used to optimize the process parameters of the multi-objective function, and the optimal range for the coefficient of friction was determined to be 0.197~0.216. The experimental results indicated that when the coefficient of friction tends to 0.197, the distribution distance of the microscopic data points on the surface profile is small and the distribution uniformity is good. When the coefficient of friction tends to 0.216, the surface profile shows a good periodic characteristic. The quality of a grinding surface depends on the uniformity and periodicity of the surface’s topography. The coefficient of friction explained the typical physical characteristics of high-quality grinding surfaces. The multi-objective optimization function was even more important for the subsequent high-quality machining of mechanical parts to provide guidance and reference significance.

Development of smart cardiovascular measurement system using feature selection and machine learning models for prediction of sleep deprivation, cold hands and feet, and Shanghuo syndrome

This study aimed to develop a smart cardiovascular measurement system using ECG and PPG to evaluate health issues: sleep deprivation, cold hands and feet, and the Shanghuo syndrome. The proposed methods extracted features from physical Signal and utilized diverse machine learning techniques for the evaluation. The results demonstrated prediction accuracies exceeding 82% (87% for sleep deprivation using k-nearest neighbor, 83% for cold hands and feet using a kernel classifier, and 82% for the Shanghuo syndrome using ensemble learning). Moreover, this study identified novel features associated with sleep deprivation, cold hands and feet, and the Shanghuo syndrome in the context of traditional Chinese medicine (TCM). An accurate prediction of TCM-defined cold hands and feet and Shanghuo syndrome, while considering relevant physiological features, is critical in the field of machine learning research. The developed system can be seamlessly integrated with the existing instruments to facilitate self-health management and collaboration with medical treatment.

Doping Engineering of Conductive Polymers and Their Application in Physical Sensors for Healthcare Monitoring.

Physical sensors have emerged as a promising technology for real-time healthcare monitoring, which tracks various physical signals from the human body. Accurate acquisition of these physical signals from biological tissue requires excellent electrical conductivity and long-term durability of the sensors under complex mechanical deformation. Conductive polymers, combining the advantages of conventional polymers and organic conductors, are considered ideal conductive materials for healthcare physical sensors due to their intrinsic conductive network, tunable mechanical properties, and easy processing. Doping engineering has been proposed as an effective approach to enhance the sensitivity, lower the detection limit, and widen the operational range of sensors based on conductive polymers. This approach enables the introduction of dopants into conductive polymers to adjust and control the microstructure and energy levels of conductive polymers, thereby optimizing their mechanical and conductivity properties. This review article provides a comprehensive overview of doping engineering methods to improve the physical properties of conductive polymers and highlights their applications in the field of healthcare physical sensors, including temperature sensors, strain sensors, stress sensors, and electrophysiological sensing. Additionally, the challenges and opportunities associated with conductive polymer-based physical sensors in healthcare monitoring are discussed.

Dual-bionic regenerative microenvironment for peripheral nerve repair.

Autologous nerve grafting serves is considered the gold standard treatment for peripheral nerve defects; however, limited availability and donor area destruction restrict its widespread clinical application. Although the performance of allogeneic decellularized nerve implants has been explored, challenges such as insufficient human donors have been a major drawback to its clinical use. Tissue-engineered neural regeneration materials have been developed over the years, and researchers have explored strategies to mimic the peripheral neural microenvironment during the design of nerve catheter grafts, namely the extracellular matrix (ECM), which includes mechanical, physical, and biochemical signals that support nerve regeneration. In this study, polycaprolactone/silk fibroin (PCL/SF)-aligned electrospun material was modified with ECM derived from human umbilical cord mesenchymal stem cells (hUMSCs), and a dual-bionic nerve regeneration material was successfully fabricated. The results indicated that the developed biomimetic material had excellent biological properties, providing sufficient anchorage for Schwann cells and subsequent axon regeneration and angiogenesis processes. Moreover, the dual-bionic material exerted a similar effect to that of autologous nerve transplantation in bridging peripheral nerve defects in rats. In conclusion, this study provides a new concept for designing neural regeneration materials, and the prepared dual-bionic repair materials have excellent auxiliary regenerative ability and further preclinical testing is warranted to evaluate its clinical application potential.

Mere tension output from spring-linkage-based mechanical metamaterials.

Metamaterials whose properties are inaccessible with conventional materials offer powerful tools for unprecedentedly manipulating physical signals. However, an effective design strategy of metamaterials still remains a challenge for changing the compression or tension characters of stress waves during forward propagation. Here, we introduce a class of spring-linkage-based metamaterials exhibiting mere tension output at the distal end, no matter that the input is an axial impact, a sudden tension, or even alternating tension-compression. The metamaterials can turn compressive waves into pure tension and filter them out from the tension-compression mixed ones while allowing tensile signal stably propagating in soliton form. This is achieved by combining nonuniform and nonlinear properties of the proposed cells. In particular, these extraordinary functions of the metamaterial can be turned on or off and adjusted by tuning a key switch cell; thus, it is anticipated to serve as a start for more complex manipulation and utilization of mechanical signals.

Enhanced germination and electrotactic behaviour of Phytophthora palmivora zoospores in weak electric fields

Soil-dwelling microorganisms use a variety of chemical and physical signals to navigate their environment. Plant roots produce endogenous electric fields which result in characteristic current profiles. Such electrical signatures are hypothesised to be used by pathogens and symbionts to track and colonise plant roots. The oomycete pathogen Phytophthora palmivora generates motile zoospores which swim towards the positive pole when exposed to an external electric field in vitro. Here, we provide a quantitative characterization of their electrotactic behaviour in 3D. We found that a weak electric field (0.7–1.0 V cm−1) is sufficient to induce an accumulation of zoospore at the positive pole, without affecting their encystment rate. We also show that the same external electric field increases the zoospore germination rate and orients the germ tube’s growth. We conclude that several early stages of the P. palmivora infection cycle are affected by external electric fields. Taken together, our results are compatible with the hypothesis that pathogens use plant endogenous electric fields for host targeting.

Physical Artificial Arterial Pulse System for Development and Testing of PPG-Based Sensors.

A physical system to generate a PPG-mimicking signal was designed and validated using everyday low-cost components to aid in medical sensor design. The pulse waveform was created by driving a working fluid into a silicone tube and changing the pressure within it. The corresponding waveform mimics a PPG signal through an artery, is adaptable, and repeatable. The working fluid is interchangeable allowing for change of blood analyte concentrations for development and testing of PPG-based sensors. The system was validated by black ink water compared to water and air compared to water testing to confirm optical transparency of the tube. The produced PPG signal, pulse rate and pressure change were compared to that seen in subjects. Optical transparency for 660 nm - 1550 nm wavelengths of light was validated with the signal, pulse rate and total compliance matching subject data. Thus, the system can mimic arterial pulses, creating a valid PPG signal that can be detected by PPG-based sensors.Clinical Relevance- Provides a low-cost, adaptable, physical PPG signal generator for research and development of optical medical sensor technologies.

Mechanobiology-informed biomaterial and tissue engineering strategies for influencing skeletal stem and progenitor cell fate.

Skeletal stem and progenitor cells (SSPCs) are the multi-potent, self-renewing cell lineages that form the hematopoietic environment and adventitial structures of the skeletal tissues. Skeletal tissues are responsible for a diverse range of physiological functions because of the extensive differentiation potential of SSPCs. The differentiation fates of SSPCs are shaped by the physical properties of their surrounding microenvironment and the mechanical loading forces exerted on them within the skeletal system. In this context, the present review first highlights important biomolecules involved with the mechanobiology of how SSPCs sense and transduce these physical signals. The review then shifts focus towards how the static and dynamic physical properties of microenvironments direct the biological fates of SSPCs, specifically within biomaterial and tissue engineering systems. Biomaterial constructs possess designable, quantifiable physical properties that enable the growth of cells in controlled physical environments both in-vitro and in-vivo. The utilization of biomaterials in tissue engineering systems provides a valuable platform for controllably directing the fates of SSPCs with physical signals as a tool for mechanobiology investigations and as a template for guiding skeletal tissue regeneration. It is paramount to study this mechanobiology and account for these mechanics-mediated behaviors to develop next-generation tissue engineering therapies that synergistically combine physical and chemical signals to direct cell fate. Ultimately, taking advantage of the evolved mechanobiology of SSPCs with customizable biomaterial constructs presents a powerful method to predictably guide bone and skeletal organ regeneration.

Optical Visualization of Cadmium Ion Using a Label-Free Nucleic Acid Aptamer for Recognition

Aptamer-based biosensors have been demonstrated to provide real-time physical and chemical signals for biomedicine, food safety, drug identification, and metal ion determination. In this study, cadmium ion (Cd2+) was determined in drinking water by color signaling using the specific binding of the aptamer and target by its regulation of the catalytic capacity of gold nanoparticles (AuNPs). When sodium borohydride (NaBH4) and a probe with a catalytic function were present, the solution containing 4-nitrophenol changed from yellow to colorless. Under optimal conditions, the naked eye was sufficient to determine Cd2+ with a limit of detection (LOD) of 5 μM. The discoloration time showed a positive correlation with Cd2+ concentration up to 30 μM. This method was label-free, efficient, rapid, and selective for Cd2+ in water without expensive instruments, suggesting practical applications.

Biomimetic Flexible Sensors and Their Applications in Human Health Detection.

Bionic flexible sensors are a new type of biosensor with high sensitivity, selectivity, stability, and reliability to achieve detection in complex natural and physiological environments. They provide efficient, energy-saving and convenient applications in medical monitoring and diagnosis, environmental monitoring, and detection and identification. Combining sensor devices with flexible substrates to imitate flexible structures in living organisms, thus enabling the detection of various physiological signals, has become a hot topic of interest. In the field of human health detection, the application of bionic flexible sensors is flourishing and will evolve into patient-centric diagnosis and treatment in the future of healthcare. In this review, we provide an up-to-date overview of bionic flexible devices for human health detection applications and a comprehensive summary of the research progress and potential of flexible sensors. First, we evaluate the working mechanisms of different classes of bionic flexible sensors, describing the selection and fabrication of bionic flexible materials and their excellent electrochemical properties; then, we introduce some interesting applications for monitoring physical, electrophysiological, chemical, and biological signals according to more segmented health fields (e.g., medical diagnosis, rehabilitation assistance, and sports monitoring). We conclude with a summary of the advantages of current results and the challenges and possible future developments.

Massive Shape Formation in Grid Environments

Shape formation mechanism plays an essential role in many natural processes, involving the formation and evolution of living or non-living structures, and shows potential applications in many emerging domains. In existing research and practice, there still lacks a shape formation mechanism that manifests efficiency, scalability, and stability at the same time. Inspired by phototaxis observed in nature, we propose a self-organized approach for the massive formation of connected shapes in grid environments. The key component of this approach is an artificial light field superimposed on a grid environment, which is determined by the positions of all agents and at the same time drives all agents to change their positions, forming a dynamic mutual feedback process. To evaluate the effectiveness of this approach, we conduct a set of simulations, involving <inline-formula> <tex-math notation="LaTeX">$156$</tex-math> </inline-formula> shapes from <inline-formula> <tex-math notation="LaTeX">$16$</tex-math> </inline-formula> categories, comparing with four baseline methods. The results show that: (1) our approach outperforms the three semi-/decentralized non-optimal baselines in efficiency, scalability, and stability; (2) compared to the centralized optimal baseline, our approach exhibits considerable decreases in the absolute completion time on diverse shape formation tasks, indicating a better efficiency and scalability of our approach. <i>Note to Practitioners</i>&#x2014;In nature, shape formation phenomena emerge from collective behaviors of swarms based on chemical or physical signals. These natural phenomena provide valuable insights to build large-scale multi-agent collaboration systems using software-defined digital signals. This work proposes a phototaxis-inspired computational approach for shape formation that enables a massive swarm of agents to form arbitrary connected shapes in grid environments based on a digital signal called artificial light field. The significance of this work is twofold: 1. it could contribute to a deep understanding of shape formation mechanisms; 2. it would motivate new research on advanced multi-agent algorithms, massive collaboration mechanisms, and artificial collective intelligence systems and facilitate their practical applications. Specifically, the shape formation mechanism has promising applications, including smart warehouses, autonomous cooperation of UAVs, and intelligent transportation systems. A possible realistic application scenario of our method in intelligent transportation systems is bike sharing systems, in which the designated parking areas for shared bicycles near the work area are often overcrowded and difficult to park in during the morning peak period, so dynamic parking route guidance for users is required. Our method can directly apply to this scenario by utilizing the light field to represent the parking state of nearby bicycles and guiding the moving direction of each user.

Recent research progress on the stimuli-responsive smart membrane: A review

Abstract The smart membrane is a new type of functional membrane. The performance of this membrane is changed according to the variations in external physical and chemical signals. This membrane has become an essential focus in specific recognition, catalysis, selective permeation, and other fields. However, the problems of this membrane are weak anti-pollution ability, poor response performance, and inability of mass production. Therefore, scholars have done a lot of research on improving this membrane by modification, grafting polymerization, phase transformation, and in situ cross-linking copolymerization. This review provides a comparative investigation and summary of smart membranes, including temperature, light, electric field, magnetic field, pH, and specific molecular and ion-responsive membranes. Moreover, the authors also introduce the preparation process, selectivity, optimization and improvement of membranes, and their application fields. Finally, the authors’ perspective on the current key issues and directions of these fields for future development are also discussed.

ДОСЛІДЖЕННЯ ЦИФРОВИХ ТЕРМІНАЛІВ ЗАХИСТУ

Abstract. Due to the development of technologies, the widely used electromechanical relays in the electric power industry have been replaced by multifunctional digital relay protection terminals. For the training of students and electrical personnel of enterprises, new approaches to the creation of experimental stands are needed, which will allow us to study the features of the use of digital protection and fully reveal their capabilities. The purpose of the work is to improve the quality of the knowledge and practical skills received by students in working with digital protection blocks. To achieve the goal, a specialized laboratory stand for testing digital security terminals has been developed. The stand consists of a mathematical model of the protected object and hardware, due to which the model mode parameters are converted into physical signals and fed in real time to a real digital device. The study of the protected element is provided in the characteristic modes of its operation close to real ones by providing a connection between the relay protection terminal and the information model. The equipment used at the stand does not require significant capital investments, and therefore this approach is available to most design and training organizations. Keywords: digital protection terminal, experimental stand, analog-to-digital converter; digital-to-analog converter, mathematical model, induction motor.

Quickest detection of deception attacks on cyber–physical systems with a parsimonious watermarking policy

Adding a physical watermarking signal to the control input of a networked control system increases the detection probability of data deception attacks at the expense of increased control cost. This paper proposes a parsimonious policy to limit the average number of watermarking events when the attack is not present, which in turn reduces the control cost. We model the system as a stochastic optimal control problem and apply dynamic programming to minimize the average detection delay (ADD) for fixed upper bounds on false alarm rate (FAR) and an average number of watermarking events (ANW) before the attack. Under practical circumstances, the optimal solution results in a two threshold policy on the posterior probability of attack, derived from the Shiryaev statistics for sequential change detection and assuming the change point is a random variable. We derive asymptotically approximate analytical expressions of ADD and FAR, applying the non-linear renewal theory for non-independent and identically distributed data. The derived expressions reveal that ADD reduces with the increase in the Kullback–Leibler divergence (KLD) between the post- and pre-attack distributions of the test statistics. Therefore, we further design the optimal watermarking that maximizes the KLD for a fixed increase in the control cost. The relationship between the ANW and the increase in control cost is also derived. Simulation studies are performed to illustrate and validate the theoretical results.

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.