Waveform Characteristics Research Articles

Pilot protection for hybrid multi-terminal HVDC transmission lines based on Euclidean distance between differential current and distributed capacitance current

Aiming at the influence of distributed capacitance on differential current in long HVDC transmission lines, the waveform characteristics of differential current and distributed capacitance current is investigated. It is discovered that the waveforms similarity between differential current and distributed capacitance current can effectively identify internal fault and external fault. On this basis, normalized Euclidean distance is adopted to extract the similarity features, and then a new principle of pilot protection for hybrid multi-terminal HVDC transmission lines based on Euclidean distance is proposed. Finally, extensive simulation tests on three-terminal bipolar Hybrid-MTDC transmission system built in PSCAD/ EMTDC show that the proposed protection can detect faults rapidly and accurately, does not depend on complicated calculation, strict data synchronization and precise line parameters, with high sensitivity and reliability.

Detecting slow eye movements using multi-scale one-dimensional convolutional neural network for driver sleepiness detection

Background:Slow eye movements (SEMs), which occurs during eye-closed periods with high time coverage rate during simulated driving process, indicate drivers’ sleep onset. New Method:For the multi-scale characteristics of slow eye movement waveforms, we propose a multi-scale one-dimensional convolutional neural network (MS-1D-CNN) for classification. The MS-1D-CNN performs multiple down-sampling processing branches on the original signal and uses the local convolutional layer to extract the features for each branch. Results:We evaluate the classification performance of this model on ten subjects’ standard train–test datasets and continuous test datasets by means of subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the standard train–test datasets, the overall average classification accuracies are about 99.1% and 98.6%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the continuous test datasets, the overall average values of accuracy, precision, recall and F1-score are 99.3%, 98.9%, 99.5% and 99.1% in subject-subject evaluation, are 99.2%, 98.8%, 99.3% and 99.0% in leave-one-subject-out cross validation. Comparison with existing method:Results of the standard train–test datasets show that the overall average classification accuracy of the MS-1D-CNN is quite higher than the baseline method based on hand-designed features by 3.5% and 3.5%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. Conclusions:These results suggest that multi-scale transformation in the MS-1D-CNN model can enhance the representation ability of features, thereby improving classification accuracy. Experimental results verify the good performance of the MS-1D-CNN model, even in leave-one-subject-out cross validation, thus promoting the application of SEMs detection technology for driver sleepiness detection.

Non-invasive monitoring of intracranial pressure changes: healthy volunteers study.

Objective: This research aims to evaluate the possible association between pulsatile near infrared spectroscopic waveform features and induced changes in intracranial pressure in healthy volunteers. Methods: An optical intracranial pressure sensor was attached to the forehead of 16 healthy volunteers. Pulsatile near infrared spectroscopic signals were acquired from the forehead during body position changes and Valsalva manoeuvers. Features were extracted from the pulsatile signals and analyses were carried out to investigate the presence of statistical differences in the features when intracranial pressure changes were induced. Classification models were developed utilizing the features extracted from the pulsatile near-infrared spectroscopic signals to classify between different body positions and Valsalva manoeuvre. Results: The presence of significant differences in the majority of the analyzed features (p 0.05) indicates the technique's ability to distinguish between variations in intracranial pressure. Furthermore, the disparities observed in the optical signal features captured by the proximal and distal photodetectors support the hypothesis that alterations in back-scattered light directly correspond to brain-related changes. Further research is required to subtract distal and proximal signals and construct predictive models employing a gold standard measurement for non-invasive, continuous monitoring of intracranial pressure. Conclusion: The study investigated the use of pulsatile near infrared spectroscopic signals to detect changes in intracranial pressure in healthy volunteers. The results revealed significant differences in the features extracted from these signals, demonstrating a correlation with ICP changes induced by positional changes and Valsalva manoeuvre. Classification models were capable of identifying changes in ICP using features from optical signals from the brain, with a sensitivity ranging from 63.07% to 80% and specificity ranging from 60.23% to 70% respectively. These findings underscored the potential of these features to effectively identify alterations in ICP. Significance: The study's results demonstrate the feasibility of using features extracted from optical signals from the brain to detect changes in ICP induced by positional changes and Valsalva manoeuvre in healthy volunteers. This represents a first step towards the non-invasive monitoring of intracranial pressure.

Study on the penetration capability of GPR for the steel-fibre reinforced concrete (SFRC) segment based on numerical simulations and model test

The SFRC segment is gaining attention in shield tunnels due to their better mechanical properties. However, the existence of steel fibres poses a greater difficulty when attempting to utilize ground penetrating radar (GPR) for nondestructive quality inspection compared to conventional reinforced concrete segments. This paper presented a refined numerical model of SFRC segments with gprMax software, to investigate the penetration characteristics of GPR. The numerical model incorporated four different steel fibre contents, namely 0%, 0.5%, 2.5%, and 5%. The model also included an internal void and a surface metal plate to compare and analyze the waveform characteristics of GPR with the normal sections of the segment. The orthogonal test of numerical simulation investigates the imaging characteristics, power attenuation patterns, and imaging features of three different GPR frequency gradients: 200 MHz, 500 MHz, and 1000 MHz. This analysis helps in selecting the appropriate detection frequency based on the observed energy attenuation patterns and imaging characteristics. Then, the model test was conducted at the selected frequency to study the penetration performance of GPR. The research results indicate that the numerical model established in this paper demonstrates good effectiveness in studying the waveform characteristics of steel fibre-reinforced concrete under various conditions. The numerical model established in this paper realized the study of the penetration characteristics and imaging features of different steel fibre contents, GPR frequencies, and detection targets. Analysis of waveform characteristics, power attenuation, and imaging results indicates that GPR at 500 MHz exhibits better detection performance for SFRC segments. The results of the numerical simulation were verified through model tests of real SFRC segments, demonstrating that 500 MHz has good penetration performance for SFRC segments. This paper also proposed a calculation method for equivalent relative dielectric constant (ERDC) based on the model test results and validated its rationality through the analysis of numerical simulation results and real GPR waveforms.

Transmission line fault cause identification method based on transient waveform image and MCNN-LSTM

Power system operators commonly employ fault recorders to record and analyze transmission line faults. However, the length of transmission lines, the number and location of power sources and the sampling frequency of fault recorder will influence the characteristics of the fault waveform leading to fault identification error. This paper proposed a multi-head convolutional neural network with long short-term memory (MCNN-LSTM) using transient waveform image as input for transmission line fault type and fault cause classification. First, the characteristics of transmission line faults were analyzed. Second, MCNN-LSTM is designed by considering interaction between electrical signal. Finally, the modified IEEE-39 node power network was adopted to test the proposed method. The test results show that: this method can improve the identification accuracy of transmission line faults even if there is class unbalance in fault sample set, and shows strong adaptability when transferred to small-size fault samples of transmission lines.

Detecting CAN overlapped voltage attacks with an improved voltage-based in-vehicle intrusion detection system

The in-vehicle voltage-based intrusion detection system (VIDS) can recognize the identity of the ECUs and detect the physical layer attacks that cannot be detected by the content-based or frequency-based IDS. However, recent research in VIDS has discovered an overlapped voltage attack that can bypass the existing VIDS by generating overlapped voltage signals from two compromised ECUs to distort the fingerprint of the target ECU. The existing solutions need to change the communication protocol or network topology to mitigate the attack, which may involve additional development costs or scheduling mechanisms to coordinate the tasks. In this paper, motivated by not changing the network topology and protocols, we propose an improved VIDS that can efficiently detect the overlapped voltage attack of the in-vehicle CAN network. Before retraining the VIDS classifier model, we add the mechanism for detecting and filtering overlapped signals in the voltage samples by applying the Long Short-Term Memory (LSTM) autoencoder network to preserve the fingerprint characteristics of the benign waveform in the original samples. First, we establish an LSTM autoencoder model with benign voltages collected in a usual scenario and then apply the automatic thresholding approach to obtain the threshold to separate the overlapped and benign voltages. Next, we filter the overlapped voltages and extract the fingerprint features from the filtered voltage samples to predict the classification of the masquerade CAN packet during the attack. Finally, we evaluate the performance and attack success rate of the proposed VIDS on a CAN bus testbench. The results show that the proposed VIDS can effectively detect the overlapped voltage attack and identify the compromised ECU mimicking the distorted voltage fingerprints with an accuracy of 99.4%. The attack success rate of the proposed VIDS drops to 0∼18%, which is about 60∼95% for the existing VIDS.

Rule-Based Non-Intrusive Load Monitoring Using Steady-State Current Waveform Features.

Monitoring electricity energy usage can help to reduce power consumption considerably. Among load monitoring techniques, non-intrusive load monitoring (NILM) provides a cost-efficient solution to identify individual load consumption details from the aggregate voltage and current measurements. Existing load monitoring techniques often require large datasets or use complex algorithms to obtain acceptable performance. In this paper, a NILM technique using six non-redundant current waveform features with rule-based set theory (CRuST) is proposed. The architecture consists of an event detection stage followed by preprocessing and framing of the current signal, feature extraction, and finally, the load identification stage. During the event detection stage, a change in connected loads is ascertained using current waveform features. Once an event is detected, the aggregate current is processed and framed to obtain the event-causing load current. From the obtained load current, the six features are extracted. Furthermore, the load identification stage determines the event-causing load, utilizing the features extracted and the appliance model. The results of the CRuST NILM are evaluated using performance metrics for different scenarios, and it is observed to provide more than 96% accuracy for all test cases. The CRuST NILM is also observed to have superior performance compared to the feed-forward back-propagation network model and a few other existing NILM techniques.

The current applications and potential advantages of the Neuropixels

Neuropixels technology has revolutionized neuroscience research by allowing scientists to obtain physiological firing data from single neurons in the central nervous system with unprecedented temporal and spatial resolution. While the technology is already highly advanced, researchers continue to work on improving it further. In this article, the authors summarize several current studies on improvements and applications of Neuropixels probes. This technology is used to capture the characteristic waveforms of neurons in different brain regions, record particular types of neurons, and acquire detailed data. The improvement of the Neuropixels is the AMIE, which solves the problem of implanting the device in unrestrained mice and recycling, enriching the electrodes and the recording sites on each Neuropixels device. By discussion and investigation, the current conduct and some refinements of the Neuropixels are demonstrated in this brief review. However, fragility and ethical problems are still inevitable obstacles of Neuropixels, and their potential harm to human beings still deserves attention.

Drilling-vibration response characteristics of rocks based on Hilbert–Huang transform

Currently, there are no in-situ methods to quantify drilling-vibration response characteristics of different lithologies. Here, we quantified the vibration responses of four lithologies (limestone, sandstone, coal, and mudstone) using a combination of theoretical deduction and numerical simulation. First, a drilling-vibration model of a particular rock was established, and the differential equations of motion and vibration responses to rock/drill bit interactions with the formation were derived. Next, finite-element simulation was adopted to simulate the rock/drill-bit interactions of the four lithologies. Finally, the Hilbert–Huang transform (HHT) was applied to extract characteristic waveforms, frequency bands, peak frequencies, and marginal spectra of vibration signals. Simulations revealed that the highest and lowest vibration responses were observed in limestone and mudstone, respectively. Vibration acceleration was proportional to the Young's modulus of rocks, whereas vibration displacement and velocity were inversely proportional to Young's modulus, compressive strength, and density. Based on the HHT results, among the four lithologies, vibration responses had the largest characteristic frequency segments and peak frequencies in limestone (28.1–34.6 kHz and 33.4 kHz, respectively), whereas those of mudstone were the smallest (15.6–21.3 kHz and 19.6 kHz, respectively). The results of this study provide a theoretical basis for establishing an identification method and expert knowledge database of lithology based on drilling-vibration responses.

Analyses of explosion earthquakes at Sinabung Volcano: characteristics of waveform, spectra, and energy

The characteristic of explosion earthquakes associated with lava avalanche at Sinabung volcano from October to December 2014 is investigated. The 1-component short-period seismometers which are Sukanalu (SKN), Laukawar (KWR), and Kebayaken (KBY) are used. All stations are located about 3-9 km away from the summit. The result shows the characteristics of these waveforms as follows; the amplitude increases rapidly to maximum for about 2-5 s, then the amplitude is almost constant for about 30-40 s followed by the gradual decrease of amplitude for about 40-50 s. Most of explosion earthquakes have a dominant frequency at about 1-4 Hz. The result also observed that the energy of explosion earthquake changes with lapse time and much stable for longer duration compared with explosion earthquake study at different volcano. Our result suggests that the explosion earthquake at Sinabung volcano may have a different waveform generation and/or mechanism to the general explosion earthquake at different volcano.

Exploration of Categorization and Category-Based Induction on Waste Sorting: A Follow-Up Observation by NeuroSky MindWave.

Waste sorting, as an embodiment of behavioral cognition, is regulated by two cognitive processes, namely, Categorization (C) and Category-Based Induction (CBI). This study employed the event-related potential (ERP) technique to assess whether there is a transformation between C and CBI in waste sorting cognition, in order to help individuals establish a correct waste sorting behavior. We reported a case of intervention in waste sorting education with a 58-year-old Chinese woman to discriminate whether such intervention facilitates the transition between C and CBI. The results showed that the waste sorting cognition follows a developmental model as C-CBI-C, where education may help the subject build a cognitive framework for waste sorting by altering inherent misperceptions and filling gaps in classification knowledge. The results also noticed that FN400 is identified as a characteristic waveform in the CBI process, by which it is indicated that the first 4 to 7 days of education is a critical period for establishing the cognitive framework. Through a comparison of the ERP waveforms at different stages of intervention, the results are insightful to help individuals improve their cognition of waste sorting.

Characterization of Excitation Effects and Data Interpretation of Combined Time-Domain Multiwaveform Transmission Currents

With the development of electronic technology, time-domain electromagnetic (TDEM) transmitters are now capable of emitting various waveforms such as trapezoidal, triangular, and semi-sinusoidal waveforms, as well as their combinations. However, the application of multi-waveform combination detection methods is still immature, and modeling methods are required to analyze the characteristics of different waveforms and complete data interpretation. Based on the Crank-Nicolson finite difference time-domain (CN-FDTD) method for discretizing the electromagnetic wave equations, the current waveform can be converted into current surface density for source-included calculations, enabling high-precision modeling of the responses to different current waveforms. The responses to multiple transmission current waveforms were compared and analyzed using various models, and the results showed that selecting targeted excitation parameters based on the characteristics of the target can improve detection sensitivity. We applied the multi-waveform combination detection method to the actual exploration in the field, obtained the pure secondary field response by subtracting the primary field of the actual current waveform from the measured data, converted the responses of different transmission current waveforms into step wave responses by deconvolution method, and performed resistivity imaging. The results of modeling and data interpretation demonstrate the effectiveness of the multi-waveform combination detection method in improving geological exploration accuracy, and promoted the development of fine-scale geophysical exploration.

A Single-Ended Fault Location Method for Transmission Line Based on Full Waveform Features Extractions of Traveling Waves

Conventional single-ended traveling wave fault location methods, which commonly depend on partial features of traveling wavefronts, may lead to fault location failure, especially for weak-signal faults such as high impedance or zero-crossing faults and close-in faults. To tackle it, this paper presents a fault location method according to the extracted panoramic features of traveling wave full waveform (TWFW) in time-frequency domains. Firstly, two rules of the TWFW features are probed, that is, the wavefront arrival sequence is varied from fault sections, as well as the frequency distribution of wavefronts is strongly related to the precise fault distance. Ergo, the mapping relationships between the TWFW and fault distance are qualitatively confirmed in order to demonstrate the uniqueness of the TWFW subsequently. Next, a LeNet-5-based convolution neural network (CNN) model is constructed to quantitatively evaluate such mapping relationships. In this model, various TWFW features will be extracted to the convolution channels when the CNN parameters are optimized adaptively, and the mapping relations can be formed in light of these sensitive features to estimate the fault distance. Finally, a Grad-CAM visualization method is deployed in the case study, and the accuracy along with the robustness of the proposed method in various fault conditions can be validated consequently.

Sympathetic Transformer Inrush Detection and Relay Blocking During Power System Restoration

A classic inrush current occurs during normal energization of reactive grid elements like transformers. It can simulate short-circuit currents. Hence inrush currents have to be detected and protection relays need to be blocked for avoiding their overfunction. Conventional detection criteria are the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{\textrm {nd}}$</tex-math></inline-formula> harmonic content and some kind of current waveform analysis. A classic inrush can evolve into a sympathetic inrush event, if the energization of a single transformers drives other transformers into magnetic saturation. The risk of facing a sympathetic inrush current is particularly high during a power system restoration process. Its characteristic waveform differs from the classic inrush. While the classic inrush has unipolar current peaks, the sympathetic inrush is characterized by bipolar peaks. The conventional detection criteria tend to fail in this case. This paper proposes a new approach to detect sympathetic transformer inrush currents. The approach includes the detection of classic inrush currents as well as the distinction against short-circuit currents for ensuring the relay blocking. First it introduces the basic physical mechanism behind the evolution of a classic to a sympathetic inrush. The higher risk of facing a sympathetic inrush during the restoration process is shown by the physical mechanism as well. The proposed detection method is then introduced in detail. Three new differentiation criteria will be applied and merged together to a final decision. The paper closes with practical lab tests of the new approach on simulated sympathetic and measured classic inrush currents. All lab tests showed good results and promise a practical implementation in the grid.

Experimental Study on the Hydrodynamic Characteristics of a Fixed Comb-Type Floating Breakwater

A comb-type floating breakwater is a new wave dissipation structure with particular force and dissipation performance advantages due to the two wave-reflecting surfaces. In this article, physical model experiments are used to study the hydrodynamic characteristics of a fixed floating comb breakwater and two structural optimization-based measures under the combined action of regular waves, irregular waves, and wave currents. The effects of factors such as the relative width, relative wave height, water flow velocity, and irregular waves on the transmission coefficient of the breakwater are analyzed. In addition, the characteristics of the transmission wave waveform are analyzed based on the time and frequency domains. The results show that (1) the wave transmission coefficient of a comb-type floating breakwater is lower than that of a rectangular floating box for long-period waves, while the transmission coefficient is larger than that of a rectangular floating box for short-period waves. (2) Under combined current and waves, the superimposition of bidirectional currents can increase the transmission coefficient, and the transmission coefficient increases with increasing current speed. The superimposition of the anti-directional current can decrease the transmission coefficient. (3) Moreover, with the same wave parameters, the transmission coefficient for irregular waves is larger than that of regular waves. (4) Finally, extending the bottom plate and adding lower baffles can effectively enhance the wave dissipation effect of the comb-type floating breakwater while also stabilizing the transmitted wave waveform.

Evaluation of the effectiveness of four alternative approaches for the classical cough test during a urodynamic study in the context of the COVID-19 global pandemic

BackgroudTo evaluate four different alternatives to the classical cough test during a urodynamic study in the context of the COVID-19 pandemic.MethodsPatients who needed to undergo a urodynamic study (UDS) at the West China Hospital of Sichuan University between April 2021 and May 2021 were randomly selected according to the inclusion and exclusion criteria. During the UDS process, we used four alternative methods to the “cough test”: 1) quickly pressing the bladder area, 2) performing the Valsalva maneuver, 3) performing the Kegel maneuver, and 4) letting the patient close their mouth while performing the cough test. The "cough" waveform amplitudes and characteristics of the graphics were obtained and compared with the classical cough test.ResultsA total of 120 patients (89 men, 31 women) were included in the study. There was no significant difference between the cough waveform amplitude induced by the Valsalva maneuver compared with the classical cough test (P = 0.182); there was no significant difference between the cough waveform amplitude induced by the cough test with the mouth closed and the classical cough test (P = 0.342); there was no significant difference between pressing quickly on the bladder area and the classical method (P = 0.076); and there was a significant difference between the data obtained by the Kegel maneuver and the classical method (P < 0.05). The average "cough" amplitudes obtained were 73.14 ± 22.48 cm H2O, 66.17 ± 17.12 cm H2O, 82.93 ± 18.95 cm H2O, 26.50 ± 8.68 cm H2O, and 68.90 ± 20.32 cm H2O by the classical cough test, by quickly pressing the bladder area, by the Valsalva maneuver, by the Kegel maneuver, and by coughing with a closed mouth, respectively.ConclusionQuickly pressing the bladder area, performing the Valsalva maneuver, and letting the patient close their mouth while performing the cough test can all provide effective cough waveforms and amplitudes.Trial registrationNo. 2021–183.

Filtering-induced changes of pulse transmit time across different ages: a neglected concern in photoplethysmography-based cuffless blood pressure measurement.

Background: Pulse transit time (PTT) is a key parameter in cuffless blood pressure measurement based on photoplethysmography (PPG) signals. In wearable PPG sensors, raw PPG signals are filtered, which can change the timing of PPG waveform feature points, leading to inaccurate PTT estimation. There is a lack of comprehensive investigation of filtering-induced PTT changes in subjects with different ages. Objective: This study aimed to quantitatively investigate the effects of aging and PTT definition on the infinite impulse response (IIR) filtering-induced PTT changes. Methods: One hundred healthy subjects in five different ranges of age (i.e., 20-29, 30-39, 40-49, 50-59, and over 60years old, 20 subjects in each) were recruited. Electrocardiogram (ECG) and PPG signals were recorded simultaneously for 120s. PTT was calculated from the R wave of ECG and PPG waveform features. Eight PTT definitions were developed from different PPG waveform feature points. The raw PPG signals were preprocessed then further low-pass filtered. The difference between PTTs derived from preprocessed and filtered PPG signals, and the relative difference, were calculated and compared among five age groups and eight PTT definitions using the analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. Linear regression analysis was used to investigate the relationship between age and filtering-induced PTT changes. Results: Filtering-induced PTT difference and the relative difference were significantly influenced by age and PTT definition (p < 0.001 for both). Aging effect on filtering-induced PTT changes was consecutive with a monotonous trend under all PTT definitions. The age groups with maximum and minimum filtering-induced PTT changes depended on the definition. In all subjects, the PTT defined by maximum peak of PPG had the minimum filtering-induced PTT changes (mean: 16.16ms and 5.65% for PTT difference and relative difference). The changes of PTT defined by maximum first PPG derivative had the strongest linear relationship with age (R-squared: 0.47 and 0.46 for PTT difference relative difference). Conclusion: The filtering-induced PTT changes are significantly influenced by age and PTT definition. These factors deserve further consideration to improve the accuracy of PPG-based cuffless blood pressure measurement using wearable sensors.

Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation.

Duplex ultrasound (DUS) is the most widely used method for surveillance of arteriovenous fistulae (AVF) created for dialysis. However, DUS is poor at predicting AVF outcomes and there is a need for novel methods that can more accurately evaluate multidirectional AVF flow. In this study we aimed to evaluate the feasibility of detecting AVF stenosis using a novel method combining tensor-decomposition of B-mode ultrasound cine loops (videos) of blood flow and machine learning classification. Classification of stenosis was based on the DUS assessment of blood flow volume, vessel diameter size, flow velocity, and spectral waveform features. Real-time B-mode cine loops of the arterial inflow, anastomosis, and venous outflow of the AVFs were analysed. Tensor decompositions were computed from both the 'full-frame' (whole-image) videos and 'cropped' videos (to include areas of blood flow only). The resulting output were labelled for the presence of stenosis, as per the DUS findings, and used as a set of features for classification using a Long Short-Term Memory (LSTM) neural network. A total of 61 out of 66 available videos were used for analysis. The whole-image classifier failed to beat random guessing, achieving a mean area under the receiver operating characteristics (AUROC) value of 0.49 (CI 0.48 to 0.50). In contrast, the 'cropped' video classifier performed better with a mean AUROC of 0.82 (CI 0.66 to 0.96), showing promising predictive power despite the small size of the dataset. The combined application of tensor decomposition and machine learning are promising for the detection of AVF stenosis and warrant further investigation.

From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function.

Aortic (central) pressure features are associated with cardiovascular complications and can be algorithmically derived from non-invasive peripheral arterial waveforms. This has conventionally been performed with a pressure waveform (i.e., tonometry or oscillometry) rather than with the optical-based sensor (photoplethysmography (PPG)) that is predominantly used in wearable health devices. Extraction of aortic features from a peripheral PPG waveform has yet to be investigated. This study aims to compare aortic features extracted from peripheral arterial waveforms acquired with different sensor modalities using the same transfer function. Radial tonometry (reference), finger volume-clamped PPG (Peňáz) and fingertip PPG waveforms were measured in participants (n=29, 36±16 years, 15 female) under baseline conditions. Waveforms were converted into an aortic pressure waveform using the transfer function. Waveform features were extracted from the converted waveform. Extracted features were compared with correlation plots and a Bland-Altman analysis. Aortic pressure features extracted from a finger using the Peňáz technique were comparable to radial tonometry derived features. Aortic features extracted from a fingertip waveform were more variable in comparison to radial tonometry-derived features. Aortic (central) pressure waveform features contain valuable haemodynamic information and have the capacity to be easily and conveniently implemented in wearable health devices. Future use of these features in wearable health devices incorporating PPG requires the development, and/or, optimization of a unique transfer function to more accurately represent the aortic pressure waveform for cardiovascular assessment.Clinical Relevance- Aortic pressure features might be used in wearable health devices following the development of a unique transfer function for optical-transduced peripheral vascular signals.

Observation of induced voltage on 10 kV overhead distribution lines with and without shield wire by rocket triggered lightning

This article studies the performance of lightning-induced overvoltage (LIV) on a 1513 m overhead distribution line in the absence or presence of shield wire, via rocket-triggered lightning experiment. It's found that whether the shield wire is erected or not has little influence on the waveform characteristics of LIVs, but mainly affects the voltage amplitude. The shield wire primarily reduces the positive peak value of LIVs at the station closest to the lightning channel, but has little influence on the negative peak value. On the contrary, for the station farthest from the lightning channel, the shield wire mainly reduces the negative peak value of LIVs, but has little impact on the positive peak value. By using the ratio of the average values of the statistical data under the operating condition with or without shield wire, it's found that for peak-to-peak value measured at the closest station, the closer to the shield wire, the smaller the ratio is. The ratio of phase A is about 0.78, while the ratio of phase C is about 0.88. For the station farthest from the lightning channel, the ratio is about 0.95, and there is no obvious difference between three phases.