Search

The nonferrous metal mines in China are rapidly depleting due to years of mining, and it has become difficult to identify new mineral resources in the periphery of the old mining area. In order to deal with this situation, advanced technologies and equipment must be deployed. The borehole transient electromagnetic method (TEM) has become a key technology due to its deep investigative capabilities within conductive geological structures. In the present study, in order to meet the exploration needs at depths of less than 3000 m, surface-borehole TEM exploration was used to analyze the characteristics of electromagnetic signals generated by a long wire source and a large loop source, providing essential data for the development of key technologies, such as sensor parameter design and signal gain optimization of the TEM system in the borehole. This study discussed in detail two key technical problems as follows: firstly, the efficient synchronization mechanism between the ground transmitter system and the borehole electromagnetic signal acquisition system ensured the accuracy and timeliness of data acquisition; and secondly, the realization of mass storage technology, which effectively solved the problem of mass storage and real-time transmission of data in a deep borehole environment. The effectiveness of the surface-borehole TEM systems with a long wire source and a large loop source was verified by tests in real mines. The surface-borehole electromagnetic signal acquisition system developed in this study effectively collected electromagnetic signals in the borehole, and the results accurately reflected the stratigraphic information of mineral resources in the study area. This study can pave a new technical path for the exploration of deep and peripheral areas of non-ferrous metal mines and provide valuable experience and insights for mineral resource exploration in similarly complex geological environments. [ABSTRACT FROM AUTHOR]

The nonferrous metal mines in China are rapidly depleting due to years of mining, and it has become difficult to identify new mineral resources in the periphery of the old mining area. In order to deal with this situation, advanced technologies and equipment must be deployed. The borehole transient electromagnetic method (TEM) has become a key technology due to its deep investigative capabilities within conductive geological structures. In the present study, in order to meet the exploration needs at depths of less than 3000 m, surface-borehole TEM exploration was used to analyze the characteristics of electromagnetic signals generated by a long wire source and a large loop source, providing essential data for the development of key technologies, such as sensor parameter design and signal gain optimization of the TEM system in the borehole. This study discussed in detail two key technical problems as follows: firstly, the efficient synchronization mechanism between the ground transmitter system and the borehole electromagnetic signal acquisition system ensured the accuracy and timeliness of data acquisition; and secondly, the realization of mass storage technology, which effectively solved the problem of mass storage and real-time transmission of data in a deep borehole environment. The effectiveness of the surface-borehole TEM systems with a long wire source and a large loop source was verified by tests in real mines. The surface-borehole electromagnetic signal acquisition system developed in this study effectively collected electromagnetic signals in the borehole, and the results accurately reflected the stratigraphic information of mineral resources in the study area. This study can pave a new technical path for the exploration of deep and peripheral areas of non-ferrous metal mines and provide valuable experience and insights for mineral resource exploration in similarly complex geological environments.

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment. [ABSTRACT FROM AUTHOR]

Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy. Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkagedisequilibrium spectrum is explored and used to search for high-linkagedisequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak singlenucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms. Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively. Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics. [ABSTRACT FROM AUTHOR]

There are rich geothermal resources in China and they are widely distributed. After years of continuous exploration and production, most of the shallow geothermal resources have been explored and the current exploration mainly focuses on deep ones. Among a great many geophysical exploration methods, seismic survey is the most effective means of geothermal resource exploration and production, but its weak seismic reflection signal, low S/N ratio and poor imaging of thermal reservoirs due to the seismic geological conditions restrict the production and utilization of deep geothermal resources. It considers through the analysis of the geophysical characteristics of the thermal reservoir that the main causes for the weak seismic reflection signals from deep thermal reservoirs are (1) the shielding effect of the strong wave impedance interface between the thermal reservoir and the caprocks; (2) the small reflection coefficient inside the thermal reservoir; and (3) the serious absorption and attenuation of high-temperature fluids such as hydrothermal fluids and steam in the thermal reservoirs by seismic waves. Accordingly, a series of seismic data acquisition technologies are proposed based on the high-precision vibroseis low-frequency shooting, high-sensitivity geophones long-spread receiving and small bin size highfold technologies, and their application in the seismic acquisition of dry-hot rocks (HDR) in Gonghe Basin, Qinghai, shows a very good application effect. [ABSTRACT FROM AUTHOR]

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar. [ABSTRACT FROM AUTHOR]

Rolling bearing health status monitoring is essential for identifying early failures and avoiding un-planned downtime in industrial systems. To overcome the problems of existing entropy methods with multiple faults that are easily confounded at different scales, a weak signal feature extraction method based on refined composite multi-scale weighted entropy is proposed in this paper. The time–frequency domain features are constituted into a multi-dimensional original fault feature set, and the feature sensitivity is evaluated in terms of four feature evaluation criteria, in order to filter out a sensitive feature subset. Three types of refined composite multi-scale entropy are combined with sensitive feature parameters, in a weighted manner, through the use of the Hadamard product operation. The effects of different combinations of feature parameters on the refined composite multi-scale entropy are analyzed through experimental validation. According to the analysis of the experimental data from two test stations, the fault recognition rate reached 100% and 92.22%, respectively, based on the RCMWE method, starting from the first features. The results indicate that the proposed method can identify bearing fault types under different damage states at any scale, with the fault recognition rate being more stable than that of other methods. The proposed method can effectively distinguish rolling bearing health and fault states, providing higher classification accuracy for rolling bearing fault types and fault damage degrees. This puts forward a new idea for rolling bearing health state assessment, which has high engineering application value. [ABSTRACT FROM AUTHOR]

Weak signals exist in communication network channels, whose amplitude is small or small, and are easy to be annihilated by strong background noise. Therefore, a multi-channel weak signal detection and synchronous acquisition algorithm is designed in this paper. Firstly Krylov subspace methods solving the weight vector of adaptive matched filter, nonlinear SR and build parameter adjustable device, detection of multi-channel weak signals of different frequency, which under the normal state of multi-channel weak signal data samples and the length of the clustering center, to build the corresponding multi-channel weak signal model. Based on this, multi-channel synchronous acquisition is realized by using a high-speed interface specially built in FPGA. All CONVST_A and CONVST_B can be simultaneously raised to realize synchronous sampling of all channels. In order to avoid the data acquisition error caused by internal state factors, the distance variance of two groups of weak signals in the target network is calculated to complete the low error synchronous acquisition. Experimental results show that the power output state of the algorithm is obviously stable, and 8 channels of single-frequency signal data are synchronized, which proves the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

Machinery fault diagnosis is crucial for maintenance cost reduction and accident prevention. Vibration signal monitoring is an effective and feasible method for machinery fault diagnosis. However, extraction of the fault-related periodic impulses from weakly monitoring signals is basic but difficul. In this paper, a new weak feature extraction model using Laplacian eigenmaps and parallel sparse filtering (LE-PSF) is presented for mechanical weak fault diagnosis. Specifically, the weak vibration signal is measured from the machinery pedestal. Then, LE is used to extract principal components of the overlapped signal segments, and PSF is employed for weak feature extraction from the principal components. Finally, the extracted features are inputted to softmax regression for fault classification. A simulation study and two experimental cases are employed to testify the effect of the LE-PSF model. Experimental performances show that the LE-PSF can not only achieve accurate fault classification but also is superior to other traditional methods. [ABSTRACT FROM AUTHOR]

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment.

Abstract The periodic‐phase‐diagram similarity method is proposed to identify the frequency of weak harmonic signals. The key technology is to find a set of optimal coefficients for Duffing system, which leads to the periodic motion under the influence of weak signal and strong noise. Introducing the phase diagram similarity, the influences of strong noise on the similarity of periodic phase diagram are discussed. The principle of highest similarity of periodic phase diagram with the same frequency is detected by discussing the persistence of similarity of periodic motion phase diagram under the strong noise and the periodic‐phase‐diagram similarity method is constructed. The weak signals of early fault and strong noise are input into Duffing system to obtain the identified system. The stochastic subharmonic Melnikov method is extended to obtain the conditions of the optimal coefficients for the identified system. Based on the results, the constructed frequency conversion harmonic weak signals are considered to form a datum periodic system. With the change of frequency in the datum periodic system, the phase diagram similarity of the two constructed systems can be calculated. Based on the periodic‐phase‐diagram similarity method, the frequency of weak harmonic signals can be identified by the principle of highest similarity of periodic phase diagram with the same frequency. The results of numerical simulation and the early fault diagnosis results of actual bearings verify the feasibility of the periodic‐phase‐diagram similarity method. The accuracy of the detection effect is 97%, and the minimum signal‐to‐noise ratio is −80.71 dB.

As one of the most important processes in steel wire rope inspection, defect signal processing is of great significance in guaranteeing safety and precision measurement. Aiming at the weak signal detection of steel wire rope with mixed strands and noise, a combined signal processing method based on magnetic flux leakage testing and multi-step filtering techniques are proposed in this paper. The experiments are first introduced and performed on three typical types of steel wire rope with diameters of 28 mm, 32 mm, 45 mm, and different wire broken defects detected under liftoff distances of 13 mm and 20 mm; the acquired signals are then analyzed both in time and frequency domain. According to the weak signal characterizations, the principle of the proposed methods and algorithm are given concretely. Afterwards, comparison of signal processing results between the traditional lowpass filtering, wavelet denoising, median filtering, and the proposed method are presented. Finally, the influence factors of smoothing types and moving average span of the proposed methods are investigated. The processing results of the proposed methods are shown through short-time Fourier transform and signal-to-noise ratio analysis, which not only demonstrates the validity and feasibility of the combined methods with the highest signal to noise ratio of 90.37 dB, but also exhibits a great potential of precision defect detection and practical application in steel wire rope inspection. [ABSTRACT FROM AUTHOR]

In a conventional GPS receiver, the carrier tracking system is the key stage that keeps the receiver locked to the radio navigation parameters (RNPs) of the received signal. The most commonly used approaches to the tracking system are phase lock loop (PLL), frequency lock loop (FLL), and FLL-assisted PLL. The main limitation of the above approaches is that their performance deteriorates when working with weak signals and in harsh environments. In recent years, Kalman filter (KF)-based tracking loop architectures have gained increased attention due to their robust and better performance compared with conventional architectures. In this paper, a novel Gauss–Hermite Kalman filtering-based carrier tracking algorithm is proposed for static and moving receivers with weak GPS signals. The performance of the proposed algorithm is compared with two other approaches: extended Kalman filter (EKF) and unscented Kalman filter (UKF). Simulations were conducted using a software-defined GPS simulator and software device radio (SDR) modules. A comparative analysis of the tracking methods demonstrated that the proposed tracking method shows a better performance and improves the tracking sensitivity and capability under weak signal conditions as compared with EKF- and UKF-based tracking methods. In addition, the results show that the proposed approach improves the Doppler frequency measurement accuracy under dynamic operation conditions. [ABSTRACT FROM AUTHOR]

In the last decade, we witnessed significant technological advancements that had huge impacts across all different aspect of everyday life. Innovative technologies present important topics that need to be overseen from their early beginnings to be able to capture their breakthrough and prepare organizations for needed transitions. This research tries to detect weak signals of emerging technologies based on scientific articles published over the last decade covering several categories. To capture emerging technology trends, topics, we propose a method we refer to as "Carbon-dating Articles with Transformer Series". The name references the Radiocarbon dating (also referred to as carbon dating) method for determining the age of an object containing organic material by using the properties of radiocarbon, a radioactive isotope of carbon. In our method, we take advantage of the state-of-the-art transformer-based architectures in Natural Language Processing (NLP). A time-series of NLP models is trained to recognize new topics (adding fresh "radio-active" isotopes), while slowly forgetting past topics ("isotope half-life"). Using the trained time-series NLP models, scientific article signatures (i.e., text sequences) can be measured and positioned in time. We believe our "Carbon-dating Articles with Transformer Series" method can give valuable insights about when topics emerge and are picked-up by the larger community, timing of (published) articles presenting progressive research versus repeating research, and the bigger picture when combined with other insights like affiliations with nations, organizations, or academics. [ABSTRACT FROM AUTHOR]

Wind profile radar systems require antennas with multiple radiation beams for detecting wind velocity, as well as with a low sidelobe and dual polarization for enhancing the sensitivity for the weak signal reflected from the turbulence. This paper proposes a lens antenna operating at 24 GHz with four reconfigurable beams for wind profile radars. This lens antenna includes 2 × 2 corrugated horn antennas for radiating 24 GHz waves in two polarizations, and the dielectric lens for modulating four radiation beams with a high gain and low sidelobe. Experiments demonstrate that this lens antenna can realize reconfigurable beams with deflections of ±15° in dual polarizations, meanwhile with the gain of 30.58 dBi and the sidelobe of −20 dB. This proposed lens antenna can be applied to mmWave wind profile radars of wind turbines for enhancing wind power efficiency. [ABSTRACT FROM AUTHOR]

The decentralized pre-filter based vector tracking loop (VTL) configuration with data wipe-off (DWO) method of the Global Positioning System (GPS) receiver is proposed for performance enhancement. It is a challenging task to continuously track the satellites' signals in weak signal environment for the GPS receiver. VTL is a very attractive technique as it can provide tracking capability in signal-challenged environments. In the VTL, each channel will not form a loop independently. On the contrary, the signals in the channels of VTL are shared with each other; the navigation processor in turn predicts the code phases. Thus, the receiver can successfully track signals even the signal strength from individual satellite is weak. The tracking loop based on the pre-filter provides more flexible adjustment to specific environments to reduce noise interference. Therefore, even if the signals from some satellites are very weak the receiver can track them from the navigation results based on the other satellites. The navigation data, which contains information necessary to perform navigation computations, are binary phase-shift keying (BPSK) modulated onto the GPS carrier phase with the bit duration of 20 ms (i.e., 50 bits per second) for the GPS L1 C/A signals. The coherent integration interval can be extended for improved tracking performance in signal-challenged environment. However, tracking accuracy is decreased by possible data bit sign reversal. The DWO algorithm can be employed to remove the data bit in I and Q correlation values so as to avoid energy loss due to bit transitions when the integration interval of the correlator is extended over 20 ms under the low carrier-to-noise ratio (C/No) environments. The proposed method has an advantage to provide continuous tracking of signals and obtain improved navigation performance. Performance evaluation of the tracking capability as well as positioning accuracy will be presented. [ABSTRACT FROM AUTHOR]

There are rich geothermal resources in China and they are widely distributed. After years of continuous exploration and production, most of the shallow geothermal resources have been explored and the current exploration mainly focuses on deep ones. Among a great many geophysical exploration methods, seismic survey is the most effective means of geothermal resource exploration and production, but its weak seismic reflection signal, low S/N ratio and poor imaging of thermal reservoirs due to the seismic geological conditions restrict the production and utilization of deep geothermal resources. It considers through the analysis of the geophysical characteristics of the thermal reservoir that the main causes for the weak seismic reflection signals from deep thermal reservoirs are (1) the shielding effect of the strong wave impedance interface between the thermal reservoir and the caprocks; (2) the small reflection coefficient inside the thermal reservoir; and (3) the serious absorption and attenuation of high-temperature fluids such as hydrothermal fluids and steam in the thermal reservoirs by seismic waves. Accordingly, a series of seismic data acquisition technologies are proposed based on the high-precision vibroseis low-frequency shooting, high-sensitivity geophones long-spread receiving and small bin size highfold technologies, and their application in the seismic acquisition of dry-hot rocks (HDR) in Gonghe Basin, Qinghai, shows a very good application effect.

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar.

In this paper, the data wipe-off (DWO) algorithm is incorporated into the vector tracking loop of the Global Positioning System (GPS) receiver for improving signal tracking performance. The navigation data, which contains information that is necessary to perform navigation computations, are binary phase-shift keying (BPSK) modulated onto the GPS carrier phase with the bit duration of 20 ms (i.e., 50 bits per second). To continuously track the satellite’s signal in weak signal environment, the DWO algorithm on the basis of pre-detection method is adopted to detect data bit sign reversal every 20 ms. Tracking accuracy of a weak GPS signal is decreased by possible data bit sign reversal every 20 ms to the predetection integration time (PIT) or integration interval. To achieve better tracking performance in weak signal environment, the coherent integration interval can be extended. However, increase of the integration interval lead to decrease of the tracking accuracy by possible data bit sign reversal every 20 ms to the integration interval. When the integration interval of the correlator is extended over 20 ms in low C/No levels, the navigation DWO algorithm can be employed to avoid energy loss due to bit transitions. The method presented in this paper has an advantage to continuously estimate the navigation data bit and achieve improved tracking performance. Evaluation of the tracking performance based on the various integration intervals for the vector tracking loop of a GPS receiver will be presented. [ABSTRACT FROM AUTHOR]

Space‐based automatic dependent surveillance‐broadcast (ADS‐B) is an important emerging augmentation of existing ground‐based ADS‐B systems. In this paper, the problem of space‐based ultra‐long‐range reception processing of ADS‐B signals is described. We first introduce a header detection method for accurately determining the pulse position of a weak ADS‐B signal. We designed a signal encoding method, shaping method, and fitness function. We then employed a genetic algorithm to perform high‐precision frequency and phase estimations of the detected weak signal. The advantage of this algorithm is that it can simultaneously estimate the frequency and phase, meaning a direct coherent demodulation can be implemented. To address the computational complexity of the genetic algorithm, we improved the ratio algorithm for frequency estimation and raised the accuracy beyond that of the original ratio algorithm with only a slight increase in the computational complexity using relatively few sampling points.

Due to the complexity of the interference operation environment of wire rope, the detection signals are usually weak and coupled in time-frequency domain, which makes the defect difficult to recognize, while the signal characterizations in phase space are also needed to be studied. Combining the nonlinear dynamic feature identification theories, phase space characteristics and chaotic features of wire rope defect detection signals are mainly investigated in this paper. First, principles of phase space reconstruction method for wire rope detection signals are presented by the chaotic dynamic indexes calculation of embedded dimension and delay time. Second, the change trends of the correlation dimension, approximate entropy and Lyapunov index of different phase space reconstructed wire rope defect detection signals are studied through the nonlinear simulation and analysis. Finally, a phase space reconstruction algorithm based on improved SVD is proposed, and the new algorithm is also compared with traditional signal processing methods. These results obtained by 6 groups of experiments were also evaluated and compared by the parameters of signal-to-noise ratio (SNR) and phase space trajectory chart, which manifests that the improved algorithm not only can increase the weak detection signal SNR to about 2.3dB of wire rope effectively, but also demonstrate the feasibility of the proposed methods in application. [ABSTRACT FROM AUTHOR]

Rolling bearing health status monitoring is essential for identifying early failures and avoiding un-planned downtime in industrial systems. To overcome the problems of existing entropy methods with multiple faults that are easily confounded at different scales, a weak signal feature extraction method based on refined composite multi-scale weighted entropy is proposed in this paper. The time–frequency domain features are constituted into a multi-dimensional original fault feature set, and the feature sensitivity is evaluated in terms of four feature evaluation criteria, in order to filter out a sensitive feature subset. Three types of refined composite multi-scale entropy are combined with sensitive feature parameters, in a weighted manner, through the use of the Hadamard product operation. The effects of different combinations of feature parameters on the refined composite multi-scale entropy are analyzed through experimental validation. According to the analysis of the experimental data from two test stations, the fault recognition rate reached 100% and 92.22%, respectively, based on the RCMWE method, starting from the first features. The results indicate that the proposed method can identify bearing fault types under different damage states at any scale, with the fault recognition rate being more stable than that of other methods. The proposed method can effectively distinguish rolling bearing health and fault states, providing higher classification accuracy for rolling bearing fault types and fault damage degrees. This puts forward a new idea for rolling bearing health state assessment, which has high engineering application value.

This paper is motivated from an HIV‐1 drug resistance study where we encounter three analytical challenges: to analyze data with an informative subsample, to take into account the weak signals, and to detect important signals and also conduct statistical inference. We start with an initial estimation method, which adopts a penalized pairwise conditional likelihood approach for variable selection. This initial estimator incorporates the informative subsample issue. To accounting for the effect of weak signals, we use a key idea of partial ridge regression. We also propose a one‐step estimation method for each of the signal coefficients and then construct confidence intervals accordingly. We apply the proposed method to the Stanford HIV‐1 drug resistance study and compare the results with existing approaches. We also conduct comprehensive simulation studies to demonstrate the superior performance of our proposed method. [ABSTRACT FROM AUTHOR]

As one of the most important processes in steel wire rope inspection, defect signal processing is of great significance in guaranteeing safety and precision measurement. Aiming at the weak signal detection of steel wire rope with mixed strands and noise, a combined signal processing method based on magnetic flux leakage testing and multi-step filtering techniques are proposed in this paper. The experiments are first introduced and performed on three typical types of steel wire rope with diameters of 28 mm, 32 mm, 45 mm, and different wire broken defects detected under liftoff distances of 13 mm and 20 mm; the acquired signals are then analyzed both in time and frequency domain. According to the weak signal characterizations, the principle of the proposed methods and algorithm are given concretely. Afterwards, comparison of signal processing results between the traditional lowpass filtering, wavelet denoising, median filtering, and the proposed method are presented. Finally, the influence factors of smoothing types and moving average span of the proposed methods are investigated. The processing results of the proposed methods are shown through short-time Fourier transform and signal-to-noise ratio analysis, which not only demonstrates the validity and feasibility of the combined methods with the highest signal to noise ratio of 90.37 dB, but also exhibits a great potential of precision defect detection and practical application in steel wire rope inspection.

Stochastic resonance is the use of nonlinear systems to synchronize an original signal with noise. This method is commonly used to extract useful signals by reducing noise and has been widely used for mechanical weak fault diagnosis. This paper analyzes the characteristics of a periodic non-sinusoidal potential function, considers the shape of the model, and introduces a time-delay. The steady-state probability density function, effective potential function, and signal-to-noise ratio are then analyzed. As a result, a signal detection method for periodic nonsinusoidal time-delay stochastic resonance (PNTSR) is proposed. Experimental and engineering data are used to explain the PNTSR through the simulation. It is found that the PNTSR method has better fault detection results when compared to the classic bi-stable stochastic resonance method. [ABSTRACT FROM AUTHOR]

Space‐based automatic dependent surveillance‐broadcast (ADS‐B) is an important emerging augmentation of existing ground‐based ADS‐B systems. In this paper, the problem of space‐based ultra‐long‐range reception processing of ADS‐B signals is described. We first introduce a header detection method for accurately determining the pulse position of a weak ADS‐B signal. We designed a signal encoding method, shaping method, and fitness function. We then employed a genetic algorithm to perform high‐precision frequency and phase estimations of the detected weak signal. The advantage of this algorithm is that it can simultaneously estimate the frequency and phase, meaning a direct coherent demodulation can be implemented. To address the computational complexity of the genetic algorithm, we improved the ratio algorithm for frequency estimation and raised the accuracy beyond that of the original ratio algorithm with only a slight increase in the computational complexity using relatively few sampling points. [ABSTRACT FROM AUTHOR]

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the stable-state matching underdamped stochastic resonance (SMUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

Wind profile radar systems require antennas with multiple radiation beams for detecting wind velocity, as well as with a low sidelobe and dual polarization for enhancing the sensitivity for the weak signal reflected from the turbulence. This paper proposes a lens antenna operating at 24 GHz with four reconfigurable beams for wind profile radars. This lens antenna includes 2 × 2 corrugated horn antennas for radiating 24 GHz waves in two polarizations, and the dielectric lens for modulating four radiation beams with a high gain and low sidelobe. Experiments demonstrate that this lens antenna can realize reconfigurable beams with deflections of ±15° in dual polarizations, meanwhile with the gain of 30.58 dBi and the sidelobe of −20 dB. This proposed lens antenna can be applied to mmWave wind profile radars of wind turbines for enhancing wind power efficiency.

Due to the Doppler Effect caused by the high line-of-sight velocity of satellites and in some cases receivers, the frequency of the Global Positioning System (GPS) satellites signals at the receivers’ level will be transformed into new ones. In the worst case, it can be deviate up to ±10 kHz. The role of the acquisition block in SDR GPS receivers is to detect visible satellites and coarse values of carrier frequency and code phase of each visible one. Several acquisition algorithms are existed now; the first algorithms developed are based on the time domain search. The new generation of GPS receivers is based on the frequency domain calculation. In this context, our developed algorithm is a new approach based on the Fast Fourier Transform (FFT) calculation. Our proposed solution has a fast execution time that can achieve 40% compared with elder ones in addition to its simplicity that make it easier to implement in a FPGA card.

Global Navigation Satellite Systems (GNSS) are used in various applications such as mapping, automotive, aviation, military and so on. GNSS receivers cannot track satellites signal in degraded signal environment. In recent years, vector tracking is one of the methods that have been used to solve the weak signal tracking problem. In the vector tracking, the extended Kalman filter (EKF) is used to estimate the states of the system. The EKF has no ability to track the satellite signal when the signal is under unfavorable conditions. In this paper, a method based on the extended Kalman filter is proposed, in which all the adjustable parameters are adapted in a fuzzy method. The results show that this proposed method can track weak signals better than the previous methods. With this method, it is possible to track poor signals for longer times than previous methods. [ABSTRACT FROM AUTHOR]

In order to be able to control the growth rate of industrial polycrystalline silicon rods well and reduce energy consumption, it is necessary to accurately locate the boundaries of the silicon rods and measure the diameter of the silicon rod during growth. In this paper, a weak edge signal detection method based on small area is proposed for the problem of weak boundary signal in the late growth stage of polycrystalline silicon rods. The method increases the gradient of the edge by projecting the boundary signal in the column direction. In addition, the method further enhances the gradient information of the weak boundary signal by improving the classical difference operator. In the experimental part, the improved difference operator increases the weak difference signal value from 304 to 686. Finally, the effectiveness of the algorithm is proved by two groups of experimental results. [ABSTRACT FROM AUTHOR]

The acquisition performance of weak global navigation satellite system signals, especially those with secondary codes, is susceptible to sign transitions. To better understand and overcome the negative impact of sign transitions, this paper derived a new receiver auto-correlation function. The straightforward formula shows that sign transitions can reduce the accuracy of the Doppler estimate. To meet the initialization requirements of the tracking block, a two-stage high-sensitivity acquisition frame is proposed based on sign combinations search using matrix notation. The first stage adopts the conventional non-coherent combination (NC) algorithm to obtain the coarse code phase. And the second stage achieves high-resolution frequency estimation through fast Fourier transform (FFT). Besides, a novel grouping FFT algorithm is proposed to reduce the complexity of the second stage. The Beidou navigation satellite system (BDS) B1I signal is used as an example to verify the performances. Compared with conventional NC algorithm, the statistical performance of the new algorithm, in terms of the detection probabilities and frequency errors, can be greatly improved. To be specific, the proposed algorithm can actualize the 90% detection possibility of BDS B1I signal at a carrier-to-noise ratio (C/N0) of 23.9 dB-Hz within 200 ms with a Doppler accuracy of about 31 Hz. And the computing load of the second stage is reduced by about 11.6% compared with the conventional FFT approach. Actual data test further verifies the superiority of the new method. Inevitably, the total processing time increases by about 30%.

With the emergence of new technologies and policies to transition to clean energy, the household energy consumption sector is also changing. In response to policy, environmental, and technical changes, researchers need to find out what significant issues are related to household energy consumption, and comprehensively analyze which issues are likely to attract attention in the future to contribute to research in the household sector. Based on the abstracts of academic papers published between 2011 and 2020, this study uses probabilistic topic modeling to increase understanding of academic issues in the household energy consumption sector and statistically reviews changes in issues over time. As a result of the analysis, topics related to digitalization and renewable energy, such as microgrid system, smart home, residential solar power generation systems, and non-intrusive load monitoring (NILM), belonging to Strong signals, are being actively studied. Weak Signals, which can attract attention in the future, are included in discussions on coal energy consumption, air pollutant emissions, energy poverty, and energy performance evaluation. The analysis results show that carbon neutrality, such as decarbonization and fossil energy consumption reduction, is expanding to research in the household energy consumption sector.

Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.

Global satellite navigation systems (GNSS) are nowadays used in many applications. GNSS receivers experience limitations in receiving weak signals in a degraded environment. Hence, tracking weak GNSS signals is a topic of interest to researchers in this field. Different methods have been proposed to address this issue, each of which has advantages and disadvantages. In this paper, a method based on the vector tracking method is proposed for weak signal tracking. This method has been developed based on a strong Kalman filter instead of the extended Kalman filter used in conventional vector tracking methods. In order to adjust important parameters of this filter, the fuzzy method is used. The results of tests performed with both simulated data and real data demonstrate that the proposed method performs better than previous ones in weak signal tracking. [ABSTRACT FROM AUTHOR]

The presence of code Doppler and navigation bit sign transitions means that the acquisition of global positioning system (GPS) signals is difficult in weak signal environments where the output signal-to-noise ratio (SNR) is significantly reduced. Post-correlation techniques are typically utilised to solve these problems. Despite the advantages of these techniques, the post-correlation techniques suffer from problems caused by the code Doppler and the navigation bit sign transitions. We present an improved semi-bit differential acquisition method which can improve the code Doppler and the bit sign transition issues in the post-correlation techniques. In order to overcome the phenomenon of navigation bit sign transitions, the proposed method utilises the properties of the navigation bit. Since each navigation bit takes as long as 20 ms, there would be 10 ms correlations duration integration time between the received signal and the local coarse/acquisition (C/A) code in which the navigation bit sign transitions will not occur. Consequently, this problem can be cancelled by performing 10 ms correlations in even and odd units separately. Compensation of the code Doppler is also accomplished by shifting the code phase of the correlation results. To validate the performance of our suggested method, simulations are performed based on three data sets. The results show that the quantity of required input SNR to detect at least four satellites in the proposed method is − 48·3 dB, compared with − 20 dB and − 9 dB, respectively, in traditional differential and non-coherent methods. [ABSTRACT FROM AUTHOR]

In order to solve the problem of quantitative detection of weak signals, the detection statistics based on the Poincare section is proposed. An effective quantitative detection method based on the Duffing oscillator is designed, and the amplitude parameters can be extracted with this method. By analyzing the Poincare section characteristics of the Duffing oscillator, it is found that different states of the system can be distinguished by the bifurcation characteristics of the system. Then, the variance statistics of the Poincare section distribution points are determined, the detection statistics and detection determination intervals are formulated to realize the quantitative detection. The simulation results show that the noise robustness of the quantitative detection method is better than that of the traditional time-domain feature detection method. Furthermore, a cyclic detection system is designed to extract the amplitude parameters and detect the unknown frequency signals. The results provide a quantitative detection approach and reference for weak signals detection based on the Duffing oscillator. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Changes in the behavior of dynamic systems are detected based on changes in the monitored quantities or their characteristics. This detection usually takes place by monitoring the time evolution of a variable and detecting the change at the time when a predetermined threshold is exceeded. This threshold is determined on the basis of the detection scheme requirements, in particular, the probability of false alarms and the detection rate for the actual change. In some cases, however, a change does not come suddenly, but certain "hints" in the system behavior can be observed that may indicate a future change. For example, an increasing frequency of outliers can result in a sudden permanent change in the signal. The occurrence of some "unusual" frequencies often indicates an imminent change. For example, an increasing correlation value indicates an undesired process status. Detection of these "subliminal" hints can often improve the characteristics of the detection scheme, especially the detection rate for the actual change. In this paper, we will deal with the detection of weak signals in statistical process monitoring using a control chart with adaptive control limits. [ABSTRACT FROM AUTHOR]