Peak Search Algorithm Research Articles

Design of health information management model for elderly care using an advanced higher-order hybrid clustering algorithm from the perspective of sports and medicine integration.

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population.

A fault diagnosis method for variable speed gearbox bearing based on SET improved multi-source ridge line

Due to the noise interference and frequency ambiguity of gearbox vibration signal under variable speed, the traditional instantaneous frequency estimation method is prone to frequency mutation and low ridge line fusion accuracy. This paper proposes an improved method of extracting multi-source time–frequency ridge lines based on time–frequency analysis by exploring the relationship between the vibration signal of the gearbox without a tachometer and the fluctuation law of the bearing rotation frequency. In the proposed method, the Variable Mode Decomposition (VMD) filter and Synchroextracting Transform (SET) are first used to analyze the time frequency of the gearbox vibration signal. Secondly, the local peak search algorithm obtains multiple time–frequency ridge lines from low-frequency and high-frequency resonance frequency bands. Thirdly, the synchronization process is carried out based on the maximum energy spectrum in the time spectrum. The multi-source ridge line extraction algorithm is improved with reduced frequency mutation and optimized ridge line fusion accuracy. Finally, the optimized speed curve is used to track the vibration signal order of the gearbox. The bearing fault characteristic order is obtained from the angular order spectrum, and the effectiveness of the proposed method is verified by the simulation and the experiment.

A Tacholess Order Tracking Method Based on Spectral Amplitude Modulation for Variable Speed Bearing Fault Diagnosis

Rolling bearings often work under nonstationary conditions, which will lead to spectrum smearing phenomenon and the failure of traditional diagnostic methods. Order tracking is a common technique to alleviate this problem by resampling the time-domain signal using the instantaneous rotation frequency. For tacholess applications, it is of great importance to obtain accurate rotation speeds. In this paper, a tacholess order tracking method based on spectral amplitude modulation (SAM) is proposed. Based on the fact that SAM can separate frequency components with different levels of energy, an appropriate weight is selected to highlight the instantaneous frequency, which is then extracted from the time-frequency representation by peak search algorithm. Moreover, entropy is employed to evaluate the valuable information in the time-frequency representation in order to select an optimal weight adaptively. A simulated signal and experimental signals of bearing outer and inner ring faults demonstrate that the proposed method can extract instantaneous rotation frequency and detect nonstationary bearing fault characteristics effectively.

Flower pollination global peak search algorithm for partially shaded solar photovoltaic system

Photovoltaic (PV) systems are required to function at their maximum power point (MPP) to fully utilize solar energy. Solar PV panels are configured with maximum power point tracking (MPPT) systems to enhance the generation and supply of the maximum available power. However, owing to external environmental factors and partial shading conditions (PSCs), the tracking process becomes complex. Conventional MPPT methods cannot always achieve the global MPP. Thus, an advanced metaheuristic MPPT scheme for PV systems, the flower pollination algorithm (FPA), is implemented in this study to find the best global duty cycle to obtain the maximum power output, and a Levy flight is utilized to enhance FPA convergence. The FPA MPPT model was established, and its performance was compared with the conventional, and some meta-heuristic-based MPPT techniques; perturb and observe (PO), incremental conductance (INC), particle swarm optimization (PSO), cuckoo search (CS), grey wolf optimization (GWO), genetic algorithm (GA), double integral sliding mode control (DISMC), and hill-climbing (HC). Different scenarios were considered for the simulation: partial shading, complex partial shading, long-width, and long-length partial shading, and different temperature conditions employing series and series-parallel PV array configurations. In addition, real-world field atmospheric irradiance data for Daejeon city is used for the study. In terms of efficiency, the FPA attains at most 83%–96% of the expected GMP from the PV array as compared with the other MPPT techniques in all scenarios considered. The adopted FPA obtained the following root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) values respectively for each Scenario as follows: Scenario I: RMSE: 0.0061, MRE: 0.0463, MAE: 6.0745e-06, Scenario II: RMSE: 0.0116, MRE: 0.0430, MAE: 1.0738e-05, Scenario III: RMSE: 0.0640, MRE: 0.206, MAE: 4.8239e-05, Scenario IV (varying temperature conditions): RMSE: 0.0062–0.0074, MRE: 0.0460–0.0548, MAE: 6.1333e-06 – 7.4922e-06. A unique sensitivity performance comparison analysis is done employing the power spectral analysis to validate the effectiveness of the adopted FPA to the other MPPT techniques. The FPA performs better than all the techniques in all scenarios in comparing their maximum power tracked, convergence speed and accuracy, system efficiency, and optimum statistical results attained.

In-Cabin MIMO Radar System for Human Dysfunctional Breathing Detection

The integration of wireless sensors into the automotive industry is making vehicles increasingly safe and autonomous. At present, vehicles are equipped with external sensors that facilitate maneuverability and braking and internal sensors that detect presence, seat belt state, and occupant vital signs. Early detection of dysfunctional breathing patterns can reduce the risk of accidents caused by drowsiness or fatigue. In this regard, this work presents an MIMO FMCW Radar System able to distinguish dysfunctional breathing patterns from human torso motion. The torso has been divided into six regions, and the amplitude of the elongation and the frequency of motion of each region have been measured during breathing. An elongation map is then constructed in order to graphically show the dysfunctional breathing patterns. The signal processing includes a peak search algorithm to detect elongation amplitude and a bandpass frequency filter to minimize dc components, random driver motion, and vehicle vibration. A torso elongation emulator phantom has been assembled with materials of skin-like relative permittivity and dc motors for calibration and validation of basic breathing patterns. Finally, the signal of the system is compared with that of a commercial respiration belt, and then the system is tested by performing measurements on people. The MIMO radar system is able to measure, differentiate, and classify patterns associated with dysfunctional breathing such as hyperventilation syndrome and thoracic dominant breathing.

An Efficient CS-Based Spectral Peak Search Method.

Spectral peak search is an essential part of the frequency domain parametric method. In this paper, a spectral peak search algorithm employing the principle of compressed sensing (CS) is proposed to rapidly estimate the spectral peaks. The algorithm adopts fast Fourier transform (FFT) with a few points to obtain the coarsely estimated spectral peak positions, and then only three small-scale inner products are iteratively calculated by increasing the input sequence length to rapidly refine the estimated positions. Compared with the conventional methods, this algorithm can directly capture the exact locations of spectral peaks without acquiring the entire spectrum. In addition, the proposed algorithm can be easily integrated into the existing frequency domain interpolation methods to accurately determine the spectral peak positions, and if so, only 30% of inner product operations of the original algorithms are required. Theoretical analysis and numerical results show that this algorithm yields accurate results with low complexity for analyzing both one-dimensional and two-dimensional signals.

A 3D Printing Triboelectric Sensor for Gait Analysis and Virtual Control Based on Human–Computer Interaction and the Internet of Things

Gait is the information that can reflect the state index of the human body, and at the same time, the leg is the organ with the maximum output power of the human body. Effective collection of maximum mechanical power output and gait information can play an important role in sustainable energy acquisition and human health monitoring. In this paper, a 3D printing triboelectric nanogenerator (3D printed TENG) is fabricated by 3D printing technology, it is composited of Poly tetra fluoroethylene (PTFE) film, Nylon film, and 3D printing substrate. Based on the principle of friction electrification and electrostatic induction, it can be used as the equipment for human sustainable mechanical energy collection and gait monitoring. In order to solve the problems of energy collection, gait monitoring, and immersion experience, we conducted the following experiments. Firstly, the problem of sustainable energy recovery and reuse of the human body was solved. Three-dimensionally printed TENG was used to collect human mechanical energy and convert it into electric energy. The capacitor of 2 μF can be charged to 1.92 V in 20 s. Therefore, 3D printed TENG can be used as a miniature sustainable power supply for microelectronic devices. Then, the gait monitoring software is used to monitor human gait, including the number of steps, the frequency of steps, and the establishment of a personal gait password. This gait password can only identify a specific individual through machine learning. Through remote wireless transmission means, remote real-time information monitoring can be achieved. Finally, we use the Internet of Things to control virtual games through electrical signals and achieve the effect of human–computer interaction. The peak search algorithm is mainly used to detect the extreme points whose amplitude is greater than a certain threshold and the distance is more than 0.1 s. Therefore, this study proposed a 3D printed TENG method to collect human mechanical energy, monitor gait information, and then conduct human–computer interaction, which opened up a multi-dimensional channel for human energy and information interaction.

Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions

The rotational frequency (RF) is an important information for multi-fault features detection of rolling bearing under varying speed conditions. In the traditional methods, such as the computed order analysis (COA) and the time–frequency analysis (TFA), the RF should be measured using an encoder or extracted by a complex algorithm, which bring challenge to bearing fault diagnosis. In order to address this issue, a novel iterative generalized demodulation (IGD) based method guided by the instantaneous fault characteristic frequency (IFCF) extraction and enhanced instantaneous rotational frequency (IRF) matching is proposed in this paper. Specifically, the resonance frequency band excited by bearing fault is first obtained by the band-pass filter, and its envelope time–frequency​ representation (TFR) is calculated using the Hilbert transform and the short-time Fourier transform (STFT). Second, the IFCF is extracted using the harmonic summation-based peak search algorithm from the envelope TFR. Third, the time-varying RF ridge is transformed into a line paralleling to the time axis using the IGD with the phase function (PF). The PF is calculated by the IFCF function and fault characteristic coefficient (FCC). Lastly, the iterative generalized demodulation spectrum (IGDS) is obtained using the fast Fourier transform (FFT) for identifying fault type corresponding to the extracted IFCF. Based on obtained fault type and FCC ratios, new PFs and frequency points (FPs) are calculated for detecting other faults. Both simulated and experimental results validate that multi-fault features of rolling bearing under time-varying rotational speeds can be effectively identified without RF measurement and extraction.

Fiber Bragg grating sensor-based temperature monitoring of solar photovoltaic panels using machine learning algorithms

Fiber Bragg Grating (FBG) sensors are an emerging and prominent optical sensing technology of accurately measuring strain, depth, temperature, density, and several physical parameters. Due to high solar radiation, the increased solar panel temperature affects photovoltaic cell efficiency. Hence, monitoring the temperature of solar panels and providing proper cooling is essential to attain optimal electrical performance. FBG sensor is used to monitor the solar panel temperature in this research. The accuracy and stability of the peak search algorithms in the acquired experimental data are analyzed. Reisz fractional-order derivative and Savitsky-Golay filter are improved using a decision tree regressor to determine the peak and noisy spectrum with different signal-to-noise ratios. The algorithm surpassed the demerits of conventional peak algorithms used for the FBG spectrum. This work implements the fractional derivatives and a machine learning algorithm initially denoised. The number of epochs for linear regression is 15. The depth allowed for decision trees is 1000, and the number of estimators for the random forest is 150. A peak is detected with a decision tree regressor trained on clean and noiseless FBG data. Machine learning algorithms such as linear regression, random forest, and decision tree are compared. The decision tree regressor with hyperparameter tuning yields the best results with high accuracy of 99.83% compared to other conventional peak detection methods. The statistical tests and metaheuristic algorithms are also performed to assess the experimental outcome.

Improvements of portable energy dispersive X‐ray fluorescence instrument: Resolution withSilicon Drift Detector, measurements stability using pyroelectric sources, and adaptation for space use

AbstractAn energy dispersive X‐ray fluorescence instrument has been developed to be used for space exploration. In particular, the electronic readout has been redesigned to withstand the radiation environment of space and improve the energy resolution down to 121.17 eV using an Silicon Drift Detector (SDD). Besides, the instrument uses pyroelectric sources as excitation sources. We include a renormalization to reduce measurement fluctuation from 25.2% to 3.7%. A peak search algorithm has been implemented based on calibration to improve accuracy and reduce possible aging drift effects.

Effect of Peak Tracking Methods on FBG Calibration Derived by Factorial Design of Experiment.

We present a calibration procedure for a humidity sensor made of a fiber Bragg grating covered by a polyimide layer. FBGs being intrinsically sensitive to temperature and strain, the calibration should tackle three variables, and, therefore, consists of a three-variable, two-level factorial design tailored to assess the three main sensitivities, as well as the five cross-sensitivities. FBG sensing information is encoded in the reflection spectrum from which the Bragg wavelength should be extracted. We tested six classical peak tracking methods on the results of the factorial design of the experiment applied to a homemade FBG humidity sensor. We used Python programming to compute, from the raw spectral data with six typical peak search algorithms, the temperature, strain and humidity sensitivities, as well as the cross-sensitivities, and showed that results are consistent for all algorithms, provided that the points selected to make the computation are correctly chosen. The best results for this particular sensor are obtained with a 3 threshold, whatever the peak search method used, and allow to compute the effective humidity sensitivity taking into account the combined effect of temperature and strain. The calibration procedure presented here is nevertheless generic and can thus be adapted to other sensors.

An infrared dim target detection algorithm based on density peak search and region consistency

To suppress background clutter and improve detection accuracy, we propose a dim target detection algorithm based on density peak search and region consistency. A density peak search algorithm is first applied to extract candidate targets, and these are then classified and marked according to the local mosaic probability factor, which is important in order to suppress the backgroundsssss clutter and accurately strip the candidate target region from the background. Based on the regional stability of the dim targets, local mosaic gradient factors are used to screen real targets from candidates, and a facet kernel filter is used to extract the irregular contours of dim targets with the aim of enhancing them. Our experimental results show that compared with existing algorithms, the proposed method has better detection accuracy and robustness in various complex scenarios.

A parameter-adaptive ACMD method based on particle swarm optimization algorithm for rolling bearing fault diagnosis under variable speed

Fault diagnosis for rolling bearing under variable speed is always a challenging topic since the vibration signal has time-varying characteristics. To overcome this difficulty, a novel method is exploited based on particle swarm optimization (PSO) and adaptive chirp mode decomposition (ACMD), named parameter-adaptive ACMD. Firstly, fast spectral kurtosis algorithm is used to get the resonance band signal. Then, the parameter-adaptive ACMD method decomposes the envelope signal to obtain the time-frequency spectrum. Next, the proposed method of fault diagnosis uses peak search algorithm to estimate instantaneous rotational frequency from the time-frequency graph processed. Finally, the measured rotational frequency is used as the phase function in the resampling process to get the order spectrum and fault characteristic order (FCO). Simulation and actual signals were analyzed and the results indicate that the proposed method can identify fault type with variable speed and has high application value.

Multiple Target Detection For OFDM Radar Based On Convolutional Neural Network

The objective of this paper is to propose a multiple target identification technique for orthogonal frequency division multiplexing (OFDM) radars. First, a 2-D (range & Doppler) periodogram is obtained from the reflected signal through 2-D fast Fourier transform (FFT) of the received OFDM symbols. Usually, the peaks of the periodogram indicates the targets. Conventionally, peak search algorithms are used to find the multiple targets. In this paper, however, a convolutional neural network (CNN) classifier is proposed to identify the targets. The proposed technique does not need any additional information but the 2-D periodogram while the conventional method requires the noise variance as well as the periodogram. The performance is examined through computer simulation. According to the results, if the number of maximum identifiable targets are small, the proposed technique performs well. However, as the number increases, the detection accuracy decreases. In the simulation environments, the proposed method outperforms the conventional one. The proposed OFDM radar technique can be applied to 6G mobile communications to identify the moving targets around the transmitter without additional frequency resource for radar systems.

Optimized Real-Time MUSIC Algorithm With CPU-GPU Architecture

Direction-of-arrival (DOA) estimation algorithm for uniform planar arrays has been applied in many fields. The multiple signal classification (MUSIC) algorithm has obvious advantage in high-resolution signal source estimation scenarios. However, the MUSIC algorithm has high computational costs, therefore it is hard to be used in real-time scenes. Many studies are dedicated to accelerating MUSIC algorithm by parallel hardware, especially by Graphics Processing Units (GPU). MUSIC algorithm based on Central Processing Unit (CPU) -GPU architecture acceleration is rarely investigated in previous literatures, and how well MUSIC Algorithm with CPU-GPU architecture could perform remains unknown. In this paper, we present and evaluate a model of search parallel MUSIC algorithm with CPU-GPU architecture. In the proposed model, the steering vector of each candidate incident signal and the corresponding value of 2D spatial pseudo-spectrum (SPS) function are sequentially calculated in a single core of the GPU, and the subsequent calculation of each elevation or azimuth is parallel in batches. Furthermore, in order to improve the peak search speed, we propose a new Coarse and Fine Traversal (CFT) peak search algorithm via CPU and a new parallel peak search algorithm based on GPU acceleration. Across strategy comparison, utilizing CPU-GPU architecture for processing, a 150-160x performance gain is achieved compared to using CPU only. Besides, the resolution of uniform planar arrays is also analyzed.

Under the Processing of Network Data Images, the Position Recognition Technology of Mechanical Parts Based on "Mathematical Modeling" is Studied

With the development of the times and the progress of society, science and technology has been developing more and more rapidly. In this regard, modern society has been composed of science and technology. Compared with the original steam engine, Jenny textile machine and other industrial era starters, now the technology has entered the era of large machines. As a result, the processing of mechanical parts is becoming more and more complex than before. Therefore, the purpose of this paper is to use the network data image processing based on mathematical modeling of mechanical parts processing location recognition technology. After consulting the relevant literature on mechanical parts processing and location recognition technology, this paper goes to the factory to observe the mechanical working principle and mechanical working process, and then uses the two-dimensional spectral peak search algorithm to design the position recognition technology in the process of mechanical parts processing to process mechanical parts based on mathematical modeling. The experimental results show that the two-dimensional spectral peak search algorithm can make the system better point out the relevant problems in the process of mechanical parts processing and solve them intelligently, and if there are problems that cannot be solved, it will be fed back to the system informer in time to check. Finally, the accuracy and correctness of mechanical parts processing are improved, and some improvements are put forward for mechanical parts processing, which is of great help to mechanical parts processing.

Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar.

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.

Gamma peak search and peak fitting algorithm for a low-resolution detector with applications in gamma spectroscopy

A Savitzky–Golay filtering for smoothing and peak search written in Python is presented in this paper alongside its applications in the list-mode digital data acquisition dual gamma–gamma coincidence bismuth germanate (BGO) detector. The study has demonstrated that the software provides a reliable and effective way to quantify trace amounts of 22Na and 7Be in aerosol samples collected at Resolute Bay, Canada with a critical limit of 3 mBq and 5 Bq respectively for a 20 h counting interval, which are believed to be the inherent limitations of the dual-BGO system.

An artificial intelligent approach for the optimization of organic rankine cycle power generation systems

<span>The study on Organic Rankine Cycle (ORC) power generation system has gained significant popularity among researchers over the past decade, mainly due to the financial and environmental benefits that the system provides. A good maximum power point tracking (MPPT) mechanism can push the efficiency of an ORC to a higher rate. In this research, a Self-Adjusted Peak Search algorithm (SAPS) is proposed as the MPPT scheme of an ORC system. The SAPS has the ability to perform a relatively detailed search when the convergence reaches the near-optima peak without jeopardizing the speed of the overall convergence process. The SAPS is tested in a simulation to track for a moving maximum power pint (MPP) of an ORC system. Experiment results show that the SAPS outperformed several other well-established optimization algorithm in tracking the moving MPP, especially in term of the solution accuracies. It can thus be concluded that the proposed SAPS performs well as a mean of an MPPT scheme in an ORC system</span>